{ "generated_at": "2026-10-15T08:22:18.753Z", "items": [ { "entities": [ "Meta AI" ], "id": "6ae7e8eb5b33ba96", "kind": "press", "meta": { "age_hours_at_score": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T08:21:14.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T08:21:14.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T08:21:14.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Meta AI" ], "models": [] }, "first_seen_at": "2026-10-15T08:22:18.753Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "geneonline.com", "pillar_reason": "regulatory/legal keyword", "publisher": "www.geneonline.com", "publisher_url": "https://www.geneonline.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 20, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "governance", "published_at": "2026-10-15T08:21:14.000Z", "score": 36.8, "source": "gnews-ai", "summary": "www.geneonline.com, via Google News.", "title": "A Human Rights Test for Medical AI: Safe, Fair, and Accountable — Regulators Draw the Line on Patient Safety and Clinical Control", "url": "https://news.google.com/rss/articles/CBMi5wFBVV95cUxOWjJfeFVJSktuZzBMR0hYWjJXQnJ3VVc4ZTRQZmlHa1pIbU5xdTVucW52RW12QmthNDNuMXBleEFBT2l2RVBpS0ZDNllUT2JkcHQ3Z3FoUElfMC1wYW9FREYtMzczX2dUaFo2dVBvRVZtTVU2M0hxbVVKSWJLOF9scURncVljcmRXeTdLSUZ4Ylg3WER6QWNvWDFPZGl5NmFaUXQyZ2pVZzhsRWhXNkE2cTBKVzFPejdUVG5WVEVSSEJmeXh0NE1zQjNRNklCaEstbmNzTjBGNEZjMkdvb3NwdWV4Qm5NdXc?oc=5" }, { "entities": [], "id": "fec13b4d97320a11", "kind": "press", "meta": { "age_hours_at_score": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T08:21:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T08:21:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T08:21:00.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T08:04:19.125Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "apnews.com", "pillar_reason": "source default", "publisher": "AP News", "publisher_url": "https://apnews.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 20, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T08:21:00.000Z", "score": 38.8, "source": "wires-ai", "summary": "AP News, via Google News.", "title": "Artificial intelligence", "url": "https://news.google.com/rss/articles/CBMiWkFVX3lxTFA4Qi1oeTk1Z21hMnphRDg4Q3FlR1NuQmd0RUpidzBDU3A4YVRlVlZoMnZDbFVNSXA5bWVtUU55UkFqaXBVR1JqY29Sd0Q4NUZOOWtSNFJEWmVZUQ?oc=5" }, { "entities": [ "Tencent" ], "id": "765c5bc0bc58a039", "kind": "press", "meta": { "age_hours_at_score": 0.1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T08:19:15.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T08:19:15.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T08:19:15.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Tencent" ], "models": [] }, "first_seen_at": "2026-10-15T08:20:18.881Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "thenextweb.com", "pillar_reason": "source default", "publisher": "The Next Web", "publisher_url": "https://thenextweb.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 20, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T08:19:15.000Z", "score": 36.8, "source": "gnews-ai", "summary": "The Next Web, via Google News.", "title": "Researchers trace AI agents that probed Alibaba’s Amap to Tencent", "url": "https://news.google.com/rss/articles/CBMilwFBVV95cUxNeEVEYVM3MkoxSlQwYy1IaE1uVXR0TF9Qd1c2YzhwSlR6WkYzb1ZGanQ4S2tlVVIzN2VpbUM5YjE5OTloa0Y5Z1dMSkloM3liblFWSDNMMWxWV0ctd2h1NVZONU8xNl8yS1lYOGd5NUhxVXdCVzM0TVBTeWhmWEdTejNBbnZvVHBnc1NuUm1pcVpFSEo4cm5j?oc=5" }, { "entities": [], "id": "6764ac37381b5ddd", "kind": "press", "meta": { "age_hours_at_score": 0.1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T08:17:46.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-15T08:17:46.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T08:17:46.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-15T08:20:18.881Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 6, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 20, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "markets", "published_at": "2026-10-15T08:17:46.000Z", "score": 35, "source": "bloomberg-tech", "summary": "Henry Allen, macro strategist at Deutsche Bank Research, discusses market moves as a selloff in the artificial intelligence trade gathered speed. \"If it was a concern, we'd be seeing broader stock market see bigger falls,\" he tells Bloomberg Television. \"The fact that we haven't seen that spread to Europe, for…", "title": "Deutsche Bank Sees 'Promising' Signs in Limited Selloff", "url": "https://bloomberg.com/news/videos/2026-10-15/deutsche-bank-sees-promising-signs-in-limited-selloff-video" }, { "entities": [ "OpenAI" ], "id": "9174e9582faab915", "kind": "press", "meta": { "age_hours_at_score": 0.1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T08:15:00.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T08:15:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T08:15:00.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "first_seen_at": "2026-10-15T08:20:18.881Z", "incident": { "band": "C", "excluded": null, "severity": 0.3, "strong_signal": false, "terms": [ { "field": "title", "matched": "lawsuit", "term": "lawsuit", "tier": "c", "weight": 0.3 } ] }, "outlet_domain": "mashable.com", "pillar_reason": "regulatory/legal keyword", "publisher": "Mashable", "publisher_url": "https://mashable.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 20, "severity": 4.5, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "governance", "published_at": "2026-10-15T08:15:00.000Z", "score": 41.3, "source": "gnews-ai", "summary": "Mashable, via Google News.", "title": "Lawsuit alleges OpenAI 'stole' children's user data", "url": "https://news.google.com/rss/articles/CBMiZkFVX3lxTE5NRFpvc2tkZzhkUl9ZSmwxZlVsdERqSWQ4cUI0UjV6bU5FR2IzTWZCdXNVeDFNYkE2Yy1VWEx6aEdpMndRTWVtWGZLLVNnNmlvMXpmX3cyWDlhSG9rZk0tYmhzbjdwZw?oc=5" }, { "entities": [ "ByteDance", "Tencent" ], "id": "06ceabcb37755150", "kind": "press", "meta": { "age_hours_at_score": 0.2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T08:12:24.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T08:12:24.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T08:12:24.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "ByteDance", "Tencent" ], "models": [] }, "first_seen_at": "2026-10-15T08:14:18.814Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "36kr.com", "pillar_reason": "source default", "publisher": "36 Kr", "publisher_url": "https://eu.36kr.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 19.9, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T08:12:24.000Z", "score": 39.7, "source": "gnews-ai", "summary": "36 Kr, via Google News.", "title": "ByteDance, Tencent, Alibaba: The Same Strategic Move Reshaping China’s Tech Landscape", "url": "https://news.google.com/rss/articles/CBMiU0FVX3lxTE1aeVVPWW5idW83c2RtV1pnOEZtWTF3d2RzSnh4d2d2ZE9FYi1DRFFjdldRVG85Q2FIUUt5bmhsMHBwSFZCbWFVZFhFdHJuUHNXZF9z?oc=5" }, { "entities": [], "id": "89a26503f575a041", "kind": "press", "meta": { "age_hours_at_score": 0.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T08:05:53.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-15T08:05:53.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T08:05:53.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-15T08:08:18.880Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 6, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.9, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "markets", "published_at": "2026-10-15T08:05:53.000Z", "score": 34.9, "source": "bloomberg-tech", "summary": "At the 2026 Bloomberg New Economy Forum, global leaders across tech, energy, finance, and government frame artificial intelligence as a generational force reshaping every sector. While recognizing its massive infrastructure and power demands, the speakers collectively stress the need for top-down executive leadership,…", "title": "Global Leaders on AI Growth and Governance", "url": "https://bloomberg.com/news/videos/2026-10-15/global-leaders-on-ai-growth-and-governance-video" }, { "entities": [ "Apple" ], "id": "34155e859febbc43", "kind": "press", "meta": { "age_hours_at_score": 0.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T08:03:00.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T08:03:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T08:03:00.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Apple" ], "models": [] }, "first_seen_at": "2026-10-15T08:12:19.422Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "chosun.com", "pillar_reason": "source default", "publisher": "Chosunbiz", "publisher_url": "https://biz.chosun.com", "score_components": { "corroboration": 0, "coverage": 10, "engagement": 0, "entities": 3, "recency": 19.9, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": { "id": "2971dc736032", "is_lead": true, "lead": "34155e859febbc43", "linked_by": [ { "rule": "shared-words", "with": "bf70ae58595759c8", "words": [ "appl", "china", "siri" ] } ], "members": [ "34155e859febbc43", "441d2525c07d0223", "bf70ae58595759c8" ], "severity": 0, "source_count": 3, "sources": [ "bluesky-ai", "gnews-ai", "techmeme" ] } }, "pillar": "attention", "published_at": "2026-10-15T08:03:00.000Z", "score": 46.7, "source": "gnews-ai", "summary": "Chosunbiz, via Google News.", "title": "Apple readies limited Apple Intelligence launch in China without new Siri - CHOSUNBIZ", "url": "https://news.google.com/rss/articles/CBMiekFVX3lxTE9sUDZhbk9mMWFrV0hCWGoyTkI0MlRNWE5fOUdDSmZiNGZvRGxPd25CSjQ2ZWdCSEJsMUpDN3FuYUIwa2FrYURmenh6a3lrWDRzREdFWnlfa094dTZVemMyQnZPSGI1OEs2VGsxd3hQOHRoTG5TX0h5aVln0gGOAUFVX3lxTE5PTlVncUNFOW9jcFdkbk11MkFPZ1pOamIxeUxCUTBITjlnUWtlOThOdk8wSkpzdVpIX0J1YmM0OGlkTGRkbFhNd1lQWS04WG9ieHZsY05aMUMyS1Z6NjdKWFQ0S1dWRGN1aHdpanNyOFdLT1o5bFBiYnhCQzZHajhpcDc5LV9ROG9lUlYxNVE?oc=5" }, { "entities": [ "Microsoft" ], "id": "d1c3a3c5ac86a6f8", "kind": "press", "meta": { "age_hours_at_score": 0.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T08:01:34.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T08:01:34.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T08:01:34.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Microsoft" ], "models": [] }, "first_seen_at": "2026-10-15T08:04:19.125Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "pa.media", "pillar_reason": "source default", "publisher": "Press Release Hub", "publisher_url": "https://pressreleasehub.pa.media", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.9, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T08:01:34.000Z", "score": 36.7, "source": "gnews-ai", "summary": "Press Release Hub, via Google News.", "title": "Hornetsecurity by Proofpoint introduces new capabilities to support secure AI adoption for organisations using Microsoft 365", "url": "https://news.google.com/rss/articles/CBMihwJBVV95cUxPN091ZlV6UWhGUktrUnZHZkxrLXZqanZfV0tJTDFFcGNtVmdBczVZcVVZOUxRaXY2YnBmTVo4dkdqVWQ0ZmdveE84Y2VlZU5PeE1HWV9JNGJKa0Jtdjg4ZHVqQ0Y5OVU4YWZFcXQ4UXhvWnhJc0RVakpZUXNxU2pnZ20yOS0zNTJSTF9TMlU5UDA3anRWbFpBRGlhMndDVzdCZGw4LUxTQkx4Wmtra0MwV0FiTlVPYy1IcnV5RWV2MEx4eTdkNFB0YlBueEVtMUNaYVNpS01fNXFKbjFUdjllQTdzTXdQUl9IMUZPZWtfX1FtRGxFZ0xyWUE2Tkl3ZHFzYm1YQ1RfZw?oc=5" }, { "entities": [ "OpenAI" ], "id": "dd3d921ff104f0a5", "kind": "press", "meta": { "age_hours_at_score": 0.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T08:00:00.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T08:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T08:00:00.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "first_seen_at": "2026-10-15T08:04:19.125Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "channellife.co.uk", "pillar_reason": "source default", "publisher": "ChannelLife UK", "publisher_url": "https://channellife.co.uk", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.9, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T08:00:00.000Z", "score": 36.7, "source": "gnews-ai", "summary": "ChannelLife UK, via Google News.", "title": "SpecterOps & OpenAI launch AI security training course", "url": "https://news.google.com/rss/articles/CBMijAFBVV95cUxPcFMxMWR4MEkzamhka2RUQWN6cFZJaGJGRXpmb05fVmFUcFVKZXB3c0JLbGhPYmRmU3l4MnJYMHdZbzFPOF9tM2hhWWtTS2U2bmhyTE9UbV93aVlqZm41b191TzE3X2tDSW52WkdwV1d4eFNZd1F2M2U1OUZYYzVzWk40bV8zQVJoa1dUZg?oc=5" }, { "entities": [ "Anthropic" ], "id": "98842f779147e049", "kind": "press", "meta": { "age_hours_at_score": 0.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:57:51.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:57:51.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:57:51.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "first_seen_at": "2026-10-15T07:58:20.287Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "investing.com", "pillar_reason": "hardware/capital keyword", "publisher": "Investing.com", "publisher_url": "https://www.investing.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.8, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": { "id": "f27ed1f158bf", "is_lead": false, "lead": "2bb464d7faf82e22", "linked_by": [ { "rule": "shared-words", "with": "2bb464d7faf82e22", "words": [ "anthropic", "jump", "revenu" ] }, { "rule": "shared-words", "with": "45e05b50f5cecac5", "words": [ "anthropic", "jump", "revenu" ] }, { "rule": "shared-words", "with": "4c31c7cec80ac7f6", "words": [ "anthropic", "jump", "revenu" ] }, { "rule": "shared-words", "with": "6c340f3efbdbf860", "words": [ "anthropic", "market", "revenu" ] }, { "rule": "shared-words", "with": "6f76a14503cf2093", "words": [ "anthropic", "jump", "revenu" ] }, { "rule": "shared-words", "with": "f47237ff75e651b0", "words": [ "anthropic", "jump", "revenu" ] } ], "members": [ "08b2cdffdbe6bf48", "2bb464d7faf82e22", "41d835035e2a01e4", "45e05b50f5cecac5", "4c31c7cec80ac7f6", "6c340f3efbdbf860", "6f76a14503cf2093", "7604567454cfc303", "98842f779147e049", "dcf332e0d23bb416", "e36d56a8a8a62f0d", "f47237ff75e651b0" ], "severity": 0, "source_count": 9, "sources": [ "bbc-tech", "bloomberg-tech", "bluesky-ai", "ft-ai", "gnews-ai", "hn-fresh-ai", "nyt-tech", "techmeme", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-15T07:57:51.000Z", "score": 36.6, "source": "gnews-ai", "summary": "Investing.com, via Google News.", "title": "AI jitters in focus; Anthropic’s reported revenue jump - what’s moving markets", "url": "https://news.google.com/rss/articles/CBMixAFBVV95cUxNalY1dHRhd3ljZ3c3V0hvWE9OQWRZeXJnclpmV1RkY1NobkhLUk1SQUhOaW1PT3M1R1hGcW14anE0OTBZNVY3SkxzX3ROdU1BdG5mM25pMjBfQ3pDM3Bjb3l4ZlRqRE42VzZ3cHQ0eEFXNTV5WWpJVlFFWnFLekFmZnpJZVMxZDdDTjMzUVVJa1lNRFhVZEhabGVNdkU5T3ptcTV6Z1pmVUFlUFVyLXRWUlNTM1ZuSjRTVV9hYW1PVlN2RzR1?oc=5" }, { "entities": [ "Gemini" ], "id": "61173c2df7656a9d", "kind": "press", "meta": { "age_hours_at_score": 0.5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:54:42.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:54:42.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:54:42.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Gemini" ] }, "first_seen_at": "2026-10-15T08:06:18.928Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "economictimes.com", "pillar_reason": "source default", "publisher": "The Economic Times", "publisher_url": "https://m.economictimes.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.8, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:54:42.000Z", "score": 36.6, "source": "gnews-ai", "summary": "The Economic Times, via Google News.", "title": "Inside Google's Gemini safety strategy: Protecting children, verifying age and tackling AI misinformation", "url": "https://news.google.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?oc=5" }, { "entities": [ "OpenAI" ], "id": "4cf2777c6f86ba6b", "kind": "press", "meta": { "age_hours_at_score": 0.5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:54:00.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:54:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:54:00.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "first_seen_at": "2026-10-15T07:58:20.287Z", "incident": { "band": "C", "excluded": null, "severity": 0.3, "strong_signal": false, "terms": [ { "field": "title", "matched": "incident", "term": "incident", "tier": "c", "weight": 0.3 } ] }, "outlet_domain": "straitstimes.com", "pillar_reason": "source default", "publisher": "The Straits Times", "publisher_url": "https://www.straitstimes.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.8, "severity": 4.5, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:54:00.000Z", "score": 41.1, "source": "gnews-ai", "summary": "The Straits Times, via Google News.", "title": "OpenAI backs Singapore’s call for AI incident transparency", "url": "https://news.google.com/rss/articles/CBMilgFBVV95cUxQcmZEUkgyWWhES0N0Q01RTGhtTk9iS2tqZUs2SnBvaloxUmprSlhGYlNEUUdRcGtGMXM4QkNzUmFNa3U0ZkVKV2tYOFgzUEU5a3VYRTVMRzBDVUxnRm82emJJWjN2MUJETGtNVTFZcmY1cE13RWtoSDNwVXZXN0ltVFRpMjVUODItUGNrbzkyV1lyUm4zM1E?oc=5" }, { "entities": [], "id": "6039bc6b80f63ac9", "kind": "press", "meta": { "age_hours_at_score": 0.5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:52:51.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T07:52:51.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:52:51.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:58:20.287Z", "incident": { "band": "B", "excluded": null, "severity": 0.6, "strong_signal": false, "terms": [ { "field": "title", "matched": "attack", "term": "attack", "tier": "b", "weight": 0.6 } ] }, "outlet_domain": "afp.com", "pillar_reason": "source default", "publisher": "AFP Fact Check", "publisher_url": "https://factcheck.afp.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.8, "severity": 9, "source_weight": 18.8 }, "source_weight": 0.75, "story": { "id": "2c71fcc0c6b3", "is_lead": true, "lead": "6039bc6b80f63ac9", "linked_by": [ { "rule": "shared-words", "with": "5926326b057d7dc9", "words": [ "generat", "houthi", "saudi", "video" ] } ], "members": [ "5926326b057d7dc9", "6039bc6b80f63ac9" ], "severity": 0.6, "source_count": 1, "sources": [ "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-15T07:52:51.000Z", "score": 47.6, "source": "wires-ai", "summary": "AFP Fact Check, via Google News.", "title": "Video of Saudi passenger plane exploding after Houthi attack is AI-generated", "url": "https://news.google.com/rss/articles/CBMiWEFVX3lxTFBDUVZfQVRxVlY4elNKdWc3b0czbThIa2k3bC01bDVuXy1nUnNmai1OdHdtaDA2dUxienRPODF5TEdJc0ZtTmExeno3eTVOWVBDdlFibGFZMEs?oc=5" }, { "entities": [ "NVIDIA" ], "id": "fc1f6cec1f19a71d", "kind": "press", "meta": { "age_hours_at_score": 0.5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:50:28.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:50:28.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:50:28.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "NVIDIA" ], "models": [] }, "first_seen_at": "2026-10-15T07:58:20.287Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "theglobeandmail.com", "pillar_reason": "source default", "publisher": "The Globe and Mail", "publisher_url": "https://www.theglobeandmail.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.8, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:50:28.000Z", "score": 36.6, "source": "gnews-ai", "summary": "The Globe and Mail, via Google News.", "title": "Can Apple Overtake Nvidia as the World's Largest Company in 2027?", "url": "https://news.google.com/rss/articles/CBMi5gFBVV95cUxQRnZjSHA5RzZQUUdUeGt2OFJLYUx2RzN2NldwMnBjaF9HY1EtU1ViN2NUX1JmTGRZSzhTd21ReG1pSC1SQVlzRF9sckFTaHd4SGI2MGdfQXlZRHZFS29JbDVqY3Uyb2ZXckkzX2ZUOXJ2WFNXN0VmRGZtTExnRG9wTmQzdTVLZ2VGb3RrdEFmN3VKdGpTazMwOUVVQmRqLWhsWmhLUkJIT3pfZTByaVhpR0ZRR0RGaFdUYnk5TDk5RGJKUF90SDdGOUw0WmdWUWd3SXFJdlA0dGk3emNGMU1lOVdWRmZqdw?oc=5" }, { "entities": [], "id": "df2e53adc4be75d7", "kind": "press", "meta": { "age_hours_at_score": 0.6, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:48:15.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T07:48:15.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:48:15.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T08:13:19.051Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "wsj.com", "pillar_reason": "source default", "publisher": "WSJ", "publisher_url": "https://www.wsj.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.8, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:48:15.000Z", "score": 38.6, "source": "wires-ai", "summary": "WSJ, via Google News.", "title": "The U.S. Army’s Desert Tech Test Shows Long Road to AI Warfare", "url": "https://news.google.com/rss/articles/CBMimgFBVV95cUxQZV9EMzVNLU80MGdqQmlEWDUxM1g2RWdQaXBCN0FyZlR3QjU4YmE3VGpjWUgtWkJhVUJocXZRN0swdkNja0pyYUNsRnlKTW11YnU0M1lqZW1STkMxbTY1dUhLQkU3X2pjZUdXN21uXzFTN3dPWUNsaDN3Q1JTbG9HblBxRUN0aFJNLWtoMlE1d3F1UkpEcVE4ZjNn?oc=5" }, { "entities": [], "id": "6970bffe6c7752a1", "kind": "press", "meta": { "age_hours_at_score": 0.6, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:47:31.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:47:31.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:47:31.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:58:20.287Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "blockhead.co", "pillar_reason": "source default", "publisher": "Blockhead", "publisher_url": "https://www.blockhead.co", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.8, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:47:31.000Z", "score": 33.6, "source": "gnews-ai", "summary": "Blockhead, via Google News.", "title": "AI Agents Have Made 138 Million Payments Over x402, Most of Them Under 10 Cents", "url": "https://news.google.com/rss/articles/CBMitgFBVV95cUxPOS12dW1nejlSUm9wTWhlLWlXS1BhOVhrd1BrUkRTUVY3ODJHWkZSb3Bqb0JMMEV4Q2t1dG02T3hDTGR0R0l1Y3ZxVHkzR3J2aXVuSGo2VFZKLUtQYkgyZ0pXX2w3cEJsOGIwUU5qRnVZZ2lDb2pRRlozd1dmLXdOTUJsOVlXM1VMTmFSeUhCcGdYSVdPcjJnZlBHSDN4V05BM0pKNkFHWDZ4RjZuYmZFTmlzZ0pjdw?oc=5" }, { "entities": [], "id": "2fdf5bdc195498ad", "kind": "press", "meta": { "age_hours_at_score": 0.6, "corroboration": { "also": [ { "dedup_key": null, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 1 }, "kind": "forum", "outlet": "reuters.com", "published_at": "2026-10-15T08:00:27.000Z", "source": "bluesky-ai", "title": "Chinese startup DexForce trains humanoid robots using virtual data to slash costs", "url": "https://bsky.app/profile/reuters.com/post/3mxvlshskwe2a", "weight": 0.6 } ], "count": 2, "first_seen_published_at": "2026-10-15T07:47:25.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T08:00:27.000Z", "lead_minutes": 13, "primary_published_at": "2026-10-15T07:47:25.000Z", "sources": [ "bluesky-ai", "wires-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 1 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:58:20.287Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "reuters.com", "pillar_reason": "source default", "publisher": "Reuters", "publisher_url": "https://www.reuters.com", "score_components": { "corroboration": 12.5, "coverage": 5, "engagement": 1.6, "entities": 0, "recency": 19.8, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": { "id": "29141ebe6275", "is_lead": true, "lead": "2fdf5bdc195498ad", "linked_by": [ { "rule": "shared-words", "with": "f9296f99d9526d0a", "words": [ "chin", "humanoid", "robot" ] } ], "members": [ "2fdf5bdc195498ad", "f9296f99d9526d0a" ], "severity": 0, "source_count": 3, "sources": [ "bluesky-ai", "techmeme", "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-15T07:47:25.000Z", "score": 57.7, "source": "wires-ai", "summary": "Reuters, via Google News.", "title": "Chinese startup DexForce trains humanoid robots using virtual data to slash costs", "url": "https://news.google.com/rss/articles/CBMiwwFBVV95cUxOY3RWbl9HdXVzVGdQMGl2SFFKU05DYW5TbXBHOGF3WDFnQWJOa2NHalNlc3JjeXB6b1FMbmgzY1RPOTI3b3hNUU8xb0lDb0pEeVdpQjBRaEl6aWZfSERLbUNHLUc5U0NkTkJTMjJtY3Bab2NnNWo1MDh4WXpOMWNnc3lZdHlmUjNyZmlSaVNjeHJqMHd1aXNFaVZkdGVTOXdyc3UybE5RVDg2ZWVYdnNFSC15WFVRNldielZzRFJ5Yk01OFU?oc=5" }, { "entities": [], "id": "6c4772dabbaa6617", "kind": "press", "meta": { "age_hours_at_score": 0.6, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:45:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T07:45:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:45:00.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T08:04:19.125Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "wsj.com", "pillar_reason": "source default", "publisher": "WSJ", "publisher_url": "https://www.wsj.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.8, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:45:00.000Z", "score": 38.6, "source": "wires-ai", "summary": "WSJ, via Google News.", "title": "U.K. Economy Defies Gloomy Predictions With Help From AI", "url": "https://news.google.com/rss/articles/CBMikwFBVV95cUxQQmE5VndlSVB3NGFIb3FEelExSFI4dFdOckJ6a0lvWTdiTlY4OXhBT2xzbllyQUx3MmNzV1dSQjhaTkV6M3l6X1lBcFBhYnF5VXBmTW55NWMyVEtEQ1RLR3BRRjdkZEE0YmYwdDlnbF9KTnJGcFVaM09oTnhvMzA1eE53V1lwLVRyd0swTzd2S0ZTYmM?oc=5" }, { "entities": [ "Alibaba Qwen", "Qwen" ], "id": "cef86a2cb8e4037d", "kind": "press", "meta": { "age_hours_at_score": 0.6, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:44:24.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:44:24.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:44:24.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Alibaba Qwen" ], "models": [ "Qwen" ] }, "first_seen_at": "2026-10-15T07:58:20.287Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "ad-hoc-news.de", "pillar_reason": "source default", "publisher": "ad-hoc-news.de", "publisher_url": "https://www.ad-hoc-news.de", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 19.8, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:44:24.000Z", "score": 39.6, "source": "gnews-ai", "summary": "ad-hoc-news.de, via Google News.", "title": "Alibaba launches Qwen N1 AI glasses. Alibaba Group stock falls 2.42 percent", "url": "https://news.google.com/rss/articles/CBMiyAFBVV95cUxPak9oM0lyb29UUzNhN2hVX0oya1k0TDF6RHdLMDlWY2dHNTJ4QmZRVDVWQjBpSnRDNXZFNHlFcjlaODB1TlJQdlEwRWZBVjV3U09qRkJTbVlKNzNYNlVsXzdiLTNCQUc0VmZWUlNKcTg5UUs5ek1MMHZHdlZHY0NZTWdybldNYkhHWFVFQ2hvQnZsajJGa1pIb24wMWxYMlkycHhucVY5bW1Md1FJZGJiaVppbTBaTzl6MHZyWk5mR3R1Q1Jwb0wxOA?oc=5" }, { "entities": [], "id": "2d6b62f0f4ece587", "kind": "press", "meta": { "age_hours_at_score": 0.7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:40:46.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:40:46.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:40:46.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:58:20.287Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "pandaily.com", "pillar_reason": "source default", "publisher": "Pandaily", "publisher_url": "https://pandaily.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.7, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:40:46.000Z", "score": 33.5, "source": "gnews-ai", "summary": "Pandaily, via Google News.", "title": "Mathematicians Use Shanghai AI Lab's Intern-S2 to Prove 3D Ghys Conjecture, Verified in Lean", "url": "https://news.google.com/rss/articles/CBMipAFBVV95cUxNM1lFeUNnNXdialBFbTRRWmEwWGZCSzkzTjhIYVhJa1pwc0V3NThBeWN4YUw1RmRKcXRwU2J3eDJZYzVlRHQwN1JFaHhQWVQtR0lTOGhDLXBHMUpoTXl1cUFvZ2l6b0lSQmpuRlFTMG56WTZYUEVoTWNzWjVYSHJpNFhIWkotTGh3X01ZRFYwV2Nub2ZsX2x2aWhjWHV2eEkzRmdNZw?oc=5" }, { "entities": [], "id": "21c0f03d1962a083", "kind": "press", "meta": { "age_hours_at_score": 0.7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:40:00.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:40:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:40:00.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:58:20.287Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "chosun.com", "pillar_reason": "hardware/capital keyword", "publisher": "Chosunbiz", "publisher_url": "https://biz.chosun.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.7, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "compute", "published_at": "2026-10-15T07:40:00.000Z", "score": 33.5, "source": "gnews-ai", "summary": "Chosunbiz, via Google News.", "title": "VesselAI shifts to neocloud, targets 200,000 GPUs by 2030 in South Korea - CHOSUNBIZ", "url": "https://news.google.com/rss/articles/CBMiekFVX3lxTE9kY08xYlZ6ZmVGMWVKcldiLWxMVF9QcGtEVW9ScVRJeWxVNlZPcW1HZmRZRnNlM19iQzI2M1ozNGNmNVZILVFmYTBiaE5qUzdSSTFESnhFeEtORnoydHNjUWVrVGJSTHY3UzRnYVNNV3VOVHE0S2VYV3h30gGOAUFVX3lxTE10Sm02dmtJNkpzRmJYOWY1aUJVVS1XcUpKdGJMbGkyd3lZNkwzRWVRSGY5eGR5MWl5NjdUVVVld0xkNmRzbnF1dlBkWUROelVOYm1QNmtxM0d2THBZVG1jYWFlSFNMaUhiM3dQRi1kLW5ieS1DUzJTZXhDdzZ3VUlvWnZ5X1pDTjA2eXR5b2c?oc=5" }, { "entities": [ "Anthropic" ], "id": "f47237ff75e651b0", "kind": "forum", "meta": { "age_hours_at_score": 0.7, "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:37:31.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-15T07:37:31.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:37:31.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50100496", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 3 }, "entity_kinds": { "labs": [ "Anthropic" ], 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"7604567454cfc303", "98842f779147e049", "dcf332e0d23bb416", "e36d56a8a8a62f0d", "f47237ff75e651b0" ], "severity": 0, "source_count": 9, "sources": [ "bbc-tech", "bloomberg-tech", "bluesky-ai", "ft-ai", "gnews-ai", "hn-fresh-ai", "nyt-tech", "techmeme", "wires-ai" ] }, "window_hours": 2 }, "pillar": "compute", "published_at": "2026-10-15T07:37:31.000Z", "score": 38, "source": "hn-fresh-ai", "summary": "New on Hacker News: 3 points, 1 comments so far.", "title": "Anthropic's Quarterly Revenue Jumped 45% to Nearly $17B", "url": "https://wsj.com/tech/ai/anthropics-quarterly-revenue-jumped-45-to-nearly-17-billion-1c7e28b8" }, { "entities": [ "TSMC" ], "id": "63901e460b8fbba9", "kind": "press", "meta": { "age_hours_at_score": 0.8, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": "aa.com.tr", "published_at": "2026-10-15T07:36:46.000Z", "source": "gnews-ai", "title": "TSMC profit jumps 68.7% to record high as AI chip demand fuels growth", "url": "https://news.google.com/rss/articles/CBMipwFBVV95cUxNTHBtQUpnd21DcXUyQVdoc09HU0JLcmdsQ1JYQlkyd3I0d2J2M28yR19CQTlWcy13aWdickI2SWdncFN1djJwU21YX3VodXhVQ19GTHlaeDFCNC1PZmN3M05vVVNBc3pMRW91TzJYX1Bqbmh2akdDTEYySGprVzdiQWxEU29RZ3Z5WDNWbUxDei1mODY0LWMwajcyVmJxcmlhZTRJODZNTQ?oc=5", "weight": 0.55 } ], "count": 1, "first_seen_published_at": "2026-10-15T07:36:46.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:37:11.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:37:11.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "TSMC" ], "models": [] }, "first_seen_at": "2026-10-15T07:39:37.691Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "aa.com.tr", "pillar_reason": "hardware/capital keyword", "publisher": "Anadolu Ajansı", "publisher_url": "http://www.aa.com.tr", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, 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"tsmc" ] } ], "members": [ "0a55943b50812b3d", "1d21ad3b85d15530", "210621f9ac5bb73c", "4e77036ce45202de", "63901e460b8fbba9", "79bd3ba482daff2d", "85f25bcb68531441", "9abd2667a185bf33", "c8e74cdda34787ec", "db7de00c48010628", "e07f6f4e87fe86b1", "e2fa9544501179ca", "e3e32d7d473c157c", "e5adffa73aaf9a9c", "f7fdca7cbe7c0131" ], "severity": 0, "source_count": 6, "sources": [ "arxiv-newest", "bloomberg-tech", "bluesky-ai", "gnews-ai", "techmeme", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-15T07:36:46.000Z", "score": 36.5, "source": "gnews-ai", "summary": "Anadolu Ajansı, via Google News.", "title": "TSMC profit jumps 68.7% to record high as AI chip demand fuels growth", "url": "https://news.google.com/rss/articles/CBMiqwFBVV95cUxQU19xQzRLajE5Q2QyanJGTDFHaDFTYW8tRjBDelNQaTkyLXI3M0hZMklLajdZVm43Q3NFQ3ZqQVJTQVpTd01oeVhpT3ZzVUlZVUdxa3N0TDh3UWphb01EbmJLTDBlU2Q3N3VrLXRTRDhuX3hQZVlBRW1vWkZ5LTlmU1dPR2pkVWlLald0U1JjNjd0X09ackRCRjNpeElTRFFOU0hra2tYZWVvQWc?oc=5" }, { "entities": [ "NVIDIA" ], "id": "594c17dc92d24907", "kind": "press", "meta": { "age_hours_at_score": 0.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:36:38.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:36:38.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:36:38.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "NVIDIA" ], "models": [] }, "first_seen_at": "2026-10-15T07:39:37.691Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "finchannel.com", "pillar_reason": "source default", "publisher": "finchannel", "publisher_url": "https://finchannel.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.7, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:36:38.000Z", "score": 36.5, "source": "gnews-ai", "summary": "finchannel, via Google News.", "title": "Danaher teams with NVIDIA to bring AI into scientific instruments", "url": "https://news.google.com/rss/articles/CBMiyAFBVV95cUxQd25HMWd2S2ZFWVJ6U09ESERXRjZ5bUhrczN1NVNTUU1NZ2VBZnl0NUdVX2ZveDNMLWlld19NeVpzRVI4RV9wRnhONldzdTlIZ3lmMDI5UlFSb004NVg3dXM0NFVQY0RrNTRjMmw1a2RqWjdsRzlxNUU5eExCUzQtZndseWZrVnZ3dUp2OV9leUNrTF9CLXM0RjNpZnRwc3BRSFpveGhhZDluZS12dXJUcVY2SUgtMGtfbE9hanFKSWNaYkMxMXZmLQ?oc=5" }, { "entities": [ "TSMC" ], "id": "0702aeb0b031678d", "kind": "press", "meta": { "age_hours_at_score": 0.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:36:15.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-15T07:36:15.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:36:15.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "TSMC" ], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", 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via Google News.", "title": "Samsung Showcases AI-Native 6G Technologies at Silicon Valley Summit", "url": "https://news.google.com/rss/articles/CBMilgFBVV95cUxPRlRvanVsY3BjU1BOeXZNbVR4SFJMVmlNLUR6a193WERVYktTY3ZkbXVFRFFvX3Jma25jRzh4RHBOblFaMnBCRnNjUU9MU2NYWTgwVmxrYXhwTE9kTGREQ01IZ3JWOTRGSFpWaUlsSGJ0bFdmb2U2aU1weFFRaWhFQk9nN3VibmhvTGZLSUZaUXJfZGNnVXc?oc=5" }, { "entities": [ "Anthropic" ], "id": "7604567454cfc303", "kind": "press", "meta": { "age_hours_at_score": 0.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:35:04.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:35:04.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:35:04.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "first_seen_at": "2026-10-15T07:39:37.691Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": 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"2026-10-15T07:35:04.000Z", "score": 36.5, "source": "gnews-ai", "summary": "trendingtopics.eu, via Google News.", "title": "Anthropic Brings In $16.8 Billion in a Single Quarter Ahead of I.P.O.", "url": "https://news.google.com/rss/articles/CBMimgFBVV95cUxPVkotMzlrOVhaWUFGY3A5UUQ3VkE5RmFhZFltd2UxTXNSSnhkVUxKMUFTNmt1OFYyTjczODZ3aUpEY0pWOUhTdHBnM1c2SFlrTENVbUFnYkwtTjhxaVJLc09kSGNyY1FJQjFRSWxqOHdUZVlUWEhIT0RhRXR4QWZSVi05b25HeG5sNlRuSkpwR0EyaWxidnBjRFBR?oc=5" }, { "entities": [ "TSMC" ], "id": "e5adffa73aaf9a9c", "kind": "press", "meta": { "age_hours_at_score": 0.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:34:18.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:34:18.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:34:18.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "TSMC" ], "models": [] }, "first_seen_at": "2026-10-15T07:58:20.287Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "chosun.com", "pillar_reason": "hardware/capital keyword", "publisher": "조선일보", "publisher_url": "https://www.chosun.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.7, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": { "id": "ce8b11d9deae", "is_lead": false, "lead": "c8e74cdda34787ec", "linked_by": [ { "rule": "shared-words", "with": "63901e460b8fbba9", "words": [ "demand", "profit", "record", "tsmc" ] } ], "members": [ "0a55943b50812b3d", "1d21ad3b85d15530", "210621f9ac5bb73c", "4e77036ce45202de", "63901e460b8fbba9", "79bd3ba482daff2d", "85f25bcb68531441", "9abd2667a185bf33", "c8e74cdda34787ec", "db7de00c48010628", "e07f6f4e87fe86b1", "e2fa9544501179ca", "e3e32d7d473c157c", "e5adffa73aaf9a9c", "f7fdca7cbe7c0131" ], "severity": 0, "source_count": 6, "sources": [ "arxiv-newest", "bloomberg-tech", "bluesky-ai", "gnews-ai", "techmeme", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-15T07:34:18.000Z", "score": 36.5, "source": "gnews-ai", "summary": "조선일보, via Google News.", "title": "TSMC Posts Record 31.62T Won Profit Driven by AI Demand", "url": "https://news.google.com/rss/articles/CBMiiAFBVV95cUxNTFE1NGpLOThGUWJOZzdpRFh0d2taRWlUMjk1YjJRdHhvVmxreHNVdk5mY0M2OE5ELWp4SDRmNU4xaDZNRFNzQmlYeFVRbjA2a2JaZUZSLWxQNF9jVlRySnFSRXFaUzZhN1pYbmh6c1l2MUhld1NyUVYybGtKVXQ1Nm4zUmg4eHV5?oc=5" }, { "entities": [ "NVIDIA" ], "id": "24ac87545b5734ab", "kind": "press", "meta": { "age_hours_at_score": 0.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:33:49.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:33:49.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:33:49.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "NVIDIA" ], "models": [] }, "first_seen_at": "2026-10-15T07:39:37.691Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "aimagazine.com", "pillar_reason": "source default", "publisher": "AI Magazine", "publisher_url": "https://aimagazine.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.7, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:33:49.000Z", "score": 36.5, "source": "gnews-ai", "summary": "AI Magazine, via Google News.", "title": "The AI Interview: Michael Kagan, CTO at NVIDIA", "url": "https://news.google.com/rss/articles/CBMihAFBVV95cUxOYVYyR2hxajRRc0lWYWN4b2ZUV0p4T3NnaFM4RmxnbjZqMHRoQUh4Y2w2NnplRElGelcyRy1wazlTVW5takVmdjZ5N2RsWk5rNjFMdEs5WW84emY4T0sycndJZWFuUHJnMGtZNEluSVdTUXl6bVhNeUtFY3JwY3dDV0h6QW4?oc=5" }, { "entities": [ "Hugging Face" ], "id": "d824a2f4e4906e39", "kind": "press", "meta": { "age_hours_at_score": 0.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:33:43.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:33:43.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:33:43.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Hugging Face" ], "models": [] }, "first_seen_at": "2026-10-15T07:39:37.691Z", "incident": { "band": "C", "excluded": null, "severity": 0.3, "strong_signal": false, "terms": [ { "field": "title", "matched": "incident", "term": "incident", "tier": "c", "weight": 0.3 } ] }, "outlet_domain": "cybersecurity-insiders.com", "pillar_reason": "source default", "publisher": "Cybersecurity Insiders", "publisher_url": "https://www.cybersecurity-insiders.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.7, "severity": 4.5, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:33:43.000Z", "score": 41, "source": "gnews-ai", "summary": "Cybersecurity Insiders, via Google News.", "title": "What the Hugging Face incident tells us about deploying AI agents safely", "url": "https://news.google.com/rss/articles/CBMisgFBVV95cUxOb0pHMzRpODdKdHE4b0pQanVtSVlxSnJDWENMLTVzZ0ZrTUx0d3VjbnFRaWlKcjZVNzh3dkVMZVEyMTdmTDlZU0paN3NOWnBfbFJ4SWE5ZktBQnJRQWctdUdabGVReWhadEdfM191NVd0akJIcDl6bHZrdnotclpBTnF0dGJZaWdVSlI4aHJtbXNYN3JCdmljTFg2bXY4dHhKQnctdndTYVBiaUpndkFCSUpn?oc=5" }, { "entities": [ "Google DeepMind" ], "id": "cf1fd0f11e2a1890", "kind": "press", "meta": { "age_hours_at_score": 0.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:32:38.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:32:38.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:32:38.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Google DeepMind" ], "models": [] }, "first_seen_at": "2026-10-15T08:02:19.413Z", "incident": { "band": "A", "excluded": null, "severity": 1, "strong_signal": false, "terms": [ { "field": "title", "matched": "hacks", "term": "hacks", "tier": "a", "weight": 1 } ] }, "outlet_domain": "thenews.com.pk", "pillar_reason": "source default", "publisher": "The News International", "publisher_url": "https://www.thenews.com.pk", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.7, "severity": 15, "source_weight": 13.8 }, "source_weight": 0.55, "story": { "id": "44510b1e1311", "is_lead": false, "lead": "5c998e059454924e", "linked_by": [ { "rule": "escalation-signal", "with": "21e7ded87d1e2500", "words": [ "warn" ] }, { "rule": "escalation-signal", "with": "25a1fb29d96c2c18", "words": [ "warn" ] }, { "rule": "escalation-signal", "with": "5c998e059454924e", "words": [ "warn" ] }, { "rule": "escalation-signal", "with": "818085bd085cd9ce", "words": [ "warn" ] }, { "rule": "escalation-signal", "with": "98900f3d4137eee5", "words": [ "warn" ] } ], "members": [ "21e7ded87d1e2500", "25a1fb29d96c2c18", "5c998e059454924e", "818085bd085cd9ce", "98900f3d4137eee5", "cf1fd0f11e2a1890" ], "severity": 1, "source_count": 7, "sources": [ "arstechnica-ai", "bluesky-ai", "ft-ai", "gnews-ai", "guardian-ai", "verge-ai", "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-15T07:32:38.000Z", "score": 51.5, "source": "gnews-ai", "summary": "The News International, via Google News.", "title": "AI agent hacks are 'early warning shots': Google DeepMind co-founder", "url": "https://news.google.com/rss/articles/CBMiqwFBVV95cUxOdGhMN3d1cFdFYWdjdmhOQW0xV1VyZHI2LVU0U2RnaW1MQVhwZ25zaFg2VEUtUkVzbE5NSUNMT2hxQlFDRHFnVE9xZlRYSS1YOS1LX3FfUEg3YzhHUDJDUy14eEJVZEZibXpuRlJZd3dhaTF1UkROUlB4YktfbzAzY2g2MGtzVktjVGVETGJrUm9PRGhscy1ZNk0xbWUwaDB4NjV4UEtfRmdFREHSAacBQVVfeXFMT1piVlZ4LUlaRzRVb1R2QW1sVEtRYVVtc2YyYzBUSkZNMWNqWnF4NC0zVjJlbGFfSGEycEdUUVAyWUE0elBaRVh6dWdTZEFraXhISEItZXRhSlJha2hCbnBmN3Y0VUUyQWRCbWFsdnRMUjlMUnVzWmhqZVJrN3J3NzkxSTAyNm10azNLMjhSdU5xdHNDb21UU2xydEdKVWY5RFI0eF9odjQ?oc=5" }, { "entities": [], "id": "33e98529b4e63af1", "kind": "press", "meta": { "age_hours_at_score": 0.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:32:03.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:32:03.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:32:03.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:39:37.691Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "swissinfo.ch", "pillar_reason": "source default", "publisher": "SWI swissinfo.ch", "publisher_url": "https://www.swissinfo.ch", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.7, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:32:03.000Z", "score": 33.5, "source": "gnews-ai", "summary": "SWI swissinfo.ch, via Google News.", "title": "Switzerland’s open AI model could help guard the web – or empower its attackers", "url": "https://news.google.com/rss/articles/CBMizgFBVV95cUxOSE96aW1nNHhScU5JN0VIQ1dJVGp1dXJVZk56ek05S1FIQkVUdHVRSEMweXJIcFlidk80UkpNYW11N2swQWFfMDYxelg3U3ZqOURjanQ0QkVLTElYQXo1bWVkZDBvaWQ5ME9aTUQtdnVtWUJyWnp6VG9md0RTVnp4b1MyamloRVF2aWluR3kyVGdYMkdUeVpiRTVHLTNmeDlCZ3hFQ19PQjNEMHF0UjNoeUMtY3dwNmNZeVdONnJMZ1U0dVNnOHJsTmF6d2duQQ?oc=5" }, { "entities": [], "id": "4fb92fa54c2aca84", "kind": "press", "meta": { "age_hours_at_score": 0.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:31:40.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:31:40.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:31:40.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:58:20.287Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "vietnam.vn", "pillar_reason": "source default", "publisher": "Vietnam.vn", "publisher_url": "https://www.vietnam.vn", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.7, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:31:40.000Z", "score": 33.5, "source": "gnews-ai", "summary": "Vietnam.vn, via Google News.", "title": "The trend of searching for specialized AI tools is increasing sharply.", "url": "https://news.google.com/rss/articles/CBMihwFBVV95cUxPYlFSbDAzQVlST1BSRzdpelhmaVc0ZUZxV3F0VWdNTVVvQk1DbEVRQThyZFgySWJVSkdsOFF1c0tpazFrYTNfVkJyRTd5dXNJSnNoYkx5VTJ4SktNajBRbWdXQkh5cUpNMUVSWXNEdlZhMW5SSUQyb3pLVXlGT0k4TlQ1NjFNTXc?oc=5" }, { "entities": [ "OpenAI" ], "id": "6bec7d11ba5f3d7b", "kind": "press", "meta": { "age_hours_at_score": 0.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:31:27.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:31:27.000Z", 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"41d835035e2a01e4", "kind": "press", "meta": { "age_hours_at_score": 0.9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:31:16.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:31:16.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:31:16.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "first_seen_at": "2026-10-15T07:39:37.691Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "europeanbusinessmagazine.com", "pillar_reason": "hardware/capital keyword", "publisher": "European Business Magazine", "publisher_url": "https://europeanbusinessmagazine.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.7, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": { "id": "f27ed1f158bf", "is_lead": false, "lead": 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"2026-10-15T07:29:11.000Z", "score": 36.5, "source": "gnews-ai", "summary": "BigGo Finance, via Google News.", "title": "China's AI chip market reshuffles as Nvidia shipments halve; domestic non-GPU accelerators overtake for the first time", "url": "https://news.google.com/rss/articles/CBMidkFVX3lxTE9CWmU2RmR3bm5TaUl2RFgxcVZpTk4tNXNaN2xwdi1pdE1SQTdzenhsZ2d5UTlRVG5TczR1aXU5ZW9xcmRuTm1BSDFvaTNGbFBicGhfNU9iNTQ2am5nb0tISlZGUXc3RUs5alhsY1dhdVFDVFJpTkE?oc=5" }, { "entities": [ "AMD" ], "id": "5551879c58fab2e4", "kind": "press", "meta": { "age_hours_at_score": 0.9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:27:00.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:27:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:27:00.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "AMD" ], "models": [] }, "first_seen_at": "2026-10-15T07:39:37.691Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "chosun.com", "pillar_reason": "source default", "publisher": "Chosunbiz", "publisher_url": "https://biz.chosun.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.6, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:27:00.000Z", "score": 36.4, "source": "gnews-ai", "summary": "Chosunbiz, via Google News.", "title": "Arm claims AI agent CPU doubles rack throughput versus AMD - CHOSUNBIZ", "url": "https://news.google.com/rss/articles/CBMiekFVX3lxTE1WZHRHM2l0enN4N3UwTXdUQVMzcWxTR2NqWVJPOTdRU203ZUd1aXFUQk5lRkd1MWZrU3JoM2xPaUxPcm1kekVpR05VMVBpM1dxM1VxSEw1WmxIakh1UEVPeFlIX1FOWVNOQ2tTTlJkTVFjalJ4R0J0MHdB0gGOAUFVX3lxTE5CSlp2RUNfQXRKY1VlX2dTSkRjbC1UdlZHV18yZk1SUmFpbVpodlUyTEdXUmZKMmZmUXlDMHhjUTdfLWV6RV80NE1EeFFoUlpLY0UwN2ljZXZtUjI0M1lralFIc0Zkd1NKSjJYajRyX05YNlJnV1M5czVIU2dYbXVZQWZiS1JSNXdDZFVid3c?oc=5" }, { "entities": [], "id": "8c279acf59499cab", "kind": "press", "meta": { "age_hours_at_score": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:23:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T07:23:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:23:00.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:58:20.287Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "wsj.com", "pillar_reason": "source default", "publisher": "WSJ", "publisher_url": "https://www.wsj.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.6, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:23:00.000Z", "score": 38.4, "source": "wires-ai", "summary": "WSJ, via Google News.", "title": "European Indexes Mixed as AI Selloff Deepens", "url": "https://news.google.com/rss/articles/CBMikwFBVV95cUxPMVdWaEpPbFR3RUJMZENxRmNnQlhxa0lLNGRTNUk5clVjYUhWdUxLS0FkOFMxb3FUOHd1M1E1MTlub2lFOHY1S3JCQ01qQVV3VUl3Yk5ScE1fcGNNdVBqQ3ZlVzVlWHlVOWt0TTBRcXJLS3VfNjQ4MTVHR0FkdWFOVk82OFR4ZEswbTdzajhNMEE1OHM?oc=5" }, { "entities": [ "Tesla" ], "id": "4dc5632e26492e3a", "kind": "press", "meta": { "age_hours_at_score": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:22:59.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T07:22:59.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:22:59.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Tesla" ], "models": [] }, "first_seen_at": "2026-10-15T07:39:37.691Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "wsj.com", "pillar_reason": "source default", "publisher": "WSJ", "publisher_url": "https://www.wsj.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.6, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:22:59.000Z", "score": 41.4, "source": "wires-ai", "summary": "WSJ, via Google News.", "title": "Exclusive | An Ex-Tesla Executive Is Steering Uber Freight’s Turnaround", "url": "https://news.google.com/rss/articles/CBMipwFBVV95cUxPMVU3ZE1wUEtHZlZzUTA1WEVuaGt1ZE1IYXlhVm9KOU55X0MtY3VVQ0kzb2FyWXhMcEN3ZWhXUVF2UG5tcTY3LUxhNWpOclI3WFFrQjlheW1lMUdBQnFCNTdpbU9KVmpmQ3A2eVdsa25vSmN0anY2X2NZalJfbkpFdHZQMG0zUzhNNjFLTWp2a1BjUkZKdHduaW9lMEVSdXBNX0VuY0hXMA?oc=5" }, { "entities": [ "Anthropic" ], "id": "6c340f3efbdbf860", "kind": "press", "meta": { "age_hours_at_score": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:20:45.000Z", "first_source": "bbc-tech", "last_published_at": "2026-10-15T07:20:45.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:20:45.000Z", "sources": [ "bbc-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "feed": "https://feeds.bbci.co.uk/news/technology/rss.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "hardware/capital keyword", "rejected_off_topic": 10, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.6, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "f27ed1f158bf", "is_lead": false, "lead": "2bb464d7faf82e22", "linked_by": [ { "rule": "shared-words", "with": "98842f779147e049", "words": [ "anthropic", "market", "revenu" ] }, { "rule": "shared-words", "with": "dcf332e0d23bb416", "words": [ "ahead", "anthropic", "flag" ] } ], "members": [ "08b2cdffdbe6bf48", "2bb464d7faf82e22", "41d835035e2a01e4", "45e05b50f5cecac5", "4c31c7cec80ac7f6", "6c340f3efbdbf860", "6f76a14503cf2093", "7604567454cfc303", "98842f779147e049", "dcf332e0d23bb416", "e36d56a8a8a62f0d", "f47237ff75e651b0" ], "severity": 0, "source_count": 9, "sources": [ "bbc-tech", "bloomberg-tech", "bluesky-ai", "ft-ai", "gnews-ai", "hn-fresh-ai", "nyt-tech", "techmeme", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-15T07:20:45.000Z", "score": 37.6, "source": "bbc-tech", "summary": "It comes as some investors have become concerned about the amount of money being spent on AI infrastructure.", "title": "Anthropic flags revenue surge to investors ahead of stock market debut", "url": "https://bbc.co.uk/news/articles/cqzjx448vxkgo" }, { "entities": [], "id": "872070710cbf3e3b", "kind": "press", "meta": { "age_hours_at_score": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:20:00.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:20:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:20:00.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:39:37.691Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "chosun.com", "pillar_reason": "source default", "publisher": "Chosunbiz", "publisher_url": "https://biz.chosun.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.6, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:20:00.000Z", "score": 33.4, "source": "gnews-ai", "summary": "Chosunbiz, via Google News.", "title": "Fadu debuts file semantic SSD, lets AI accelerators read data directly - CHOSUNBIZ", "url": "https://news.google.com/rss/articles/CBMiekFVX3lxTFBUZElhREYtSlVPT1NrYkE1RVA3Q0ZmblNta0IweDdQOTJ1V2g1THBKSFN2LTlIWG5MYW9wdU9XTEY5UjNpWE9oSVB6bHgyUXhzV2c1aTdVZXM0R2NIQlpUUFhKRURpZElNMGVVemFYUzJVWDIxWHRpS2t30gGOAUFVX3lxTE5xWTV3MkpUZ1RDemFyQ0JGM3BMUUFld2U2NWxLUjBjT3dXYWNrdEN0SUY2TVVSLXRTZV9uNHlkSi1OT3ZZR0NNdUZ6R0dzcnVTNUpfQW5Hek1UN1AtQkhXOEJDRmwxaEZfb3Q0VlJRbzh4RkVsLUU1VDNONzU4VFM5RDZMQ3Z2aVd6T2pXN0E?oc=5" }, { "entities": [], "id": "ba5e773ab5090073", "kind": "press", "meta": { "age_hours_at_score": 1.1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:17:04.000Z", "first_source": "ft-ai", "last_published_at": "2026-10-15T07:17:04.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:17:04.000Z", "sources": [ "ft-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.ft.com/artificial-intelligence?format=rss", "first_seen_at": "2026-10-15T08:09:18.770Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "hardware/capital keyword", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.6, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "compute", "published_at": "2026-10-15T07:17:04.000Z", "score": 35.9, "source": "ft-ai", "summary": "The inside story on the Asia tech trends that matter, from Nikkei Asia and the Financial Times", "title": "Robot dolls, chip shortages and Apple’s soft sales", "url": "https://ft.com/content/b771ff23-8f40-4b44-ad50-50cea967a171?syn-25a6b1a6=1" }, { "entities": [ "Anthropic" ], "id": "95519afb5e9a2925", "kind": "press", "meta": { "age_hours_at_score": 1.1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:16:26.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:16:26.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:16:26.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "first_seen_at": "2026-10-15T07:58:20.287Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "livenowfox.com", "pillar_reason": "source default", "publisher": "LiveNOW from FOX", "publisher_url": "https://www.livenowfox.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.6, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:16:26.000Z", "score": 36.4, "source": "gnews-ai", "summary": "LiveNOW from FOX, via Google News.", "title": "Anthropic’s New AI Rules Explained", "url": "https://news.google.com/rss/articles/CBMiY0FVX3lxTE85S3FNcWNNa0UyZUp5d3JxLTFXa3VnRFZZclg2SmVuS2NJVGVBSTd3RWh5NVhsRnF2RmRJeGFWMDVHQ2pteFdia3VZOE5BakFSRl9BejN5ZXdBdnIxOE9CdVAwY9IBaEFVX3lxTE5KalNYNFRHdXhrbV9WWkpmU0tyZXU4dV9zNzJ3TjhtZ3lhVGlRSm4yY2NlVFBhRVBIblpsZmlERUtWTy1hUVpyakdvMGtEd044UEJ2dG9LMU9VX0l1c1NkM0FtLWd2TnQ3?oc=5" }, { "entities": [ "GPT", "OpenAI" ], "id": "f0aeaf09406aa3eb", "kind": "press", "meta": { "age_hours_at_score": 1.1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:15:41.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:15:41.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:15:41.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [ "GPT" ] }, "first_seen_at": "2026-10-15T08:10:19.630Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "informat.ro", "pillar_reason": "source default", "publisher": "Informat.ro", "publisher_url": "https://informat.ro", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 19.6, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": { "id": "6f5b03c289ee", "is_lead": false, "lead": "8a32126259e22cff", "linked_by": [ { "rule": "shared-words", "with": "314164cb6dc66c59", "words": [ "chatgpt", "openai", "stat", "sue" ] }, { "rule": "shared-words", "with": "3985ae25e3ef1633", "words": [ "openai", "stat", "sue" ] }, { "rule": "shared-words", "with": "8a32126259e22cff", "words": [ "chatgpt", "openai", "stat", "sue" ] }, { "rule": "shared-words", "with": "afdd4bd9efa8609e", "words": [ "openai", "stat", "sue" ] } ], "members": [ "314164cb6dc66c59", "3985ae25e3ef1633", "8a32126259e22cff", "8b16dc6b17676a97", "afdd4bd9efa8609e", "f0aeaf09406aa3eb" ], "severity": 0.3, "source_count": 7, "sources": [ "bluesky-ai", "gnews-ai", "guardian-ai", "hn-fresh-ai", "techmeme", "verge-ai", "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-15T07:15:41.000Z", "score": 39.4, "source": "gnews-ai", "summary": "Informat.ro, via Google News.", "title": "Eight American states sue OpenAI over ChatGPT risks", "url": "https://news.google.com/rss/articles/CBMikwFBVV95cUxNOGMtLTVNZFJ6dEVmOXJ3cTE0YjFYa1ZsNm5QNm9GWl84alpLUDNQczQyck4tenJ6OGp5cGh5YmtoNGc0eWpiT0FfV0tPNTRCZVJqQ0RrT21ySDYxdmxySU5RcEU1d29FNlRFVlpyNk1mamRlRTl1TmZld3hyQ3RmTlpYb2dvRkVOUmowQkRRYTZ2bGM?oc=5" }, { "entities": [], "id": "d9e9684d9bcff72e", "kind": "press", "meta": { "age_hours_at_score": 1.2, "corroboration": { 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to Unwind AI Exposure", "url": "https://news.google.com/rss/articles/CBMiswFBVV95cUxNS1ZmX19fclBFd1I3bEdCWVk5NEpfRmJGWE1VbjhDRThlVUFFeFBqYzYzTXpTR2wzeHNUcWtNN1VFV2ltaC1WNnc1S1ZsOFMxalVER01yS0otb2VjbTQ1cHJjZERsa05rMHNabDVyenp5MUlLNmpZQzdERzVzbElnTlgtQ0ljV1hsOGJNTEMwNEFPUmMtSXdjUzE3b0REWmR0ZDNfQzh0RU5UalY1dWg3b1pwVQ?oc=5" }, { "entities": [ "CoreWeave" ], "id": "ab1dd5db63060246", "kind": "press", "meta": { "age_hours_at_score": 1.2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:12:26.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:12:26.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:12:26.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "CoreWeave" ], "models": [] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "investing.com", "pillar_reason": "source 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"last_published_at": "2026-10-15T07:12:20.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:12:20.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "incident": { "band": "C", "excluded": null, "severity": 0.3, "strong_signal": false, "terms": [ { "field": "title", "matched": "lawsuit", "term": "lawsuit", "tier": "c", "weight": 0.3 } ] }, "outlet_domain": "lmtribune.com", "pillar_reason": "regulatory/legal keyword", "publisher": "The Lewiston Tribune", "publisher_url": "https://www.lmtribune.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.6, "severity": 4.5, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "governance", "published_at": "2026-10-15T07:12:20.000Z", "score": 40.9, "source": "gnews-ai", "summary": "The Lewiston Tribune, via Google News.", "title": "Idaho files lawsuit against OpenAI", "url": "https://news.google.com/rss/articles/CBMihwFBVV95cUxNQ3h1QkV6N2VrcGVLRzFCOUZRWkMyeWUwRVo1ZmFNYWVpNWl5cDk4QXBIMnZxbmlQdmdyUERka1J2TzZGdHpPdWpTRDVWVDhSeEVtMGxWNDh4VXdkdTZhMlUxUU9UQlVpZ08wd2xaQmxlcGJEQkYtaVVsZGdqZDJxQ0RHVlJNTzg?oc=5" }, { "entities": [ "Anthropic" ], "id": "dcf332e0d23bb416", "kind": "press", "meta": { "age_hours_at_score": 1.2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:12:12.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:12:12.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:12:12.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "dealroom.co", "pillar_reason": "hardware/capital keyword", "publisher": "Dealroom", "publisher_url": "https://app.dealroom.co", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.6, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": { "id": "f27ed1f158bf", "is_lead": false, "lead": "2bb464d7faf82e22", "linked_by": [ { "rule": "shared-words", "with": "6c340f3efbdbf860", "words": [ "ahead", "anthropic", "flag" ] }, { "rule": "shared-words", "with": "7604567454cfc303", "words": [ "ahead", "anthropic", "quarter" ] } ], "members": [ "08b2cdffdbe6bf48", "2bb464d7faf82e22", "41d835035e2a01e4", "45e05b50f5cecac5", "4c31c7cec80ac7f6", "6c340f3efbdbf860", "6f76a14503cf2093", "7604567454cfc303", "98842f779147e049", "dcf332e0d23bb416", "e36d56a8a8a62f0d", "f47237ff75e651b0" ], "severity": 0, "source_count": 9, "sources": [ "bbc-tech", "bloomberg-tech", "bluesky-ai", "ft-ai", "gnews-ai", "hn-fresh-ai", "nyt-tech", "techmeme", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-15T07:12:12.000Z", "score": 36.4, "source": "gnews-ai", "summary": "Dealroom, via Google News.", "title": "Anthropic posts $16.8B quarter, flags profit ahead of $2T IPO", "url": "https://news.google.com/rss/articles/CBMilwFBVV95cUxNaUVvb3QyV1hraGhHX1dCWFhMdDViR3pERTVNV1pINHRKdWxCT1huaUJOY1dvS3lpMWFHZlFfQkpnc0RCVGU2dFcxYUhaMmZlaWE2SU5hbGw5MzhJYXpfQTVrOUEyVnBET25SY3ZPTUhaWEZZMjlkMXlkSmxZVWhxaFI5SFVOSTNfaGZsQ3NBZ1AyS0VlbTdz?oc=5" }, { "entities": [ "Anthropic" ], "id": "e36d56a8a8a62f0d", "kind": "press", "meta": { "age_hours_at_score": 1.2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:12:10.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:12:10.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:12:10.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "36kr.com", "pillar_reason": "hardware/capital keyword", "publisher": "36 Kr", "publisher_url": "https://eu.36kr.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.6, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": { "id": "f27ed1f158bf", "is_lead": false, "lead": "2bb464d7faf82e22", "linked_by": [ { "rule": "shared-words", "with": "41d835035e2a01e4", "words": [ "anthropic", "ipo", "revenu" ] }, { "rule": "shared-words", "with": "f47237ff75e651b0", "words": [ "anthropic", "quarterly", "revenu" ] } ], "members": [ "08b2cdffdbe6bf48", "2bb464d7faf82e22", "41d835035e2a01e4", "45e05b50f5cecac5", "4c31c7cec80ac7f6", "6c340f3efbdbf860", "6f76a14503cf2093", "7604567454cfc303", "98842f779147e049", "dcf332e0d23bb416", "e36d56a8a8a62f0d", "f47237ff75e651b0" ], "severity": 0, "source_count": 9, "sources": [ "bbc-tech", "bloomberg-tech", "bluesky-ai", "ft-ai", "gnews-ai", "hn-fresh-ai", "nyt-tech", "techmeme", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-15T07:12:10.000Z", "score": 36.4, "source": "gnews-ai", "summary": "36 Kr, via Google News.", "title": "Anthropic Hits $16.8 Billion Quarterly Revenue, Surges Toward IPO with Strong Profitability Report", "url": "https://news.google.com/rss/articles/CBMiU0FVX3lxTE1jeEtIdC1scEo0MkhYaHFfZkFSNlhSVlRVUklmZUlFV2FubkVLcWk2OEpIaUItNzNSMkZwR2pFRUs1ek1qLXRicFgzalp0aE9haGhF?oc=5" }, { "entities": [], "id": "3d00cf4d0a66820c", "kind": "press", "meta": { "age_hours_at_score": 1.2, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": "chosun.com", "published_at": "2026-10-15T07:12:00.000Z", "source": "gnews-ai", "title": "Krafton and NC chase Korea’s enterprise AI with game-built solutions - CHOSUNBIZ", "url": "https://news.google.com/rss/articles/CBMijgFBVV95cUxPYkZMdnBLanRTd1BqWHdFemVGa2FiUWo3SVNfdlBoQV9DbTJ0SndkeFdqbGJSWng2ZzRDbUlmTklKU2l6LV9zSlRwdFpsVHJvY2ZZcjlfclRSTjNyS0MzTG05RXpwNHVDcTVkNm55QWVDWDNmT0NlOGdoNUxHbklwd1R4SmtQcDBTQkI4SUFn0gGOAUFVX3lxTE9iRkx2cEtqdFN3UGpYd0V6ZUZrYWJRajdJU192UGhBX0NtMnRKd2R4V2psYlJaeDZnNENtSWZOSUpTaXotX3NKVHB0WmxUcm9jZllyOV9yVFJOM3JLQzNMbTlFenA0dUNxNWQ2bnlBZUNYM2ZPQ2U4Z2g1TEduSXB3VHhKa1BwMFNCQjhJQWc?oc=5", "weight": 0.55 } ], "count": 1, "first_seen_published_at": "2026-10-15T07:12:00.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:12:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:12:00.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "chosun.com", "pillar_reason": "source default", "publisher": "Chosunbiz", "publisher_url": "https://biz.chosun.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.6, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:12:00.000Z", "score": 33.4, "source": "gnews-ai", "summary": "Chosunbiz, via Google News.", "title": "Krafton and NC chase Korea’s enterprise AI with game-built solutions - CHOSUNBIZ", "url": "https://news.google.com/rss/articles/CBMiekFVX3lxTFByWW9kVVlkTVNBcTk2NG0xMllmT1Vua1BmcGFzOUxsUC1YcjgyVjczc2lnLWZiZWptelc4dXJHYnd6TFJzNTE2NkM0NDUyVG1BZFB4TnVhNHdNWUF4NU5lWkdMT292aml1QmZHZFdfTmk5WUgwUnN6NTdR0gGOAUFVX3lxTE9iRkx2cEtqdFN3UGpYd0V6ZUZrYWJRajdJU192UGhBX0NtMnRKd2R4V2psYlJaeDZnNENtSWZOSUpTaXotX3NKVHB0WmxUcm9jZllyOV9yVFJOM3JLQzNMbTlFenA0dUNxNWQ2bnlBZUNYM2ZPQ2U4Z2g1TEduSXB3VHhKa1BwMFNCQjhJQWc?oc=5" }, { "entities": [], "id": "19bb3cf6d474de98", "kind": "press", "meta": { "age_hours_at_score": 1.2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:10:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T07:10:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:10:00.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T08:07:19.188Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "nv.ua", "pillar_reason": "source default", "publisher": "The New Voice of Ukraine", "publisher_url": "https://english.nv.ua", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.5, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:10:00.000Z", "score": 38.3, "source": "wires-ai", "summary": "The New Voice of Ukraine, via Google News.", "title": "Russia launches two missiles and 580 drones at Ukraine, air defense downs 536", "url": "https://news.google.com/rss/articles/CBMikgFBVV95cUxQQ2dzUzZLc0xWOXdwaEZCS1plQW4zdzlmdm5GTG5HVTh0MXUxV1I5dkNWcWRDVF8wWWdNemI5TWVzVjdzMHRHLUVQZ1Z2dWluanVnT1E2ZFA5M19oVHJCZkt2ZHN1S2RqUElZOXhReVVaRFRDMTZaVjdiMlllQkdsVzRtVHhUbno1SlRpczZ1ZkFUZ9IBjgFBVV95cUxNQU00SkRYeTdQTFRzSmJ1U3M5cGRHXy1rX092eG50eHhGRXpqVlczSWs2dENHbGY4TkFtSm55MFc5b3NSRlVOU1dwX1RrMkJfZFFrcHljcmVEaGJlU3QxYkhyRTBQeHMybzBsaFp6YmFHWnNIR3pGLVZzWnc1Y1A1WDBFY0NfTUFXU1NsbnVB?oc=5" }, { "entities": [ "OpenAI" ], "id": "32c83e7c3c9d1d26", "kind": "press", "meta": { "age_hours_at_score": 1.2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:09:55.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:09:55.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:09:55.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "analyticsindiamag.com", "pillar_reason": "source default", "publisher": "Analytics India Magazine", "publisher_url": "https://analyticsindiamag.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 5, "engagement": 0, "entities": 3, "recency": 19.5, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": { "id": "12eaea226caa", "is_lead": true, "lead": "32c83e7c3c9d1d26", "linked_by": [ { "rule": "shared-words", "with": "b7a3f9a43a69d720", "words": [ "katti", "leav", "openai", "sachin" ] } ], "members": [ "32c83e7c3c9d1d26", "b7a3f9a43a69d720" ], "severity": 0, "source_count": 2, "sources": [ "gnews-ai", "techmeme" ] } }, "pillar": "attention", "published_at": "2026-10-15T07:09:55.000Z", "score": 41.3, "source": "gnews-ai", "summary": "Analytics India Magazine, via Google News.", "title": "Sachin Katti To Leave OpenAI After Leading Major Compute Expansion", "url": "https://news.google.com/rss/articles/CBMipwFBVV95cUxQVjBmMzc3clRDbnNIc2hjN0s0THphNllTeDByOHh1TmYxV0hlRjhfNDRnZERIZXh1U19ob2RrX0NnVVlJdjh2bFdjNnhhZmtYMEhqVldCX1BpME1paFZqZkVod1NydXJScUpabEsxTFBjU21GS0ZxNGpMT3NoT2xZMTJiTVBMWWVxdUF6Mzh2X0Q0aFBXZUF2Y195RjU0Z0ZsdDNsbGlvaw?oc=5" }, { "entities": [], "id": "699e3a6c1ef03ef3", "kind": "press", "meta": { "age_hours_at_score": 1.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:06:45.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:06:45.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:06:45.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:39:37.691Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "marketbeat.com", "pillar_reason": "source default", "publisher": "MarketBeat", "publisher_url": "https://www.marketbeat.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.5, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:06:45.000Z", "score": 33.3, "source": "gnews-ai", "summary": "MarketBeat, via Google News.", "title": "Alamos Gold (NYSE:AGI) Share Price Breaks Below Two Hundred Day Moving Average - Time to Sell?", "url": "https://news.google.com/rss/articles/CBMi4wFBVV95cUxPRFpqZzJzb29kNzRPa1lxY0VaYjgzLUZMQ1hNNVhFbzJQLVduNVhUc2laS2pMcmgtdG43SllHcWZKWHh3OFlGRXJOSTBYUnRtVWdOR3Z1VnhvX3dWYWF2SVFLN0hnUXlPS3ZOT2k4N2JGX1ZOY0FzT3UtOHdhSi1RX09OYkJiTWlGNlVfYVpUOGZkS0RRU1dYd1lNVXYwaG1aMG9EdXBGTHpFcW5YaGhWWTMtWjhQME5wamNWXzFUcFBfZ2RIYTRmdUVjM3dHUU9zUGdfeXlxbzlhQzhLRm4zREZscw?oc=5" }, { "entities": [ "Anthropic" ], "id": "b141c2007e7338e2", "kind": "press", "meta": { "age_hours_at_score": 1.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:04:21.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:04:21.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:04:21.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "mixed-news.com", "pillar_reason": "source default", "publisher": "MIXED Reality News", "publisher_url": "https://mixed-news.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.5, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:04:21.000Z", "score": 36.3, "source": "gnews-ai", "summary": "MIXED Reality News, via Google News.", "title": "Anthropic ran a 26B model across 15 Android TV boxes, and got 0.39 tokens a second", "url": "https://news.google.com/rss/articles/CBMikwFBVV95cUxNS1dqWGcxNW02UVF0VWF2VUhselZtMnVBanRtYU1SM1dDeERuNW1QUVV2VGw5TGZfNzRYcXJTZTQzMW5pQkY1TlU1VXNuaG8tM0QzSS14eW1vZVB2cVhQQTdqRGV5MzRaX2w1a2xQTFpNdVR5ZDZRcVNfOXRyN29ScUg5VWlrNGw5ZzZvOF82ZzMxZlE?oc=5" }, { "entities": [ "OpenAI" ], "id": "4533e2b5df1cb4f7", "kind": "press", "meta": { "age_hours_at_score": 1.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:03:13.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:03:13.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:03:13.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "newsnationnow.com", "pillar_reason": "source default", "publisher": "NewsNation", "publisher_url": "https://www.newsnationnow.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.5, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": { "id": "f0b473571419", "is_lead": false, "lead": "66f5e62202fa6571", "linked_by": [ { "rule": "shared-words", "with": "66f5e62202fa6571", "words": [ "openai", "toilet", "wrapp" ] } ], "members": [ "0343a01e6ce9dfac", "043aba25a09a5b5c", "18ff78a815c5e459", "1d9b0f2a4e47bd83", "2444a8e5a9bf9dd7", "2845c6266106c48a", "3666ba1e02facdbf", "4533e2b5df1cb4f7", "47b1c61501a00890", "4a54f7ed9060d0c2", "4e5e343645d9b16e", "66f5e62202fa6571", "6986f04e2795a0c1", "6bec7d11ba5f3d7b", "75aa40e09badce18", "82065bf620f89892", "bab59c5f7007dae1", "c666cbb353ec8719", "de2ec4083de14f91", "e3d70f1a671b390f", "f67cff07b7fcf963", "fdcf029b66aaffa6" ], "severity": 0.35, "source_count": 16, "sources": [ "arstechnica-ai", "bbc-tech", "bloomberg-tech", "bluesky-ai", "ft-ai", "gnews-ai", "guardian-ai", "hn-fresh-ai", "nyt-tech", "reddit-chatgpt", "techcrunch-ai", "techmeme", "the-decoder", "verge-ai", "wired-ai", "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-15T07:03:13.000Z", "score": 36.3, "source": "gnews-ai", "summary": "NewsNation, via Google News.", "title": "‘$14 camera wrapped in toilet paper’: The low-tech used to spy on OpenAI", "url": "https://news.google.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?oc=5" }, { "entities": [ "TSMC" ], "id": "db7de00c48010628", "kind": "press", "meta": { "age_hours_at_score": 1.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:01:28.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:01:28.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:01:28.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "TSMC" ], "models": [] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "benzinga.com", "pillar_reason": "hardware/capital keyword", "publisher": "Benzinga", "publisher_url": "https://www.benzinga.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.5, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": { "id": "ce8b11d9deae", "is_lead": false, "lead": "c8e74cdda34787ec", "linked_by": [ { "rule": "shared-words", "with": "63901e460b8fbba9", "words": [ "demand", "growth", "tsmc" ] }, { "rule": "shared-words", "with": "79bd3ba482daff2d", "words": [ "beat", "earn", "tsmc" ] } ], "members": [ "0a55943b50812b3d", "1d21ad3b85d15530", "210621f9ac5bb73c", "4e77036ce45202de", "63901e460b8fbba9", "79bd3ba482daff2d", "85f25bcb68531441", "9abd2667a185bf33", "c8e74cdda34787ec", "db7de00c48010628", "e07f6f4e87fe86b1", "e2fa9544501179ca", "e3e32d7d473c157c", "e5adffa73aaf9a9c", "f7fdca7cbe7c0131" ], "severity": 0, "source_count": 6, "sources": [ "arxiv-newest", "bloomberg-tech", "bluesky-ai", "gnews-ai", "techmeme", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-15T07:01:28.000Z", "score": 36.3, "source": "gnews-ai", "summary": "Benzinga, via Google News.", "title": "TSMC Beats Q3 Earnings and Revenue Estimates as AI Chip Demand Drives 41% Sales Growth", "url": "https://news.google.com/rss/articles/CBMi1AFBVV95cUxPOVFGQUFZTXJtTmYwTXFUN1cyR0FzSnNLTmNHMDBqZVlJbW8xRlQxU09jM3Q4dEIyejNyMk5IaXd3WkxXR0VHY24yTXpWYjZNamZVenAzT1E1VFF5WU5VMmpNNmNQaUw5NGJzSHp3NFlvdHhWc0F6blNwU2F5bkExaDY5MHgwMERJeGlmT2VDbmhSV1hzSzNWOTVQSi13UmFaRXJTeVVmbUptWVB6Q1Bhb3FSMDNpRHExQ3laLTFILVZXN1FpUFZCMUt0eGgwenFwOFhkdg?oc=5" }, { "entities": [ "OpenAI" ], "id": "bed6054bd5c867ff", "kind": "press", "meta": { "age_hours_at_score": 1.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:00:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T07:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:00:00.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "telegraph.co.uk", "pillar_reason": "source default", "publisher": "The Telegraph", "publisher_url": "https://www.telegraph.co.uk", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.5, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:00:00.000Z", "score": 41.3, "source": "wires-ai", "summary": "The Telegraph, via Google News.", "title": "OpenAI’s British staff handed £800,000 each", "url": "https://news.google.com/rss/articles/CBMikwFBVV95cUxOMVBybkE4OWVVZ2k2UUhNcE5NdVV3cTYzM2pJMG9ZX3V5cGlTd3lMR21kalVNbFE5X0JnVUh1SUE1R0FzNjA0RlVUOXltX3lHb0dRYlBGVW1ja0phOHgzdktnbU1NaWRUV3ZjTGxsb2dMU3pySFBzc3FxSk1FNkFfQkdiSHBwR08yWHJXa1VhaUc0QUE?oc=5" }, { "entities": [ "Anthropic", "SpaceX" ], "id": "f6626952d2d40949", "kind": "press", "meta": { "age_hours_at_score": 1.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:00:00.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:00:00.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic", "SpaceX" ], "models": [] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "fortune.com", "pillar_reason": "hardware/capital keyword", "publisher": "Fortune", "publisher_url": "https://fortune.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 19.5, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "compute", "published_at": "2026-10-15T07:00:00.000Z", "score": 39.3, "source": "gnews-ai", "summary": "Fortune, via Google News.", "title": "An early Anthropic backer predicts a post-IPO valuation of up to $2.4 trillion, topping SpaceX", "url": "https://news.google.com/rss/articles/CBMic0FVX3lxTE1jckgxTW1rV2xUZUZnTlk0ODZBa0pCQTZzLUFzb0pjeVlDNGY1QXlwZm5oNlpkM1hFejJaX3dyOUg3S0RXbVpNSG55TFNGbkNFbnBHclZLc2ZOcV9WaWh5YW9WTFJLdGZCSVRxUzcwZE5wcUU?oc=5" }, { "entities": [], "id": "18098b54dc9bc7e8", "kind": "press", "meta": { "age_hours_at_score": 1.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:00:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T07:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:00:00.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:39:37.691Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "afp.com", "pillar_reason": "source default", "publisher": "AFP.com", "publisher_url": "https://www.afp.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.5, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T07:00:00.000Z", "score": 38.3, "source": "wires-ai", "summary": "AFP.com, via Google News.", "title": "From AI to Autonomous Networks: Mobileum Spark Highlights the Next Frontier of Telecom Intelligence", "url": "https://news.google.com/rss/articles/CBMiugFBVV95cUxNWVpYZFNNWG9VWWJsbFVUaG5zaW54UFR1TVUyU0phaGl1NzR3YzNCdFBwX3BVNzhPT29sZW44NUlfelVzRVZuNlJUY0tVM04zLXlkVnYzSmRSTVVzb3pCbEJEejhsN2x3MFJWVXVKdW9ZQWxuNHVLelNBMTZRZ1BYRHVWbTA1QUtWbkU2V2FfRmZZQmtRV0d4TlVRckZ2MU1TLWZvVUtESWNBbjdiRUZWaDN1U3N2em8wbWc?oc=5" }, { "entities": [], "id": "a579dfc0d502af2e", "kind": "press", "meta": { "age_hours_at_score": 1.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T07:00:00.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T07:00:00.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:39:37.691Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "tradingview.com", "pillar_reason": "source default", "publisher": "TradingView", "publisher_url": "https://www.tradingview.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 5, "engagement": 0, "entities": 0, "recency": 19.5, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": { "id": "5411d919f500", "is_lead": true, "lead": "a579dfc0d502af2e", "linked_by": [ { "rule": "shared-words", "with": "5902ec903929cb2a", "words": [ "appl", "china", "iphon" ] } ], "members": [ "5902ec903929cb2a", "a579dfc0d502af2e" ], "severity": 0, "source_count": 2, "sources": [ "gnews-ai", "scmp-tech" ] } }, "pillar": "attention", "published_at": "2026-10-15T07:00:00.000Z", "score": 38.3, "source": "gnews-ai", "summary": "TradingView, via Google News.", "title": "Key facts: Apple $5T; Ternus/HomeView; China AI Dec; iPhone 18s Up", "url": "https://news.google.com/rss/articles/CBMiwAFBVV95cUxQelRKWXVlZFpDaDNBbU1EMmZLenRNNDQtZnBnb0FHUDR0NlBBcUJfdlRVaVRIYlZXekVCQjFPZnZjcFh6dHRINmpNbTIxM2Z0bHR6MEEzUkdUTmdVaksyZFVGeHpZRDhpMUpWSGhsRTVER3NXekFZQjI0QU9ZanBNamVldE5xU3Iyd2xhTlVkckdlaml1d3JmMHdsNlotVEtLSXVNNFJJX2Z1VkRmRll5Q0thN2JvbHVJcVZNakpaVk8?oc=5" }, { "entities": [], "id": "7395d8a41759c141", "kind": "press", "meta": { "age_hours_at_score": 1.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:58:38.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T06:58:38.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:58:38.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "ibtimes.co.uk", "pillar_reason": "source default", "publisher": "International Business Times UK", "publisher_url": "https://www.ibtimes.co.uk", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.5, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": { "id": "6e67bd05b171", "is_lead": true, "lead": "7395d8a41759c141", "linked_by": [ { "rule": "shared-words", "with": "5e4aaa8c75a8e41b", "words": [ "anti", "criminal", "hegseth", "iran", "pet", "protester", "war" ] }, { "rule": "shared-words", "with": "87f63a586123e782", "words": [ "criminal", "hegseth", "nazi", "pet", "war" ] } ], "members": [ "5e4aaa8c75a8e41b", "7395d8a41759c141", "87f63a586123e782" ], "severity": 0, "source_count": 1, "sources": [ "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-15T06:58:38.000Z", "score": 38.3, "source": "wires-ai", "summary": "International Business Times UK, via Google News.", "title": "US Secretary of War Pete Hegseth Branded 'War Criminal' and 'Nazi' as Six Anti-War Protesters Erupt Over Children Killed in Iran", "url": "https://news.google.com/rss/articles/CBMilwFBVV95cUxQR0x6dmlaVDRPbXdjSV82eEhMc2VHc0theVotbWRmWWxNYjE3NERPYUlDcWhKWlRiWUQyTzdTZHgxUVRPS25Va2p0QTlUczZsV0xjQUFKUWpLd3VyVGlWVHhKUk56ckZ5aHhubGF6QXh5ZGFIcDFaSThrWXpWMXNpY1dtX29nS1JHamh6bVVBcDZ4cVRLQlFR?oc=5" }, { "entities": [], "id": "968c5305dd53ff7c", "kind": "press", "meta": { "age_hours_at_score": 1.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:56:51.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-15T06:56:51.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:56:51.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-15T07:18:20.412Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 6, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.5, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "d6607eb5558d", "is_lead": true, "lead": "968c5305dd53ff7c", "linked_by": [ { "rule": "shared-words", "with": "3c2290fb034839df", "words": [ "gary", "marcu", "nyu" ] } ], "members": [ "3c2290fb034839df", "968c5305dd53ff7c" ], "severity": 0, "source_count": 1, "sources": [ "bloomberg-tech" ] } }, "pillar": "markets", "published_at": "2026-10-15T06:56:51.000Z", "score": 34.5, "source": "bloomberg-tech", "summary": "Gary Marcus, Emeritus Professor of Psychology & Neural Science, New York University discusses building trustworthy AI with Bloomberg’s David Merritt at the 2026 Bloomberg New Economy Forum in New Delhi. (Source: Bloomberg)", "title": "NYU’s Gary Marcus on Building AI We Can Trust", "url": "https://bloomberg.com/news/videos/2026-10-15/nyu-s-gary-marcus-on-building-ai-we-can-trust-video" }, { "entities": [ "CoreWeave" ], "id": "4e2d563ce35eb4fb", "kind": "press", "meta": { "age_hours_at_score": 1.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:56:41.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T06:56:41.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:56:41.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "CoreWeave" ], "models": [] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "moomoo.com", "pillar_reason": "source default", "publisher": "Moomoo", "publisher_url": "https://www.moomoo.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.5, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T06:56:41.000Z", "score": 36.3, "source": "gnews-ai", "summary": "Moomoo, via Google News.", "title": "Tracking Cathie Wood: Buying Heavily into Kodiak AI and CoreWeave, Cutting Personalis and Toast", "url": "https://news.google.com/rss/articles/CBMiugFBVV95cUxOaWJOVlJLSTZCN0lPY0Q1RXkwUThncGx4aXAyRVdQamJlSWNkdUpCbHEtYzRzckc3VW1aSk1kaU9jRUFKOF9nekI2NmN6ZFI0Q1FudHlWTWFybzA0MUU3aF85WFA5ZTdyTlJWSTIxMXFLWmVrOVZsb2szZ1Q1WGlPczIyc3NneEJqRi01OGVDMlFXN1lCTGFSS2swMHF5QWwtR0NTZUhnUi1WV0FiZTUtZFJ4TzZJc0JYMnc?oc=5" }, { "entities": [ "Anthropic", "TSMC" ], "id": "79bd3ba482daff2d", "kind": "press", "meta": { "age_hours_at_score": 1.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:55:37.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T06:55:37.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:55:37.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic", "TSMC" ], "models": [] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "bloomberg.com", "pillar_reason": "hardware/capital keyword", "publisher": "Bloomberg.com", "publisher_url": "https://www.bloomberg.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 19.5, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": { "id": "ce8b11d9deae", "is_lead": false, "lead": "c8e74cdda34787ec", "linked_by": [ { "rule": "shared-words", "with": "db7de00c48010628", "words": [ "beat", "earn", "tsmc" ] } ], "members": [ "0a55943b50812b3d", "1d21ad3b85d15530", "210621f9ac5bb73c", "4e77036ce45202de", "63901e460b8fbba9", "79bd3ba482daff2d", "85f25bcb68531441", "9abd2667a185bf33", "c8e74cdda34787ec", "db7de00c48010628", "e07f6f4e87fe86b1", "e2fa9544501179ca", "e3e32d7d473c157c", "e5adffa73aaf9a9c", "f7fdca7cbe7c0131" ], "severity": 0, "source_count": 6, "sources": [ "arxiv-newest", "bloomberg-tech", "bluesky-ai", "gnews-ai", "techmeme", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-15T06:55:37.000Z", "score": 44.3, "source": "wires-ai", "summary": "Bloomberg.com, via Google News.", "title": "Watch TSMC Earnings Beat; Anthropic Says It's Now Profitable", "url": "https://news.google.com/rss/articles/CBMiiAFBVV95cUxPMUxfSm96aDdVZDNwWlpHZXVrRzVVaXN1YzVKOTJBM3llaVRBc2s1RW1GMDlfQi13ejBHYjJGZ3FpN3N5ZEdSenljM1l3Y0lvVXgxNjE0clJxTTgwNVZVUmlsZWllMzJEcWt6QUowMFAwbG12cDBTQmRDQjdDdzhEVmdrbmpUTGdy?oc=5" }, { "entities": [ "Anthropic" ], "id": "4263068c477fb2e3", "kind": "press", "meta": { "age_hours_at_score": 1.5, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": "moneyweb.co.za", "published_at": "2026-10-15T08:00:01.000Z", "source": "gnews-ai", "title": "Anthropic results may aid IPO but not AI trade, Analysts say", "url": "https://news.google.com/rss/articles/CBMimwFBVV95cUxPa3JHWmNiaU03WGlILWNZSlhZTFd2MGl2X283cFktYzFDdDB3TnJjQUo5WlJ5UFdrNnRxajUyRk9RUWZXeW5mRFFnbTZLa3hZcmRFMk9lY2o0QTNxUFUxdElhZkdaNlVHM0p5aVE4cDQyMzVDOGtPalFwLWtaZ0otc1gxVVR2c085T2JZMkItcDB1anFBWFRaLTctbw?oc=5", "weight": 0.55 } ], "count": 2, "first_seen_published_at": "2026-10-15T06:55:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T08:00:01.000Z", "lead_minutes": 65, "primary_published_at": "2026-10-15T06:55:00.000Z", "sources": [ "gnews-ai", "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "first_seen_at": "2026-10-15T03:45:39.508Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "bloomberg.com", "pillar_reason": "hardware/capital keyword", "publisher": "Bloomberg.com", "publisher_url": "https://www.bloomberg.com", "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.4, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "compute", "published_at": "2026-10-15T06:55:00.000Z", "score": 53.7, "source": "wires-ai", "summary": "Bloomberg.com, via Google News.", "title": "Anthropic Results May Aid IPO But Not AI Trade, Analysts Say", "url": "https://news.google.com/rss/articles/CBMirwFBVV95cUxPSEcxNXE3QWZYcDNJMEtaQW9MWV9mSXlLcTQ2ZUZoSndtOGE0dEdsaWlrRjRXajZDTnlWRW1FaDNseFQ2WGJSUjFQeWRmUkN1UGp5dVBFVE0tUUtuMF9tdDJ5S1dEMEtkZzdTQ3d3azZTVWtnUzVmOGpmYzhZRVRvWWZzOWpZRm1XSWRmang5dzN4ZGdvZWstLUpEbnhpRnF1d2g2czhHVVUzOVFMNkpR?oc=5" }, { "entities": [ "Anthropic" ], "id": "7630f4534d386727", "kind": "press", "meta": { "age_hours_at_score": 1.5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:55:00.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T06:55:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:55:00.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "bloomberg.com", "pillar_reason": "source default", "publisher": "Bloomberg.com", "publisher_url": "https://www.bloomberg.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.4, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T06:55:00.000Z", "score": 36.2, "source": "gnews-ai", "summary": "Bloomberg.com, via Google News.", "title": "Anthropic Threatens the AI Trade Before the End of Humanity", "url": "https://news.google.com/rss/articles/CBMiswFBVV95cUxNZWxGOFlxVk9nRVcxUmhTcDZ2NXBGV2tUS3JaazJQOTNlWEpNdmxpQmZBaHNQbVo3T3c4TlJuZFdZelV4RTc1azZUVTdxMnVvQjJ5cGNwNnpIb2E1djJSc0s3ZTh0RHFiOUFmeEhtT1dPT0VZNnFlUjhTUHk4WGdpWVVjM1pPRmw0dUU3V0xUcFRYdXRtNU1kc0dVRmZHaUVmTnlIRmNjLXlYWXNYU1M0NnlSNA?oc=5" }, { "entities": [ "Anthropic", "OpenAI" ], "id": "2357b0b6b2f1086f", "kind": "press", "meta": { "age_hours_at_score": 1.5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:54:44.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T06:54:44.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:54:44.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic", "OpenAI" ], "models": [] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "incident": { "band": "A", "excluded": null, "severity": 1, "strong_signal": false, "terms": [ { "field": "title", "matched": "hacking", "term": "hacking", "tier": "a", "weight": 1 } ] }, "outlet_domain": "pasqualepillitteri.it", "pillar_reason": "source default", "publisher": "Pasquale Pillitteri", "publisher_url": "https://pasqualepillitteri.it", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 19.4, "severity": 15, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T06:54:44.000Z", "score": 54.2, "source": "gnews-ai", "summary": "Pasquale Pillitteri, via Google News.", "title": "EU Demands Answers From OpenAI and Anthropic Over Hacking AI Agents", "url": "https://news.google.com/rss/articles/CBMingFBVV95cUxQYXRVbVhmTjE0UU15SW1TVU02RlFkZEpJVG9QNUJicGMwUjBKaVVxQ2FqaGxGRTlJbFZGMEdzaEh6MHRzbmlqY0kwckN2V1h4WHM2UUZ2TWphWmY4NGtobE95cU9PQ0dqcWN1VTg5Z0R2Ynl0Q1VNSVhldXpKeEdHU0lDZkpBQWlhSnVhWWFUSEJjMlpTTVd6VTZ3czhLZw?oc=5" }, { "entities": [ "Llama" ], "id": "f9af4a4483ee7631", "kind": "press", "meta": { "age_hours_at_score": 1.5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:50:00.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T06:50:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:50:00.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Llama" ] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "neowin.net", "pillar_reason": "source default", "publisher": "Neowin", "publisher_url": "https://www.neowin.net", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.4, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T06:50:00.000Z", "score": 36.2, "source": "gnews-ai", "summary": "Neowin, via Google News.", "title": "Llama for Windows lets you download and run local AI models in a single click", "url": "https://news.google.com/rss/articles/CBMiqgFBVV95cUxOOUtVTnppTXJSLWZwcVVkT2l1RzVpY0x1UE96UGdoaUNuYkJZb1FaWFpUY1ZLeEFITndVcko4YUVvYnRUTkNjcFV6MjJlUVIxZGJldlVtcDdncFE1ektJNE9md3ZLekU3MEljMGs1UDhmZ1ZGeXRBNzZaZEt5b1U0YXVMVXZ6SUNQRGU1azNWdG8telkxNzYxR2tLUUlRLWphd1RpdVh1S1k4dw?oc=5" }, { "entities": [ "TSMC" ], "id": "e2fa9544501179ca", "kind": "press", "meta": { "age_hours_at_score": 1.6, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:49:00.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T06:49:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:49:00.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "TSMC" ], "models": [] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "calcalistech.com", "pillar_reason": "hardware/capital keyword", "publisher": "CTech", "publisher_url": "https://www.calcalistech.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.4, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": { "id": "ce8b11d9deae", "is_lead": false, "lead": "c8e74cdda34787ec", "linked_by": [ { "rule": "shared-words", "with": "63901e460b8fbba9", "words": [ "fuel", "growth", "profit", "record", "tsmc" ] } ], "members": [ "0a55943b50812b3d", "1d21ad3b85d15530", "210621f9ac5bb73c", "4e77036ce45202de", "63901e460b8fbba9", "79bd3ba482daff2d", "85f25bcb68531441", "9abd2667a185bf33", "c8e74cdda34787ec", "db7de00c48010628", "e07f6f4e87fe86b1", "e2fa9544501179ca", "e3e32d7d473c157c", "e5adffa73aaf9a9c", "f7fdca7cbe7c0131" ], "severity": 0, "source_count": 6, "sources": [ "arxiv-newest", "bloomberg-tech", "bluesky-ai", "gnews-ai", "techmeme", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-15T06:49:00.000Z", "score": 36.2, "source": "gnews-ai", "summary": "CTech, via Google News.", "title": "TSMC's AI-fueled growth continues with record $23.6 billion quarterly profit", "url": "https://news.google.com/rss/articles/CBMiZ0FVX3lxTE1uTzlPUnNkRXNyNUt6dThUOUdNSnh0Zy1LQkkyZy11bXRZTGE4dWxsVzNCQTZkSm1tcGV1S1VhOVY4TmJpc0RoWjdpa0dKamhKN3pwaTZZcW96RXZRSHhYQXBJci05X3c?oc=5" }, { "entities": [], "id": "a7a830d2cd2352c2", "kind": "press", "meta": { "age_hours_at_score": 1.6, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:48:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T06:48:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:48:00.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "bloomberg.com", "pillar_reason": "hardware/capital keyword", "publisher": "Bloomberg.com", "publisher_url": "https://www.bloomberg.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.4, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "compute", "published_at": "2026-10-15T06:48:00.000Z", "score": 38.2, "source": "wires-ai", "summary": "Bloomberg.com, via Google News.", "title": "Global Chip Selloff Deepens as Investors Retreat from AI", "url": "https://news.google.com/rss/articles/CBMirwFBVV95cUxPMUlJWTZNNjM3R3M2UHZSWDBVRzJKTDlmWDIxY3NlRkZqSkJERGFOQmk1cjBPWnBqald5RktiS240a2Z5bTN0Z0UxQV9meldKS3lVMWhIOEhQVmswc2RnZTJuSGIxM3AySndtajhLWGVtdlpOMnlqYVViZ0F1clVTbEZTVmdOdDhlUDZjaUlDQmlRWHEwOUdGd3phak5pcERTV2ZaUkQyNXoyVU9fUElv?oc=5" }, { "entities": [ "TSMC" ], "id": "e3e32d7d473c157c", "kind": "press", "meta": { "age_hours_at_score": 1.6, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:46:19.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T06:46:19.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:46:19.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "TSMC" ], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "ft.com", "pillar_reason": "hardware/capital keyword", "publisher": "Financial Times", "publisher_url": "https://www.ft.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.4, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": { "id": "ce8b11d9deae", "is_lead": false, "lead": "c8e74cdda34787ec", "linked_by": [ { "rule": "shared-words", "with": "63901e460b8fbba9", "words": [ "demand", "profit", "tsmc" ] } ], "members": [ "0a55943b50812b3d", "1d21ad3b85d15530", "210621f9ac5bb73c", "4e77036ce45202de", "63901e460b8fbba9", "79bd3ba482daff2d", "85f25bcb68531441", "9abd2667a185bf33", "c8e74cdda34787ec", "db7de00c48010628", "e07f6f4e87fe86b1", "e2fa9544501179ca", "e3e32d7d473c157c", "e5adffa73aaf9a9c", "f7fdca7cbe7c0131" ], "severity": 0, "source_count": 6, "sources": [ "arxiv-newest", "bloomberg-tech", "bluesky-ai", "gnews-ai", "techmeme", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-15T06:46:19.000Z", "score": 41.2, "source": "wires-ai", "summary": "Financial Times, via Google News.", "title": "Chipmaker TSMC profits surge amid relentless AI demand", "url": "https://news.google.com/rss/articles/CBMihAFBVV95cUxPS3dVb21CbHNmNVNOLUI1aXJGRGoyRWtsSEs0bDMwbi13YkVYMUVacDRtZXRnYXowSXhTLV9lN3hZeFgzUUI0UVRjNUhjTXJxbTVjMHZLV1NRUjhuQ1VjdmdpUkxqMWUwU3FwNzBjZ2x3XzZReVhXekdiQkJDX1p4cEY2ZnU?oc=5" }, { "entities": [], "id": "1145923713800827", "kind": "forum", "meta": { "age_hours_at_score": 1.6, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:45:46.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-15T06:45:46.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:45:46.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T07:39:37.691Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.4, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-15T06:45:46.000Z", "score": 28.2, "source": "reddit-artificial", "summary": "I understand why people use AI companions. They are available, patient, and less intimidating than starting a difficult conversation with another person. That can be helpful, especially for people who are isolated or anxious. But I also wonder whether an always agreeable companion could make real relationships feel…", "title": "AI companions may reduce loneliness and increase isolation at the same time", "url": "https://reddit.com/r/artificial/comments/1x6el0b/ai_companions_may_reduce_loneliness_and_increase" }, { "entities": [ "CoreWeave" ], "id": "664dba066a24bd74", "kind": "press", "meta": { "age_hours_at_score": 1.6, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:43:37.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T06:43:37.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:43:37.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "CoreWeave" ], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "marketscreener.com", "pillar_reason": "source default", "publisher": "marketscreener.com", "publisher_url": "https://www.marketscreener.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 5, "engagement": 0, "entities": 3, "recency": 19.4, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": { "id": "35e203f22459", "is_lead": true, "lead": "664dba066a24bd74", "linked_by": [ { "rule": "shared-words", "with": "d1302db5389775a6", "words": [ "bond", "concern", "coreweav", "stock", "yield" ] } ], "members": [ "664dba066a24bd74", "d1302db5389775a6" ], "severity": 0, "source_count": 2, "sources": [ "gnews-ai", "techmeme" ] } }, "pillar": "attention", "published_at": "2026-10-15T06:43:37.000Z", "score": 41.2, "source": "gnews-ai", "summary": "marketscreener.com, via Google News.", "title": "CoreWeave stock tumbles as bond yields approach 15% amid concerns over AI debt", "url": "https://news.google.com/rss/articles/CBMi8AFBVV95cUxPeFR3bFZFTU5HbXVpZDh2ZXBDaWJZaHlPR2I0dlVvTzNfbTBxSmtLajVzRHJDaWpOTWxqazZnZzNMWXJDa2JTcWNpaUdMRWJKV3NEejB6WWwzZjd2OHZ5WmlTYTZFLU1HSlZ0emhDM2RpbkEzZ2djWHhXanBMakw1LWdEbG83ekJxX2hFVzZrdGVEZDNya3VhWS1Yd3h4LUV5N0JoOUhIekE5ZWg3YXUyTVlrWVg1akVOOExReUhKRjFaZ0xJcGNSV1JFY05jOXluQU5XUnh3Q3BOaVhyWXA1dkNRV2hwYkcyLUFTYjNOUF8?oc=5" }, { "entities": [], "id": "5edbd41c27f8245f", "kind": "press", "meta": { "age_hours_at_score": 1.7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:42:42.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T06:42:42.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:42:42.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "36kr.com", "pillar_reason": "source default", "publisher": "36 Kr", "publisher_url": "https://eu.36kr.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.4, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T06:42:42.000Z", "score": 33.2, "source": "gnews-ai", "summary": "36 Kr, via Google News.", "title": "AI Trading Growth Rate: Why It’s Falling Short of Market Expectations", "url": "https://news.google.com/rss/articles/CBMiU0FVX3lxTFBMaGl5bGpLSkRpREhheWRGOWtZRFgzUzBkVXo1SFRDX0ItUHNvM0VHWjBtLWYzWW52YmpwQ1hDSnpRZ1JjMnZ1eEQ1bU5xZTdJVzln?oc=5" }, { "entities": [ "DeepSeek", "DeepSeek-R" ], "id": "6f4a29201ca13cba", "kind": "press", "meta": { "age_hours_at_score": 1.7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:42:33.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T06:42:33.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:42:33.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "DeepSeek" ], "models": [ "DeepSeek-R" ] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "36kr.com", "pillar_reason": "source default", "publisher": "36 Kr", "publisher_url": "https://eu.36kr.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 19.4, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T06:42:33.000Z", "score": 39.2, "source": "gnews-ai", "summary": "36 Kr, via Google News.", "title": "DeepSeek servers suffer global outage, mysterious Harness accounts emerge simultaneously", "url": "https://news.google.com/rss/articles/CBMiU0FVX3lxTE9KVHVoRGF3VUtBalJIOEJCSnlhRmM3b0M1alpZb0h5OGZRLVZaZ0MwczRwOGdfVTVxbDctRFdRdEZfRVFpNk9QOWpGRUVNVHA1ZUkw?oc=5" }, { "entities": [ "Claude" ], "id": "4beb41f25b1fc84b", "kind": "press", "meta": { "age_hours_at_score": 1.7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:42:32.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T06:42:32.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:42:32.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Claude" ] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "36kr.com", "pillar_reason": "source default", "publisher": "36 Kr", "publisher_url": "https://eu.36kr.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.4, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": { "id": "5d82c5f4c985", "is_lead": false, "lead": "15107a0d279b3003", "linked_by": [ { "rule": "shared-words", "with": "15107a0d279b3003", "words": [ "claud", "cod", "desktop" ] } ], "members": [ "15107a0d279b3003", "4beb41f25b1fc84b", "9cfef81b690b3ab2" ], "severity": 1, "source_count": 2, "sources": [ "gnews-ai", "hn-fresh-ai" ] } }, "pillar": "attention", "published_at": "2026-10-15T06:42:32.000Z", "score": 36.2, "source": "gnews-ai", "summary": "36 Kr, via Google News.", "title": "Radical Overhaul of Claude Code Desktop: One Person Commands an AI Legion to Crank Out Code at Blazing Speed", "url": "https://news.google.com/rss/articles/CBMiU0FVX3lxTE0xNm9pTkIyd0VpTm95Y09fMndWQXYxS0Q4VVQ5dkplTDhOR2hnTjB5NzhZa1N1cVNqZDJVb2FWdkhka3l5Y0tlWnhpVGxQYUdqWTdZ?oc=5" }, { "entities": [ "Meta AI" ], "id": "ef2df265fbde97d1", "kind": "press", "meta": { "age_hours_at_score": 1.7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:42:29.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T06:42:29.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:42:29.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Meta AI" ], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "acrofan.com", "pillar_reason": "source default", "publisher": "us.acrofan.com", "publisher_url": "https://us.acrofan.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.4, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T06:42:29.000Z", "score": 36.2, "source": "gnews-ai", "summary": "us.acrofan.com, via Google News.", "title": "HQVT Debuts Multispectral AI Technology at Hong Kong Electronics Fair, Expanding Its Global Reach", "url": "https://news.google.com/rss/articles/CBMiXEFVX3lxTE5EUDdTTFA3am5xQlVIRFNfMmEtYzZ1bjFKa1VrTTlKcERJZjQyMVI4UGhPVG9xcUcyU3ZmUHAxMkwyNUNOcWZjaU5idzlXajhYR2t1NU1JVTZFb2J5?oc=5" }, { "entities": [ "Claude" ], "id": "9cfef81b690b3ab2", "kind": "press", "meta": { "age_hours_at_score": 1.7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:42:09.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T06:42:09.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:42:09.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Claude" ] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "incident": { "band": "B", "excluded": null, "severity": 0.6, "strong_signal": false, "terms": [ { "field": "title", "matched": "leak", "term": "leak", "tier": "b", "weight": 0.6 } ] }, "outlet_domain": "36kr.com", "pillar_reason": "source default", "publisher": "36 Kr", "publisher_url": "https://eu.36kr.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.4, "severity": 9, "source_weight": 13.8 }, "source_weight": 0.55, "story": { "id": "5d82c5f4c985", "is_lead": false, "lead": "15107a0d279b3003", "linked_by": [ { "rule": "incident-pair", "with": "15107a0d279b3003", "words": [ "claud", "cod" ] } ], "members": [ "15107a0d279b3003", "4beb41f25b1fc84b", "9cfef81b690b3ab2" ], "severity": 1, "source_count": 2, "sources": [ "gnews-ai", "hn-fresh-ai" ] } }, "pillar": "attention", "published_at": "2026-10-15T06:42:09.000Z", "score": 45.2, "source": "gnews-ai", "summary": "36 Kr, via Google News.", "title": "Prophets Reveal Company A's Research Blueprint Leak: Claude Code Traces Back 5 Years", "url": "https://news.google.com/rss/articles/CBMiU0FVX3lxTE1EUGhxYmt0THQxYkZ3RnpWczhhV09xZDdWR05aTGNuZURtQ0VpdFljNHUxMTBoT0FfeVRBODR4V3NDR0poMWlZVFVnRDhMYmdxbTNR?oc=5" }, { "entities": [], "id": "c79fb3be1704c7be", "kind": "press", "meta": { "age_hours_at_score": 1.7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:42:09.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T06:42:09.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:42:09.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "36kr.com", "pillar_reason": "source default", "publisher": "36 Kr", "publisher_url": "https://eu.36kr.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.4, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T06:42:09.000Z", "score": 33.2, "source": "gnews-ai", "summary": "36 Kr, via Google News.", "title": "Elon Musk Reveals Six Episodes of \"AI Psychosis\" He Experienced Over the Past Year", "url": "https://news.google.com/rss/articles/CBMiU0FVX3lxTE4xWl9tZEZQZHRuc2lRR1U3WXc0cW5NY1pYYzl6TXVoYTJyaV9iYU43dEY4REJ3Tk1kUFE0bTJtQUpWU1BkV0sxYmNYcWN0TU1rcmYw?oc=5" }, { "entities": [], "id": "e5254f68fb56aa40", "kind": "press", "meta": { "age_hours_at_score": 1.7, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": "independent.co.uk", "published_at": "2026-10-15T06:40:00.000Z", "source": "gnews-ai", "title": "AI chatbots spread false information, major new report warns", "url": "https://news.google.com/rss/articles/CBMinAFBVV95cUxQSzVXeDlfSXNyQk5OaWxEeEFGQmptdEtjNUxhTkF4Y2VGYzNmREk0X1FfeWpoOEMtZWNITjE4ZzFybmprdFFzN0Jjb2ZTZjNaNi1YSTRhYUhhTkg1cHVqZVkzcEhlOUlMdE9wLXU5MVNCTUFpdUhRRkdERXVIcHlocTFaVWFfcW9aVURSakNoY0wtSXdXLTRlZXFuVUQ?oc=5", "weight": 0.55 } ], "count": 1, "first_seen_published_at": "2026-10-15T06:40:00.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T06:40:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:40:00.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "independent.co.uk", "pillar_reason": "source default", "publisher": "The Independent", "publisher_url": "https://www.independent.co.uk", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.4, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T06:40:00.000Z", "score": 33.2, "source": "gnews-ai", "summary": "The Independent, via Google News.", "title": "AI chatbots spread false information, major new report warns", "url": "https://news.google.com/rss/articles/CBMilgFBVV95cUxOa05rLVk0UXhXZ0lYY1o2MW5KZ09JSFJYb1R1OG85Wl9fMWZlVElNM0hRbWxmT0xVcnR5RTdGbk5OdFhUUWlqM2dQYkZuNTdMUlNJZnY1YmsyYi1UZTJ2LUh5NURNVWJpYUtudzJQYUFXazNQZ3BtWHJSV3pudnBHVm9qX2pjT1YwcDFFRER3Q1dBUmZrYXc?oc=5" }, { "entities": [ "DeepSeek", "DeepSeek-R" ], "id": "2cb77d9f8bd2e024", "kind": "press", "meta": { "age_hours_at_score": 1.7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:38:40.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T06:38:40.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:38:40.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "DeepSeek" ], "models": [ "DeepSeek-R" ] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "technode.com", "pillar_reason": "source default", "publisher": "TechNode", "publisher_url": "https://technode.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 19.3, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-15T06:38:40.000Z", "score": 39.1, "source": "gnews-ai", "summary": "TechNode, via Google News.", "title": "DeepSeek implements fix after partial outage affects chat and API services", "url": "https://news.google.com/rss/articles/CBMiqwFBVV95cUxQMHllN2szNUxzVmtYRlBiM0hTX0poenBjUkRVdEUzeUpXQkpfZHVRNWlJZTROVzNPRW1uZktyM1V4U29wbTdWVnZkdWZoYTB5cUc2aTBGUVBwN01VNU9PaXRxVGdMT3RqcHRBY2QwNElMWlJ5V0VuMlRuMVhqT2RBSDZ2R0JuYXZrQ3prbjNYaTh2NnQzQzBHYkJmN0wxS3drRHRFcy00VkFqeHc?oc=5" }, { "entities": [ "ByteDance" ], "id": "429bd414c3da3733", "kind": "press", "meta": { "age_hours_at_score": 1.7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:38:40.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T06:38:40.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:38:40.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "ByteDance" ], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "technode.com", "pillar_reason": "hardware/capital keyword", "publisher": "TechNode", "publisher_url": "https://technode.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.3, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": { "id": "d79fb1f90367", "is_lead": false, "lead": "ca3bf36fb5385aa7", "linked_by": [ { "rule": "shared-words", "with": "8f7aea038b16439c", "words": [ "buy", "bytedanc", "center", "mediatek" ] }, { "rule": "shared-words", "with": "ca3bf36fb5385aa7", "words": [ "bytedanc", "center", "mediatek" ] } ], "members": [ "429bd414c3da3733", "8f7aea038b16439c", "ca3bf36fb5385aa7" ], "severity": 0, "source_count": 4, "sources": [ "bloomberg-tech", "gnews-ai", "techmeme", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-15T06:38:40.000Z", "score": 36.1, "source": "gnews-ai", "summary": "TechNode, via Google News.", "title": "ByteDance nears deal to buy MediaTek’s custom AI chips for data centers", "url": "https://news.google.com/rss/articles/CBMipgFBVV95cUxNWjc4R3ZQMmx6QUlKNkxhNWhlY1pXYWtxMDFiRHB6dVp0YW5OWS12aTFDYzEtTmpmUXBoODB5UUxYZ3VwU0pXOUtBZE9tX3F6bF96NndDMk1OZG9kNEk4NHA2NGswS3NBX2FWbk51SWphUFNpVE5nUzF0bHY2ZjM0c3ExUWdRWlZJQmU3M2VUc01IY19oMGdwbkFWbnIwQllWckNzLU93?oc=5" }, { "entities": [], "id": "23979bd8e7016953", "kind": "press", "meta": { "age_hours_at_score": 1.7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:37:34.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T06:37:34.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:37:34.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:39:37.691Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "997wpro.com", "pillar_reason": "source default", "publisher": "WPRO", "publisher_url": "https://www.997wpro.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.3, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": { "id": "dd450c592c1b", "is_lead": true, "lead": "23979bd8e7016953", "linked_by": [ { "rule": "shared-words", "with": "5261a04adb95ef8c", "words": [ "montana", "rang", "trump" ] } ], "members": [ "23979bd8e7016953", "5261a04adb95ef8c" ], "severity": 0, "source_count": 1, "sources": [ "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-15T06:37:34.000Z", "score": 38.1, "source": "wires-ai", "summary": "WPRO, via Google News.", "title": "Trump Announces Plans for New Military “Super Range” in Montana", "url": "https://news.google.com/rss/articles/CBMingFBVV95cUxQdDc1U25Sdng3M1V4cXhHN3lCaDZaMmRlZTRyR3dhVUxmM05sVGpUREZFM3FBQkJVU2kyQnk0Q0Z2NmxRS0p2U1Ftai1BaV8yRDQ3cDFNeThUaTNWWHZpYkhaWkstRHV2ZGdNQ1NUdUVjTU5nSDlGT1pvNzFYWFVZdWplX0FFb3VyR1owNktiN0RHOXM4WlIybGt5ZERDUQ?oc=5" }, { "entities": [ "GPT", "OpenAI" ], "id": "c61a164910b3f7ac", "kind": "press", "meta": { "age_hours_at_score": 1.7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:37:20.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T06:37:20.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:37:20.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [ "GPT" ] }, "first_seen_at": "2026-10-15T08:19:19.153Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "wsj.com", "pillar_reason": "source default", "publisher": "WSJ", "publisher_url": "https://www.wsj.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 19.3, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T06:37:20.000Z", "score": 44.1, "source": "wires-ai", "summary": "WSJ, via Google News.", "title": "OpenAI Makes Progress in Preventing AI-Driven ChatGPT Delusions", "url": "https://news.google.com/rss/articles/CBMiekFVX3lxTE1xQ0lVRVpfMENMYlZ2Ulg5LW0tTnNFQmxPZXVqdlQ5UEpGbVlKd2lvRS1GaXJqLTZGMkhteW1HRWl1XzBSdEZqS2tsYUFNa0c1ajlHYklwcHNtZ0ZVV0ZzYW85aHVCdVdtdmJfblJyRnJmUTZscXk3MVFR?oc=5" }, { "entities": [ "Palantir" ], "id": "126afe56b7abc21c", "kind": "forum", "meta": { "age_hours_at_score": 1.8, "comments": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:35:28.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-15T06:35:28.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:35:28.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50100028", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 12 }, "entity_kinds": { "labs": [ "Palantir" ], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "hn_id": "50100028", "incident": { "band": "C", "excluded": null, "severity": 0.3, "strong_signal": false, "terms": [ { "field": "title", "matched": "investigation", "term": "investigation", "tier": "c", "weight": 0.3 } ] }, "outlet_domain": "substack.com", "pillar_reason": "source default", "points": 12, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 7.4, "entities": 3, "recency": 19.3, "severity": 4.5, "source_weight": 11.3 }, "source_weight": 0.45, "story": null, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-15T06:35:28.000Z", "score": 45.5, "source": "hn-fresh-ai", "summary": "New on Hacker News: 12 points, 0 comments so far.", "title": "Investigation: EU citizens' private messages are run through Palantir's software", "url": "https://arvidgrange.substack.com/p/investigation-eu-citizens-private" }, { "entities": [], "id": "2aedfa1e8abae253", "kind": "press", "meta": { "age_hours_at_score": 1.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:35:01.000Z", "first_source": "techmeme", "last_published_at": "2026-10-15T06:35:01.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:35:01.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.3, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": null }, "pillar": "attention", "published_at": "2026-10-15T06:35:01.000Z", "score": 36.8, "source": "techmeme", "summary": "New York Times : Q&A with MIT sociologist Sherry Turkle, who has been studying relationships between humans and machines since the 1970s, on her new book, AI chatbots, and more — This is an edited transcript of “The Ezra Klein Show.” You can listen to the episode wherever you get your podcasts.", "title": "Q&A with MIT sociologist Sherry Turkle, who has been studying relationships between humans and machines since the 1970s, on her new book, AI chatbots, and more (New York Times)", "url": "https://techmeme.com/261015/p10" }, { "entities": [], "id": "e50c7e7da973aae2", "kind": "press", "meta": { "age_hours_at_score": 1.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:35:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T06:35:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:35:00.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T08:10:19.630Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "telegraph.co.uk", "pillar_reason": "source default", "publisher": "The Telegraph", "publisher_url": "https://www.telegraph.co.uk", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.3, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": { "id": "aa7cf63c3377", "is_lead": false, "lead": "238f5cf7d450448d", "linked_by": [ { "rule": "shared-words", "with": "238f5cf7d450448d", "words": [ "asian", "market", "sell" ] }, { "rule": "shared-words", "with": "798c1644ec60a7eb", "words": [ "asian", "deepen", "market", "sell" ] } ], "members": [ "238f5cf7d450448d", "798c1644ec60a7eb", "e50c7e7da973aae2" ], "severity": 0, "source_count": 2, "sources": [ "bluesky-ai", "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-15T06:35:00.000Z", "score": 38.1, "source": "wires-ai", "summary": "The Telegraph, via Google News.", "title": "Asian markets plunge as AI fears deepen tech sell-off", "url": "https://news.google.com/rss/articles/CBMikgFBVV95cUxOeWpBeXloeVpxaHE0eDY4OThEb051Yjh6Si00TnpaU19LY1kwcDNEVGRZclgtbGdpQ2hEWVZDS3l2aTBCaUE3SUFwamZOb0Jldk9qcm05dk1KRm9pUXhrbWp4QXY2MHhITHFqbk85RW44Q3MteXR5cDNFcGhDVkpEMDE0S2JHb1lycVZDNWZVallMQQ?oc=5" }, { "entities": [ "Amazon" ], "id": "80d677398742f886", "kind": "press", "meta": { "age_hours_at_score": 1.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:35:00.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T06:35:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:35:00.000Z", "sources": [ "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Amazon" ], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "biggo.com", "pillar_reason": "source default", "publisher": "BigGo Finance", "publisher_url": "https://finance.biggo.com", 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"2026-10-15T06:32:01.000Z", "sources": [ "lesswrong" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.lesswrong.com/feed.xml", "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 2, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.3, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "governance", "published_at": "2026-10-15T06:32:01.000Z", "score": 31.8, "source": "lesswrong", "summary": "A guide to unintended consequences of AI safety interventions and how you can mitigate them. Introduction My cofounder and I are launching Impartial Projects , a studio for AI safety field strategy. We hope to start the projects most urgently needed in the AI safety ecosystem (which we understand to include AI…", "title": "Robustness: Backfire Risks in AI Safety and Governance", "url": "https://lesswrong.com/posts/g73ZwuzChySQCxjjv/robustness-backfire-risks-in-ai-safety-and-governance" }, { "entities": [ "Claude" ], "id": "ff434dea3f5949f5", "kind": "forum", "meta": { "age_hours_at_score": 1.8, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:31:51.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-15T06:31:51.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:31:51.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Claude" ] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T07:39:37.691Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.3, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-15T06:31:51.000Z", "score": 31.1, "source": "reddit-artificial", "summary": "What I need to do is to search through photographed and scanned archive documents, that I download in the Ukrainian and Polish languages for certain names/surnames/birthdates/village names. Often times these are very difficult to read due to the file quality and writing style due to the ages. I have used Claude which…", "title": "Photographed Text", "url": "https://reddit.com/r/artificial/comments/1x6edaf/photographed_text" }, { "entities": [], "id": "ea14912f1eabd33b", "kind": "press", "meta": { "age_hours_at_score": 1.9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:30:01.000Z", "first_source": "techmeme", "last_published_at": "2026-10-15T06:30:01.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:30:01.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 5, "engagement": 0, "entities": 0, "recency": 19.3, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": { "id": "612f54dc2cb9", "is_lead": true, "lead": "ea14912f1eabd33b", "linked_by": [ { "rule": "shared-words", "with": "a31e351486a933e6", "words": [ "acoustic", "celin", "dion", "liv" ] } ], "members": [ "a31e351486a933e6", "ea14912f1eabd33b" ], "severity": 0, "source_count": 2, "sources": [ "bloomberg-tech", "techmeme" ] } }, "pillar": "attention", "published_at": "2026-10-15T06:30:01.000Z", "score": 41.8, "source": "techmeme", "summary": "Benoit Berthelot / Bloomberg : A look at L-Acoustics, whose audio platform uses machine learning to help artists like Céline Dion isolate their voices and cut through noise at live concerts — New software from L-Acoustics helps isolate a singer's voice to cut through concert noise", "title": "A look at L-Acoustics, whose audio platform uses machine learning to help artists like Céline Dion isolate their voices and cut through noise at live concerts (Benoit Berthelot/Bloomberg)", "url": "https://techmeme.com/261015/p9" }, { "entities": [ "Tencent" ], "id": "4e71c69390cd1baf", "kind": "forum", "meta": { "age_hours_at_score": 1.9, "comments": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:28:19.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-15T06:28:19.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:28:19.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50099975", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 3 }, "entity_kinds": { "labs": [ "Tencent" ], "models": [] }, "first_seen_at": "2026-10-15T07:39:37.691Z", "hn_id": "50099975", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "swarmcha.se", "pillar_reason": "source default", "points": 3, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 4, "entities": 3, "recency": 19.3, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-15T06:28:19.000Z", "score": 37.6, "source": "hn-fresh-ai", "summary": "New on Hacker News: 3 points, 0 comments so far.", "title": "We found a Tencent agent fleet", "url": "https://swarmcha.se/posts/chinese-agent-fleet-full-report" }, { "entities": [], "id": "3885e81981808b8d", "kind": "press", "meta": { "age_hours_at_score": 2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:21:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T06:21:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:21:00.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "reuters.com", "pillar_reason": "source default", "publisher": "Reuters", "publisher_url": "https://www.reuters.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.2, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T06:21:00.000Z", "score": 38, "source": "wires-ai", "summary": "Reuters, via Google News.", "title": "Governments globally to turn to non-dollar bonds amid AI-driven liquidity crunch, Citi banker says", "url": "https://news.google.com/rss/articles/CBMixgFBVV95cUxPbURrVzdKbi1YZzdaMUh1Si0tYm5qTGlWNDlDNUR0SnY5ejB3MmlYYi1MUXE0RzBuS0pyTDZtODNzU3JPeDU5Qy1md083MW5BVGpNODFxVk1Rd2dCbDA2bHdHMnd2ZFE4RXpPU1l5Ql9sZW1ibHZkcGhxeXhjQmFWQnBOb01KZ0w5dDNPZlZlbGoyODM4dGZ6NEVDTzhFcmRwdVNCenA3emozdS10Q2ZGaDlBSkh3emJyNzZqQjhFQ0E2SnI5WHc?oc=5" }, { "entities": [ "TSMC" ], "id": "3f82d0d1e6da5f9e", "kind": "press", "meta": { "age_hours_at_score": 2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:20:35.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-15T06:20:35.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:20:35.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "TSMC" ], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "hardware/capital keyword", "rejected_off_topic": 6, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.2, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "compute", "published_at": "2026-10-15T06:20:35.000Z", "score": 37.2, "source": "bloomberg-tech", "summary": "Taiwan Semiconductor Manufacturing Co. delivered a better-than-expected revenue outlook, sustaining a torrid pace of growth yet failing to dispel rising concerns about the sustainability of a global AI buildout.", "title": "TSMC’s Upbeat Outlook Fails to Quash AI Buildout Concerns", "url": "https://bloomberg.com/news/articles/2026-10-15/tsmc-s-profit-surpasses-expectations-after-ai-orders-rush-in" }, { "entities": [ "TSMC" ], "id": "210621f9ac5bb73c", "kind": "press", "meta": { "age_hours_at_score": 2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:20:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T06:20:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:20:00.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "TSMC" ], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "bloomberg.com", "pillar_reason": "hardware/capital keyword", "publisher": "Bloomberg.com", "publisher_url": "https://www.bloomberg.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.2, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": { "id": "ce8b11d9deae", "is_lead": false, "lead": "c8e74cdda34787ec", "linked_by": [ { "rule": "shared-words", "with": "9abd2667a185bf33", "words": [ "expectat", "profit", "tsmc" ] }, { "rule": "shared-words", "with": "e07f6f4e87fe86b1", "words": [ "expectat", "profit", "tsmc" ] } ], "members": [ "0a55943b50812b3d", "1d21ad3b85d15530", "210621f9ac5bb73c", "4e77036ce45202de", "63901e460b8fbba9", "79bd3ba482daff2d", "85f25bcb68531441", "9abd2667a185bf33", "c8e74cdda34787ec", "db7de00c48010628", "e07f6f4e87fe86b1", "e2fa9544501179ca", "e3e32d7d473c157c", "e5adffa73aaf9a9c", "f7fdca7cbe7c0131" ], "severity": 0, "source_count": 6, "sources": [ "arxiv-newest", "bloomberg-tech", "bluesky-ai", "gnews-ai", "techmeme", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-15T06:20:00.000Z", "score": 41, "source": "wires-ai", "summary": "Bloomberg.com, via Google News.", "title": "TSMC’s Profit Surpasses Expectations After AI Orders Rush In", "url": "https://news.google.com/rss/articles/CBMisAFBVV95cUxQNnZQMElNRlc2aGFEd21EQTBjRS1fLTFZR0hHZG9MTzJTMFBsR25pMDhCSHRmcDRhR3JkQWtYOV91cjlVd3BEcEtlVUNPQ21yRVNRRXJ4MFdXZldkdjk3OWJHLU9JcFBSYUx5Q1FjMlVTMlhqaUlKamFnX2VIVVNMZmxMTENDZ2YtbnVxanpfRnhKUzY5RnFkcHVnVm1uMVZyTy15OWt3ZmdTc1F4Mk4yaw?oc=5" }, { "entities": [], "id": "952eda13d1ff64de", "kind": "forum", "meta": { "age_hours_at_score": 2.1, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:15:15.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-15T06:15:15.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:15:15.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T07:39:37.691Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.2, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-15T06:15:15.000Z", "score": 28, "source": "reddit-artificial", "summary": "Most jobs are not just the execution of already well-defined tasks. They also involve deciding clearly what needs to be done, whether it's been done well enough and completely enough, and if not, what needs to be done next to address this. Having machines to execute already well-defined tasks more cheaply, quickly,…", "title": "AI and Human Labour", "url": "https://reddit.com/r/artificial/comments/1x6e3qd/ai_and_human_labour" }, { "entities": [ "ASML" ], "id": "181f363fdd7c62de", "kind": "forum", "meta": { "age_hours_at_score": 2.2, "bsky_handle": "technologyreview.com", "corroboration": { "also": [ { "dedup_key": null, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 3 }, "kind": "forum", "outlet": "technologyreview.com", "published_at": "2026-10-15T06:44:51.839Z", "source": "bluesky-ai", "title": "The $400 million machine powering the future of chipmaking", "url": "https://bsky.app/profile/technologyreview.com/post/3mxvhlbw6bk2x", "weight": 0.6 } ], "count": 1, "first_seen_published_at": "2026-10-15T06:11:01.154Z", "first_source": "bluesky-ai", "last_published_at": "2026-10-15T06:44:51.839Z", "lead_minutes": 34, "primary_published_at": "2026-10-15T06:11:01.154Z", "sources": [ "bluesky-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 3 }, "entity_kinds": { "labs": [ "ASML" ], "models": [] }, "external_url": "https://trib.al/7OYay9F", "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 2, "mode": "author-feeds", "outlet_domain": "technologyreview.com", "pillar_reason": "hardware/capital keyword", "reposts": 0, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 3.1, "entities": 3, "recency": 19.2, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "compute", "published_at": "2026-10-15T06:11:01.154Z", "score": 40.3, "source": "bluesky-ai", "summary": "Posted on Bluesky by @technologyreview.com: The AI era needs ever faster chips. ASML has a monopoly on the expensive contraptions needed to pattern them. Can anyone catch up?", "title": "The $400 million machine powering the future of chipmaking", "url": "https://bsky.app/profile/technologyreview.com/post/3mxvforc5kc2o" }, { "entities": [], "id": "798c1644ec60a7eb", "kind": "press", "meta": { "age_hours_at_score": 2.2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:10:46.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T06:10:46.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:10:46.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "ft.com", "pillar_reason": "source default", "publisher": "Financial Times", "publisher_url": "https://www.ft.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.2, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": { "id": "aa7cf63c3377", "is_lead": false, "lead": "238f5cf7d450448d", "linked_by": [ { "rule": "shared-words", "with": "e50c7e7da973aae2", "words": [ "asian", "deepen", "market", "sell" ] } ], "members": [ "238f5cf7d450448d", "798c1644ec60a7eb", "e50c7e7da973aae2" ], "severity": 0, "source_count": 2, "sources": [ "bluesky-ai", "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-15T06:10:46.000Z", "score": 38, "source": "wires-ai", "summary": "Financial Times, via Google News.", "title": "Global AI sell-off deepens as Asian markets tumble", "url": "https://news.google.com/rss/articles/CBMihAFBVV95cUxPYUozTHM0dkZqb01wNmhUc2twRnYzMk8yOTV5V2YwSW5ndGRjN0lwNDdNUGFWbHRrU0kwWEp4OGJUNXp4djlDQnE4eXNDdy1BVk1WTUNMV1RCUWtGN3FOcmx0NHFRRkRIY3IzYzEwandjV0FTNlRfbnNZbkxvbDMtNEgzWE4?oc=5" }, { "entities": [], "id": "2d9fe915b59f2067", "kind": "forum", "meta": { "age_hours_at_score": 2.3, "categories": [ "MachineLearning" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:06:50.000Z", "first_source": "reddit-machinelearning", "last_published_at": "2026-10-15T06:06:50.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:06:50.000Z", "sources": [ "reddit-machinelearning" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/MachineLearning/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T07:18:20.412Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "hardware/capital keyword", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.1, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null }, "pillar": "compute", "published_at": "2026-10-15T06:06:50.000Z", "score": 30.4, "source": "reddit-machinelearning", "summary": "PhD student here. My university’s GPU cluster has apparently become a luxury resort. I submit a job requesting a couple of GPUs, and it takes 3–4 business days just to START running. Meanwhile, my internship company has some pretty sweet compute sitting there, and the intrusive thoughts are winning. Hypothetically…", "title": "University GPU cluster is holding my research hostage. Can I use my internship company’s GPUs instead? 💀 [D]", "url": "https://reddit.com/r/MachineLearning/comments/1x6dytx/university_gpu_cluster_is_holding_my_research" }, { "entities": [], "id": "f988a8b4e67d3fb2", "kind": "press", "meta": { "age_hours_at_score": 2.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:06:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T06:06:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:06:00.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:58:20.287Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "reuters.com", "pillar_reason": "source default", "publisher": "Reuters", "publisher_url": "https://www.reuters.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.1, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T06:06:00.000Z", "score": 37.9, "source": "wires-ai", "summary": "Reuters, via Google News.", "title": "AI-boosted UK economy beats forecasts, giving Healey pre-budget lift", "url": "https://news.google.com/rss/articles/CBMikwFBVV95cUxQclVJOWlaQURzRjZZVzhibFpUTnpjc2F3enRTT1NKZUotUjF2WUpCWjhFT0lzSlY0SXpoZ3ZlZDhMYkMtN1Vfa3Y1UGtZemFmV3N6WVVPQ3p2dld0N3ZubDUzVndDRE52dWdVbmpwOEFYY0UxbzYwR3dGcTNxSG9UcmNxd2ZhSzJVT2FRUTRrbnNldjg?oc=5" }, { "entities": [], "id": "9bde0c46d3d0b8c9", "kind": "press", "meta": { "age_hours_at_score": 2.3, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-15T06:05:22.000Z", "source": "bloomberg-tech", "title": "Blue Owl’s Stack Eyes $6 Billion Loan as Selloff Hits AI Sector", "url": "https://bloomberg.com/news/articles/2026-10-15/blue-owl-s-stack-eyes-6-billion-loan-as-selloff-hits-ai-sector", "weight": 0.6 }, { "dedup_key": null, "engagement": null, "kind": "press", "outlet": "bloomberg.com", "published_at": "2026-10-15T06:05:00.000Z", "source": "wires-ai", "title": "Blue Owl’s Stack Eyes $6 Billion Loan as Selloff Hits AI Sector", "url": "https://news.google.com/rss/articles/CBMi0gFBVV95cUxNN1IzaV9rUnJkNmo4MmhHQnU0WWNndTFxbDR4TGZhajI3MEJxUERmT19sb2p5VzZodjhaR2RoTnY0TFBOYWRsTDJWQmkzOUI5NzRVYm92Y0dHNzM3LTF6TlREaGJibUw0RVNNeVFOOEV6UEFoOUctNkZhQnNnQUhIUXhsYkZTeVo5dExON09GbjNsczdQTlQxOWRmd24wbTBQdUhuaWpFVEdiclBQUXNVY1dGYVNLSmxDY1ZKRFF5VGJoUlc5dzA1dDE3aXFfalhDUXc?oc=5", "weight": 0.75 } ], "count": 2, "first_seen_published_at": "2026-10-15T06:05:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T06:05:22.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:05:00.000Z", "sources": [ "bloomberg-tech", "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "bloomberg.com", "pillar_reason": "hardware/capital keyword", "publisher": "Bloomberg.com", "publisher_url": "https://www.bloomberg.com", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.1, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "compute", "published_at": "2026-10-15T06:05:00.000Z", "score": 50.4, "source": "wires-ai", "summary": "Bloomberg.com, via Google News.", "title": "Blue Owl’s Stack Eyes $6 Billion Loan as Selloff Hits AI Sector", "url": "https://news.google.com/rss/articles/CBMiswFBVV95cUxNRDlmYUZtdEtNaFJ0cXRLN0lpUWY3LWJGd0ZXdkdCM2xGVk9qbTNHUjJGTTBIVGdBMjNiSWpIckVOakxWQTNaZ2RnTzFGc3dIdEc0M0JOcUtnRHltdjVoUC1KWXAtVThxdV8tNGVVaGE4V0JSNXJIWVAyN2xoMXg2a3UwTDNGTzc2YmFhQy1USDNGd3NDQXhINlFDQ01VaS1SUG9zX1BFbjhabmhMNlI1YmZaaw?oc=5" }, { "entities": [ "Meta AI" ], "id": "0e8804db9e183f17", "kind": "press", "meta": { "age_hours_at_score": 2.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:01:54.000Z", "first_source": "scmp-tech", "last_published_at": "2026-10-15T06:01:54.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:01:54.000Z", "sources": [ "scmp-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "Meta AI" ], "models": [] }, "feed": "https://www.scmp.com/rss/36/feed", "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 7, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.1, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "0eff40998d8f", "is_lead": true, "lead": "0e8804db9e183f17", "linked_by": [ { "rule": "shared-words", "with": "6ac8f6a9e00e0e9e", "words": [ "amid", "china", "push" ] } ], "members": [ "0e8804db9e183f17", "6ac8f6a9e00e0e9e" ], "severity": 0, "source_count": 1, "sources": [ "scmp-tech" ] } }, "pillar": "attention", "published_at": "2026-10-15T06:01:54.000Z", "score": 37.1, "source": "scmp-tech", "summary": "A Chinese start-up has unveiled a prototype extreme ultraviolet (EUV) light-source machine – a component often described as the “heart” of advanced lithography systems – marking an early but critical step in Beijing’s drive for self-reliance in chipmaking equipment. At the China International Industry Fair in…", "title": "Start-up unveils prototype ‘heart’ of EUV lithography amid China chipmaking push", "url": "https://scmp.com/tech/big-tech/article/3370966/start-unveils-prototype-heart-euv-lithography-amid-china-chipmaking-push" }, { "entities": [], "id": "4f2c5c550b5002fb", "kind": "forum", "meta": { "age_hours_at_score": 2.4, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:00:52.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-15T06:00:52.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:00:52.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T08:20:18.881Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.1, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-15T06:00:52.000Z", "score": 27.9, "source": "reddit-artificial", "summary": "Sorry am tech noob. Ive seen experts on the news talking about P(doom). Are these just vibe reading or based on anything scientific? submitted by /u/WrongdoerFew2580 [link] [comments]", "title": "How valuable is P(doom)?", "url": "https://reddit.com/r/artificial/comments/1x6dv9l/how_valuable_is_pdoom" }, { "entities": [], "id": "aa1b19172743f47a", "kind": "press", "meta": { "age_hours_at_score": 2.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:00:11.000Z", "first_source": "scmp-tech", "last_published_at": "2026-10-15T06:00:11.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:00:11.000Z", "sources": [ "scmp-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.scmp.com/rss/36/feed", "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 7, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.1, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-15T06:00:11.000Z", "score": 34.1, "source": "scmp-tech", "summary": "A mild displacement of jobs across the Asia-Pacific region as companies expand their use of AI would trim five percentage points off growth in office space demand in the next few years, according to property consultancy CBRE. In a report released on Thursday it said 3.9 per cent of office jobs were vulnerable to…", "title": "AI job losses set to rein in office demand in developed Asia-Pacific economies: report", "url": "https://scmp.com/business/article/3370934/ai-job-losses-set-rein-office-demand-developed-asia-pacific-economies-report" }, { "entities": [], "id": "4b6c1202e73f5660", "kind": "press", "meta": { "age_hours_at_score": 2.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T06:00:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T06:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T06:00:00.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "reuters.com", "pillar_reason": "hardware/capital keyword", "publisher": "Reuters", "publisher_url": "https://www.reuters.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.1, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "compute", "published_at": "2026-10-15T06:00:00.000Z", "score": 37.9, "source": "wires-ai", "summary": "Reuters, via Google News.", "title": "COMMENTARY: AI investment boom may be papering over US economic cracks", "url": "https://news.google.com/rss/articles/CBMi1AFBVV95cUxQS1Q2OXZIUkpRbEJQUVlCVUJzdDZQQ2pJelNhTmZVTGJYSEtCWGd6TXZtTzJnc01UUGR5bzdmZW4tZ2pLQ3hCSXQwMzV4TGdXQS1ZMkE3cDg0R2RjYXRQRF9ETERWNXpLS3JjcjViWk5ZLU14ajZjbmt0aWJaZHphdWhEV0tCZ2RiRU1ZLTQwRE1GWHo3dUxkeFM1aWEtRDNyWERYTTZJN05qUmRmSEJGU19oTGdEWnFOSElyTnp3ZFB3V0RrOEI4LUhVeWRhX3pHQ1NaZQ?oc=5" }, { "entities": [ "NVIDIA", "TSMC" ], "id": "e07f6f4e87fe86b1", "kind": "press", "meta": { "age_hours_at_score": 2.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T05:59:22.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-15T05:59:22.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T05:59:22.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "NVIDIA", "TSMC" ], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "hardware/capital keyword", "rejected_off_topic": 6, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 19.1, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "ce8b11d9deae", "is_lead": false, "lead": "c8e74cdda34787ec", "linked_by": [ { "rule": "shared-words", "with": "210621f9ac5bb73c", "words": [ "expectat", "profit", "tsmc" ] } ], "members": [ "0a55943b50812b3d", "1d21ad3b85d15530", "210621f9ac5bb73c", "4e77036ce45202de", "63901e460b8fbba9", "79bd3ba482daff2d", "85f25bcb68531441", "9abd2667a185bf33", "c8e74cdda34787ec", "db7de00c48010628", "e07f6f4e87fe86b1", "e2fa9544501179ca", "e3e32d7d473c157c", "e5adffa73aaf9a9c", "f7fdca7cbe7c0131" ], "severity": 0, "source_count": 6, "sources": [ "arxiv-newest", "bloomberg-tech", "bluesky-ai", "gnews-ai", "techmeme", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-15T05:59:22.000Z", "score": 40.1, "source": "bloomberg-tech", "summary": "TSMC reported a better-than-expected profit for the third quarter as net income rose 69% to NT$763.1 billion ($23.6 billion). That came after the exclusive chipmaker of Nvidia and Apple’s most advanced semiconductors reported a sales jump of 51% in the period, taking its revenue across the three months to $47 billion.…", "title": "TSMC’s Third-Quarter Profit Beats Expectations", "url": "https://bloomberg.com/news/videos/2026-10-15/tsmc-s-third-quarter-profit-beats-expectations-video" }, { "entities": [], "id": "09376befd13c101d", "kind": "press", "meta": { "age_hours_at_score": 2.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T05:58:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T05:58:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T05:58:00.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:39:37.691Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "deccanherald.com", "pillar_reason": "source default", "publisher": "Deccan Herald", "publisher_url": "https://www.deccanherald.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.1, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T05:58:00.000Z", "score": 37.9, "source": "wires-ai", "summary": "Deccan Herald, via Google News.", "title": "US military prepared to use ‘mother of all bombs’ in Iran: Report", "url": "https://news.google.com/rss/articles/CBMivwFBVV95cUxOR1N5am5QN2VxTW9EeEw4YnJiWEJ2V1JwOThBX04wWUdreWlETGN3XzhjOW84aWFvcnQ2bWMzLWptX0E5WUJBMFA2SE1fVEQxOXZpZUx2dEtoR1RxWVJqa01kQ2pYV1lrQlRIRzloSkhOZ1U0QlhpUlR5U1BZbnpjRkhkSjJZMlZua2RaM0Vma3BXOU1NcElFNVZTMTV4YTJuanZwSnk5NFVGc0NOZGMzVXB4WGxkLUtsc0x1OWhiOA?oc=5" }, { "entities": [ "TSMC" ], "id": "85f25bcb68531441", "kind": "press", "meta": { "age_hours_at_score": 2.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T05:56:47.000Z", "first_source": "techmeme", "last_published_at": "2026-10-15T05:56:47.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T05:56:47.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [ "TSMC" ], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "hardware/capital keyword", "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.1, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": { "id": "ce8b11d9deae", "is_lead": false, "lead": "c8e74cdda34787ec", "linked_by": [ { "rule": "shared-words", "with": "4e77036ce45202de", "words": [ "quarterly", "record", "yoy" ] }, { "rule": "shared-words", "with": "63901e460b8fbba9", "words": [ "demand", "profit", "record", "tsmc" ] } ], "members": [ "0a55943b50812b3d", "1d21ad3b85d15530", "210621f9ac5bb73c", "4e77036ce45202de", "63901e460b8fbba9", "79bd3ba482daff2d", "85f25bcb68531441", "9abd2667a185bf33", "c8e74cdda34787ec", "db7de00c48010628", "e07f6f4e87fe86b1", "e2fa9544501179ca", "e3e32d7d473c157c", "e5adffa73aaf9a9c", "f7fdca7cbe7c0131" ], "severity": 0, "source_count": 6, "sources": [ "arxiv-newest", "bloomberg-tech", "bluesky-ai", "gnews-ai", "techmeme", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-15T05:56:47.000Z", "score": 39.6, "source": "techmeme", "summary": "Jenny Lee / CNBC : TSMC reports Q3 revenue up 50.9% YoY to $46.36B, net income up 68.7% to $23.7B, both above est., marking a sixth straight quarterly profit record on AI demand — Taiwan Semiconductor Manufacturing Co. on Thursday reported a record third-quarter profit that beat estimates, powered by an unrelenting…", "title": "TSMC reports Q3 revenue up 50.9% YoY to $46.36B, net income up 68.7% to $23.7B, both above est., marking a sixth straight quarterly profit record on AI demand (Jenny Lee/CNBC)", "url": "https://techmeme.com/261015/p7" }, { "entities": [], "id": "0796d54768d8f16e", "kind": "press", "meta": { "age_hours_at_score": 2.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T05:56:18.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T05:56:18.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T05:56:18.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T08:13:19.051Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "wsj.com", "pillar_reason": "source default", "publisher": "WSJ", "publisher_url": "https://www.wsj.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.1, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T05:56:18.000Z", "score": 37.9, "source": "wires-ai", "summary": "WSJ, via Google News.", "title": "PE Daily: Investors Warn About AI Concentration Risks", "url": "https://news.google.com/rss/articles/CBMiogFBVV95cUxNYkg5NWRPWmNidWEzM2NIaG5iM1dVNExHX1dXTVJNZm54RklKdWFGMzd6cDdFVXBxLTFlNlZiRHNxQXBTV2FJbDloak9PWklZa3BSMXB6QzNwbWxyNmp3c0ZCY3RQZW13aDIzNF82LWYzNlYwazRJQ0cwTUxZUzhybTZYUTQ1TGRlS1BDaWlIblltOWxzeTdHbmRpckdXSDM1Wmc?oc=5" }, { "entities": [], "id": "8aaeee65dbad7e6d", "kind": "press", "meta": { "age_hours_at_score": 2.4, "categories": [ "AI (artificial intelligence)", "Business", "Cities", "Computing", "Cost of living crisis", "Economics", "Europe", "European Union", "Gentrification", "Poland", "Technology", "Technology sector", "World news" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T05:56:12.000Z", "first_source": "guardian-ai", "last_published_at": "2026-10-15T05:56:12.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T05:56:12.000Z", "sources": [ "guardian-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.theguardian.com/technology/artificialintelligenceai/rss", "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.1, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-15T05:56:12.000Z", "score": 34.1, "source": "guardian-ai", "summary": "In the final part of our series on the country, Poles tell how wages have soared – along with the cost of living Will military spending help or hinder Poland’s growth story? Poland’s push for EU energy resilience amid Russian aggression Above the rain-soaked streets of Warsaw, the observation deck of the Varso tower…", "title": "Poland looks to economic heights in a country transformed since Soviet days", "url": "https://theguardian.com/world/2026/oct/15/poland-economy-heights-transformed-regional-tech-hub" }, { "entities": [], "id": "a4e4af50bbf19bf5", "kind": "forum", "meta": { "age_hours_at_score": 2.5, "bsky_handle": "bloomberg.com", "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T05:54:49.000Z", "first_source": "bluesky-ai", "last_published_at": "2026-10-15T05:54:49.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T05:54:49.000Z", "sources": [ "bluesky-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 5 }, "entity_kinds": { "labs": [], "models": [] }, "external_url": null, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 5, "mode": "author-feeds", "outlet_domain": "bloomberg.com", "pillar_reason": "source default", "reposts": 0, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 4, "entities": 0, "recency": 19.1, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "ad4350870e7d", "is_lead": false, "lead": "503be02b143344f6", "linked_by": [ { "rule": "shared-words", "with": "f572f7deb5faa75d", "words": [ "bloomberg", "economy", "forum" ] } ], "members": [ "503be02b143344f6", "9575be1071f6749e", "a4e4af50bbf19bf5", "f572f7deb5faa75d" ], "severity": 0, "source_count": 3, "sources": [ "bloomberg-tech", "bluesky-ai", "wires-ai" ] }, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-15T05:54:49.000Z", "score": 38.1, "source": "bluesky-ai", "summary": "Posted on Bluesky by @bloomberg.com.", "title": "Former UK Prime Minister Rishi Sunak speaks on AI and the need for better governance at Bloomberg’s New Economy Forum, held in New Delhi. Read more: bloom.bg/480Dqj3", "url": "https://bsky.app/profile/bloomberg.com/post/3mxversq62225" }, { "entities": [ "TSMC" ], "id": "1d21ad3b85d15530", "kind": "press", "meta": { "age_hours_at_score": 2.5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T05:54:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T05:54:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T05:54:00.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "TSMC" ], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "wsj.com", "pillar_reason": "hardware/capital keyword", "publisher": "WSJ", "publisher_url": "https://www.wsj.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19.1, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": { "id": "ce8b11d9deae", "is_lead": false, "lead": "c8e74cdda34787ec", "linked_by": [ { "rule": "shared-words", "with": "63901e460b8fbba9", "words": [ "demand", "record", "tsmc" ] } ], "members": [ "0a55943b50812b3d", "1d21ad3b85d15530", "210621f9ac5bb73c", "4e77036ce45202de", "63901e460b8fbba9", "79bd3ba482daff2d", "85f25bcb68531441", "9abd2667a185bf33", "c8e74cdda34787ec", "db7de00c48010628", "e07f6f4e87fe86b1", "e2fa9544501179ca", "e3e32d7d473c157c", "e5adffa73aaf9a9c", "f7fdca7cbe7c0131" ], "severity": 0, "source_count": 6, "sources": [ "arxiv-newest", "bloomberg-tech", "bluesky-ai", "gnews-ai", "techmeme", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-15T05:54:00.000Z", "score": 40.9, "source": "wires-ai", "summary": "WSJ, via Google News.", "title": "AI Demand Extends TSMC’s Record Earnings Streak", "url": "https://news.google.com/rss/articles/CBMihwFBVV95cUxNdEdMeDJhNG03UExRNVQ2MEJQWndsZDFteUhQSC1WekZrdnFWVHdBb2s0V2I4SVJCakw5Z0d0dVpkRUdVSXlSQUxJaFMxdERQX010a1ZVeHZYV3AtWVh2YVVkN1pDcU4xLUNQUHpRWVRLY2ZRc0poWU9leUt6LU5BOEZtOFctbjA?oc=5" }, { "entities": [], "id": "4c5b077138aa38ee", "kind": "press", "meta": { "age_hours_at_score": 2.5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T05:54:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T05:54:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T05:54:00.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "reuters.com", "pillar_reason": "source default", "publisher": "Reuters", "publisher_url": "https://www.reuters.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19.1, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T05:54:00.000Z", "score": 37.9, "source": "wires-ai", "summary": "Reuters, via Google News.", "title": "Is the AI rout making bonds safe again?", "url": "https://news.google.com/rss/articles/CBMilAFBVV95cUxPc2pzbXo0U2otM1p2TXYwRHZRUjNuWFRfLV9ob0l1ZjB3Sk55TGVETVZsSHRhUkZDMUFFbkVPSWNGQnc2eVBlUTNDLXJfMUJXQ0VsM3gwM1dGSGRrN29HMWF2dW9aOTFyRFNQSmJ1RnN5bmtBWGVsODJmd3BGSXk1aG5uWmFxWFVORkNQVlJ4UzBueWc1?oc=5" }, { "entities": [], "id": "bda0f28d7d904118", "kind": "press", "meta": { "age_hours_at_score": 2.5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T05:50:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T05:50:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T05:50:00.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "bloomberg.com", "pillar_reason": "source default", "publisher": "Bloomberg.com", "publisher_url": "https://www.bloomberg.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T05:50:00.000Z", "score": 37.8, "source": "wires-ai", "summary": "Bloomberg.com, via Google News.", "title": "The AI Boom Is Creating a New Market for $500,000 Matchmakers", "url": "https://news.google.com/rss/articles/CBMiwgFBVV95cUxNanNTZlNaUmxEcW5MZVJrbXBKaHQybE9jOXc3N2pMcVFoNnNJb05KUE5IUUxscV9xWlVQT3YzOUE3SEJLSFJBdlBEZ1hrdmEwdFlHcm4tUnlWb2NuYVI4NTJmdW1zOHR5SGdJTmhKdEJUeDhYVWJIVmxKcHRPd3p2UnBoRmpSekJnR3VnQXU5dE5WLWlrUzZqY0gtcjdCT29Ua2lBc2J3Vjd3QV9nV1RoUEc5c2dsX3E1R0FxbURZUE1vZw?oc=5" }, { "entities": [], "id": "5e4aaa8c75a8e41b", "kind": "press", "meta": { "age_hours_at_score": 2.6, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T05:49:07.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T05:49:07.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T05:49:07.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:39:37.691Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "sundayguardianlive.com", "pillar_reason": "source default", "publisher": "The Sunday Guardian", "publisher_url": "https://sundayguardianlive.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": { "id": "6e67bd05b171", "is_lead": false, "lead": "7395d8a41759c141", "linked_by": [ { "rule": "shared-words", "with": "7395d8a41759c141", "words": [ "anti", "criminal", "hegseth", "iran", "pet", "protester", "war" ] } ], "members": [ "5e4aaa8c75a8e41b", "7395d8a41759c141", "87f63a586123e782" ], "severity": 0, "source_count": 1, "sources": [ "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-15T05:49:07.000Z", "score": 37.8, "source": "wires-ai", "summary": "The Sunday Guardian, via Google News.", "title": "US-Israel-Iran War Latest News: ‘War Criminal’, ‘You’re a Coward’ – Pete Hegseth Heckled by Anti-War Protesters at US Army Conference Over Iran War and Gaza Crisis", "url": "https://news.google.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?oc=5" }, { "entities": [ "Coinbase" ], "id": "a49f608d7b66b203", "kind": "press", "meta": { "age_hours_at_score": 2.7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T05:40:01.000Z", "first_source": "techmeme", "last_published_at": "2026-10-15T05:40:01.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T05:40:01.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [ "Coinbase" ], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": null }, "pillar": "attention", "published_at": "2026-10-15T05:40:01.000Z", "score": 39.5, "source": "techmeme", "summary": "Camila Grigera Naón / Fortune : Chainalysis: agentic commerce on Coinbase's Base grew from ~1,500 transactions in Q2 2025 to 138M+ in Q3 2026; 87%+ of the payments were for less than 10 cents — The field of agentic commerce, which describes the use of AI agents to carry out internet transactions, is rapidly…", "title": "Chainalysis: agentic commerce on Coinbase's Base grew from ~1,500 transactions in Q2 2025 to 138M+ in Q3 2026; 87%+ of the payments were for less than 10 cents (Camila Grigera Naón/Fortune)", "url": "https://techmeme.com/261015/p6" }, { "entities": [], "id": "1f52dcc8a2eb70dd", "kind": "press", "meta": { "age_hours_at_score": 2.7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T05:39:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T05:39:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T05:39:00.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "apnews.com", "pillar_reason": "source default", "publisher": "AP News", "publisher_url": "https://apnews.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 19, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T05:39:00.000Z", "score": 37.8, "source": "wires-ai", "summary": "AP News, via Google News.", "title": "Historic first as AI gives UK peer with disabilities a voice in the Lords", "url": "https://news.google.com/rss/articles/CBMiywFBVV95cUxOUkg0WTJkOXdzbzN1dWthTEZ1bEswYVpsZkE5dURWSDU4bk5pSE5nN2ZMZHBWd2ZvQjdjaWZqRTZpV1pNbEdTRGctSGlMMHRrS3RVX21fVFptTVdMd0NzbUt1UVZrbkMyQWJkazlvZzJ4VElwZEVYMDBCZWk1bGY4M1NxVjFkak43SXRRM3RIUTRwYklUT0Q4NXVrTjV1OWx0a2xFaGZDazRHRDdoVklUZ1R2N2VUeENpbmt0YzRKTzdCTlBpS0lfakpNSQ?oc=5" }, { "entities": [ "TSMC" ], "id": "9abd2667a185bf33", "kind": "press", "meta": { "age_hours_at_score": 2.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T05:35:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T05:35:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T05:35:00.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "TSMC" ], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "reuters.com", "pillar_reason": "hardware/capital keyword", "publisher": "Reuters", "publisher_url": "https://www.reuters.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 19, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": { "id": "ce8b11d9deae", "is_lead": false, "lead": "c8e74cdda34787ec", "linked_by": [ { "rule": "shared-words", "with": "210621f9ac5bb73c", "words": [ "expectat", "profit", "tsmc" ] }, { "rule": "shared-words", "with": "63901e460b8fbba9", "words": [ "jump", "profit", "record", "tsmc" ] } ], "members": [ "0a55943b50812b3d", "1d21ad3b85d15530", "210621f9ac5bb73c", "4e77036ce45202de", "63901e460b8fbba9", "79bd3ba482daff2d", "85f25bcb68531441", "9abd2667a185bf33", "c8e74cdda34787ec", "db7de00c48010628", "e07f6f4e87fe86b1", "e2fa9544501179ca", "e3e32d7d473c157c", "e5adffa73aaf9a9c", "f7fdca7cbe7c0131" ], "severity": 0, "source_count": 6, "sources": [ "arxiv-newest", "bloomberg-tech", "bluesky-ai", "gnews-ai", "techmeme", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-15T05:35:00.000Z", "score": 40.8, "source": "wires-ai", "summary": "Reuters, via Google News.", "title": "TSMC Q3 profit jumps 69% to record, beating expectations", "url": "https://news.google.com/rss/articles/CBMiqgFBVV95cUxQUkd5cXQ0TjJXLWVtRFJZS0x5T1p5ZlBWTllOZjdqTG1xTTJ5eURYQm52eUdQYnhEVy1YSDg2YjU2M2RZQnFBYXk5SEE4U2tSVFZlMTdBMlJTUjRxSVNQREFiR2dTc045blJabGR3eGs1MEtpU09vd09pTkpkUGplY25udTJSamxpbjM1R19CdUNOeUFSM1dHWXhGZDBsN0xVOWM4dzhUNjNZZw?oc=5" }, { "entities": [ "Microsoft" ], "id": "667e847d9eb4906b", "kind": "press", "meta": { "age_hours_at_score": 2.9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T05:31:12.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T05:31:12.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T05:31:12.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Microsoft" ], "models": [] }, "first_seen_at": "2026-10-15T07:58:20.287Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "bloomberg.com", "pillar_reason": "source default", "publisher": "Bloomberg.com", "publisher_url": "https://www.bloomberg.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 18.9, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T05:31:12.000Z", "score": 40.7, "source": "wires-ai", "summary": "Bloomberg.com, via Google News.", "title": "Microsoft Shows the AI Bubble Will Grow Bigger", "url": "https://news.google.com/rss/articles/CBMixgFBVV95cUxQWHZTSUU3T3ExSWpLWWRXWGYtdldIRFB1TTVnbnJaUjhCSlFQQUh4RXlNMFh6VUpuREdRc0RiWnIzVmx2eFdBMjMzeFZ1RXZvRm9SenJscFFfS3pkQWZVMmRWT0pIRnFkSGNHbHc1T3d1RFF3MkZHVXR6V1A5QlctU09mZDlqVE5zMDZ2dGtQeVhVTGYyeC05ZzJvTnlFZXhGYW1fUkNwdUxET2JfVzJfdWJTUTdERVZjWUFNQVh3M1JOOUJ5LUE?oc=5" }, { "entities": [], "id": "804fb2a10a3c0920", "kind": "press", "meta": { "age_hours_at_score": 2.9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T05:30:45.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T05:30:45.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T05:30:45.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "ft.com", "pillar_reason": "source default", "publisher": "Financial Times", "publisher_url": "https://www.ft.com", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 18.9, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T05:30:45.000Z", "score": 37.7, "source": "wires-ai", "summary": "Financial Times, via Google News.", "title": "CPI comes in hot, AI goes cold", "url": "https://news.google.com/rss/articles/CBMihAFBVV95cUxNbFBVTXRmemNna2l2ZmZCamo2VVVfbXF5M1hfUmxuWE1rSzkwSkZ3ZDVMSFVGTU9jcGFNQUlEb2o1clhJdi03a0hndG5sUkZfX0dvNjdTSENGVU5KNWRmdXJmUkdmLTIyZnFsMERYUnNHaHRFaXBZSTN2LXNWdlVKOTdjYkw?oc=5" }, { "entities": [], "id": "9d758fa3f9a6c3e7", "kind": "press", "meta": { "age_hours_at_score": 2.9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T05:30:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T05:30:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T05:30:00.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "telegraph.co.uk", "pillar_reason": "hardware/capital keyword", "publisher": "The Telegraph", "publisher_url": "https://www.telegraph.co.uk", "score_components": { 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"2026-10-15T05:18:00.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "reuters.com", "pillar_reason": "hardware/capital keyword", "publisher": "Reuters", "publisher_url": "https://www.reuters.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 18.9, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "compute", "published_at": "2026-10-15T05:18:00.000Z", "score": 37.7, "source": "wires-ai", "summary": "Reuters, via Google News.", "title": "Cost of AI default insurance spikes as data centre confidence wobbles", "url": "https://news.google.com/rss/articles/CBMikAFBVV95cUxOa21Bbk9kN3JUMWZXLWNLVWFlRkQ1dDFSYnRQQzBRNDVudTYtaDhjcjcyenBQM0prdHlYY2ZxY25fZzdYQk9FYkh1N043NW83amxZbHVvZUxYUXk5YnVtR3NtaXdPMFFNa3Z4UUpYdWlXQzhmbjV2bF96TktQQ3VNOFFhLWMtcUE2NllOMzNsN3I?oc=5" }, { "entities": [], "id": "4174db52065c959d", "kind": "press", "meta": { "age_hours_at_score": 3.1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T05:13:32.000Z", "first_source": "govuk-ai", "last_published_at": "2026-10-15T05:13:32.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T05:13:32.000Z", "sources": [ "govuk-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.gov.uk/search/news-and-communications.atom?keywords=artificial+intelligence", "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 18.8, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "governance", "published_at": "2026-10-15T05:13:32.000Z", "score": 32.6, "source": "govuk-ai", "summary": "As the UK-ASEAN Dialogue Partnership marks its fifth year, Helen Fazey highlights progress and future opportunities for collaboration.", "title": "5 Years on, the UK and ASEAN must keep advancing together: op-ed by Helen Fazey", "url": "https://gov.uk/government/news/5-years-on-the-uk-and-asean-must-keep-advancing-together-op-ed-by-helen-fazey" }, { "entities": [ "Micron" ], "id": "8593780cca0bf3f3", "kind": "press", "meta": { "age_hours_at_score": 3.2, "corroboration": { "also": [ { "dedup_key": null, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 5 }, "kind": "forum", "outlet": "reuters.com", "published_at": "2026-10-15T07:35:05.000Z", "source": "bluesky-ai", "title": "Taiwan president calls for Micron, workers to negotiate 'sincerely' ahead of possible strike", "url": "https://bsky.app/profile/reuters.com/post/3mxvkf4kwwd2a", "weight": 0.6 } ], "count": 2, "first_seen_published_at": "2026-10-15T05:11:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T07:35:05.000Z", "lead_minutes": 144, "primary_published_at": "2026-10-15T05:11:00.000Z", "sources": [ "bluesky-ai", "wires-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 5 }, "entity_kinds": { "labs": [ "Micron" ], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "reuters.com", "pillar_reason": "source default", "publisher": "Reuters", "publisher_url": "https://www.reuters.com", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 4, "entities": 3, "recency": 18.8, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T05:11:00.000Z", "score": 57.1, "source": "wires-ai", "summary": "Reuters, via Google News.", "title": "Taiwan president calls for Micron, workers to negotiate 'sincerely' ahead of possible strike", "url": "https://news.google.com/rss/articles/CBMizwFBVV95cUxQcTFwdXItQUpwWG1SRlRyaXpsLTlkdFdxcUxQZmdSOWVZdG1oVnkxRlBObkxJQ1U4M2FnUjZTTUZrN1M5bFE3VUpWbl8taHVpWWVmSHFkb2JCZkZsYS1OSjZxLTFoUnlkQmUwNmFOQzFtcExrN1RUTnhZaWI5NUpjaENiWnNvcVVTTVFSNUNMcEV4WEw5c3dCelNDbFk3MVVpQ3ZzYXN1Z205X1NLV0J4c2hDNGt5S0xrN0Q4eG9HejRHZGs3S0JSZjhQMVRzMjA?oc=5" }, { "entities": [], "id": "66deac3566f373e9", "kind": "press", "meta": { "age_hours_at_score": 3.2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T05:09:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T05:09:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T05:09:00.000Z", "sources": [ "wires-ai" ] }, 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"https://news.google.com/rss/articles/CBMitwFBVV95cUxOeVl3TUpFMUJ3Qmd5RmRRLUtienRuSEJia1dtd24wVm5iMHFhbUQ0aFFRUk56cWVJQ01PZmJPVVZBN3JHVGpFTXVNX1ozNElOSGY3MFZ3WTJJYXEwZmIyc21NVjlKRFZRU3hHVTBxNVg5WGpxdmZJcm56cDAtdm1ob0llbmV0VllnNFdlbkcxTTJVUldIYTR6bXp0WHNteW93X0tyT1N4LUtEOGpORlU1eWRDZndJS2M?oc=5" }, { "entities": [], "id": "5926326b057d7dc9", "kind": "press", "meta": { "age_hours_at_score": 3.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T05:06:52.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T05:06:52.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T05:06:52.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": "B", "excluded": null, "severity": 0.6, "strong_signal": false, "terms": [ { "field": "title", "matched": "attacks", "term": "attacks", "tier": "b", "weight": 0.6 } ] }, "outlet_domain": 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"https://news.google.com/rss/articles/CBMiWEFVX3lxTE9mOWhURHZ5ejRVVTF0Qk03MHFpeVIteFYtamRIVUcydUJwNkVnX0Y4OFF2My1FVTdMS25EdzY2Q3NjeFV0al9yY3FLcndjaTc0cVdBSGloNUM?oc=5" }, { "entities": [ "Claude" ], "id": "15107a0d279b3003", "kind": "forum", "meta": { "age_hours_at_score": 3.3, "comments": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T05:02:06.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-15T05:02:06.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T05:02:06.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50099375", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 3 }, "entity_kinds": { "labs": [], "models": [ "Claude" ] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "hn_id": "50099375", "incident": { "band": "A", "excluded": null, "severity": 1, "strong_signal": true, "terms": [ { "field": "title", "matched": "exfiltration", "term": "exfiltration", "tier": "a", "weight": 1 } ] }, "outlet_domain": "noma.security", "pillar_reason": "source default", "points": 3, "retained": true, "score_components": { "corroboration": 0, "coverage": 5, "engagement": 4, "entities": 3, "recency": 18.8, "severity": 15, "source_weight": 11.3 }, "source_weight": 0.45, "story": { "id": "5d82c5f4c985", "is_lead": true, "lead": "15107a0d279b3003", "linked_by": [ { "rule": "shared-words", "with": "4beb41f25b1fc84b", "words": [ "claud", "cod", "desktop" ] }, { "rule": "incident-pair", "with": "9cfef81b690b3ab2", "words": [ "claud", "cod" ] } ], "members": [ "15107a0d279b3003", "4beb41f25b1fc84b", "9cfef81b690b3ab2" ], "severity": 1, "source_count": 2, "sources": [ "gnews-ai", "hn-fresh-ai" ] }, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-15T05:02:06.000Z", "score": 57.1, "source": "hn-fresh-ai", "summary": "New on Hacker News: 3 points, 0 comments so far.", "title": "Claude Code Desktop Vulnerability: Preview Pane Data Exfiltration", "url": "https://noma.security/noma-labs/sneakpeek-how-the-preview-pane-in-claude-code-desktop-can-exfiltrate-your-data" }, { "entities": [ "Samsung" ], "id": "4e77036ce45202de", "kind": "press", "meta": { "age_hours_at_score": 3.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T05:00:56.000Z", "first_source": "techmeme", "last_published_at": "2026-10-15T05:00:56.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T05:00:56.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [ "Samsung" ], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 18.7, "severity": 0, "source_weight": 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gained while Xiaomi, Oppo, and Vivo saw deeper declines — Apple and Samsung continue to benefit from a strong premium portfolio to gain share — The global smartphone market shrank 22.0% year …", "title": "Global smartphone shipments fell 22% YoY to 255.3M in Q3, a record quarterly drop, as Samsung and Apple gained while Xiaomi, Oppo, and Vivo saw deeper declines (IDC)", "url": "https://techmeme.com/261015/p4" }, { "entities": [], "id": "9c243144b9843464", "kind": "press", "meta": { "age_hours_at_score": 3.4, "categories": [ "AI (artificial intelligence)", "Asia Pacific", "Austerity", "China", "Donald Trump", "Economics", "Global economy", "Globalisation", "International Monetary Fund (IMF)", "Middle East and north Africa" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T05:00:22.000Z", "first_source": "guardian-ai", "last_published_at": "2026-10-15T05:00:22.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T05:00:22.000Z", "sources": [ 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There is little chance of that Not much has gone right for the global economy this year, and plenty has gone wrong. Two big regional wars are destabilising prices and supply chains everywhere.…", "title": "The global economy is muddling through against the odds. But a storm is coming | Larry Elliott", "url": "https://theguardian.com/commentisfree/2026/oct/15/global-economy-multilateral-cooperation-crisis-imf" }, { "entities": [ "NVIDIA" ], "id": "fedbb3936e92270b", "kind": "lab", "meta": { "age_hours_at_score": 3.4, "categories": [ "AI", "AI Factory", "Artificial Intelligence", "Customer Stories", "Events", "Inception", "NVIDIA in Europe", "Open Source", "Physical AI", "Quantum Computing", "Robotics", "Sovereign AI" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T05:00:12.000Z", "first_source": "nvidia-blog", "last_published_at": "2026-10-15T05:00:12.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T05:00:12.000Z", "sources": [ "nvidia-blog" ] }, "engagement": null, "entity_kinds": { "labs": [ "NVIDIA" ], "models": [] }, "feed": "https://blogs.nvidia.com/feed/", "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "hardware/capital keyword", "rejected_off_topic": 2, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 18.7, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": { "id": "0966cb5f8be7", "is_lead": false, "lead": "4396c4b347c1dbe4", "linked_by": [ { "rule": "shared-words", "with": "4396c4b347c1dbe4", "words": [ "advanc", "commit", "nvidia" ] } ], "members": [ "30f6cbd45dfa74c8", "4396c4b347c1dbe4", "ba9b4bd8e730b3b7", "fedbb3936e92270b" ], "severity": 0, "source_count": 6, "sources": [ "gnews-ai", "guardian-ai", "nvidia-blog", "techmeme", "the-decoder", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-15T05:00:12.000Z", "score": 40.5, "source": "nvidia-blog", "summary": "The Nordic countries have become the European leader in unicorn startup density, producing the most emerging companies valued at $1 billion or more per capita on the continent. Now, a new chapter for the region is unfolding as countries race to build one of Europe’s most consequential technology ecosystems. The many…", "title": "The Nordics and NVIDIA Show What’s Possible When a Region Commits to Advanced Computing", "url": "https://blogs.nvidia.com/blog/nordics-technology-ecosystem" }, { "entities": [ "GPT" ], "id": "2223b6ab8feb39b6", "kind": "forum", "meta": { "age_hours_at_score": 3.4, "comments": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T04:57:43.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-15T04:57:43.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T04:57:43.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50099345", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 3 }, "entity_kinds": { "labs": [], "models": [ "GPT" ] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "hn_id": "50099345", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "openai.com", "pillar_reason": "source default", "points": 3, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 4, "entities": 3, "recency": 18.7, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-15T04:57:43.000Z", "score": 37, "source": "hn-fresh-ai", "summary": "New on Hacker News: 3 points, 0 comments so far.", "title": "I had ChatGPT one-shot a novel for me and it was pretty good", "url": "https://chatgpt.com/share/6ad05d0a-3674-83e8-a3fb-91d3ee3f08bb" }, { "entities": [], "id": "87f63a586123e782", "kind": "press", "meta": { "age_hours_at_score": 3.5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T04:53:50.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T04:53:50.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T04:53:50.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:39:37.691Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "moneycontrol.com", "pillar_reason": "source default", "publisher": "Moneycontrol.com", "publisher_url": "https://www.moneycontrol.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 18.7, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": { "id": "6e67bd05b171", "is_lead": false, "lead": "7395d8a41759c141", "linked_by": [ { "rule": "shared-words", "with": "7395d8a41759c141", "words": [ "criminal", "hegseth", "nazi", "pet", "war" ] } ], "members": [ "5e4aaa8c75a8e41b", "7395d8a41759c141", "87f63a586123e782" ], "severity": 0, "source_count": 1, "sources": [ "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-15T04:53:50.000Z", "score": 37.5, "source": "wires-ai", "summary": "Moneycontrol.com, via Google News.", "title": "‘War criminal’, ‘Nazi’: Pete Hegseth heckled at US Army conference, responds ‘She thinks highly...", "url": "https://news.google.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?oc=5" }, { "entities": [], "id": "7aa2f850e09fff3f", "kind": "press", "meta": { "age_hours_at_score": 3.6, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T04:45:48.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-15T04:45:48.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T04:45:48.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 6, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 18.7, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "markets", "published_at": "2026-10-15T04:45:48.000Z", "score": 33.7, "source": "bloomberg-tech", "summary": "Suzlon Group Co-Founder Girish Tanti examines what could drive the next phase of India’s wind expansion, from overcoming structural bottlenecks to competing with solar and storage. He also explains why Suzlon is betting on AI-powered blade factories. He speaks with Haslinda Amin from the sidelines of \"Bloomberg New…", "title": "Suzlon's Tanti on India's Next Big Wind Power Push", "url": "https://bloomberg.com/news/videos/2026-10-15/suzlon-s-tanti-on-india-s-next-big-wind-power-push-video" }, { "entities": [], "id": "b57b9658f88d45f4", "kind": "press", "meta": { "age_hours_at_score": 3.7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T04:39:22.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-15T04:39:22.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T04:39:22.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 6, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 18.6, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "markets", "published_at": "2026-10-15T04:39:22.000Z", "score": 33.6, "source": "bloomberg-tech", "summary": "Former UK Prime Minister Rishi Sunak speaks about winning in the AI innovation race, arguing that \"adoption is all.\" (Source: Bloomberg)", "title": "'Adoption Is All': Rishi Sunak on Winners in the AI Race", "url": "https://bloomberg.com/news/videos/2026-10-15/-adoption-is-all-rishi-sunak-on-winners-in-the-ai-race-video" }, { "entities": [ "CoreWeave" ], "id": "fc19c67676f0e349", "kind": "press", "meta": { "age_hours_at_score": 3.7, "corroboration": { "also": [ { "dedup_key": null, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 8 }, "kind": "forum", "outlet": "reuters.com", "published_at": "2026-10-15T05:15:08.000Z", "source": "bluesky-ai", "title": "Morning Bid: Market picks CoreWeave as AI rout deepens", "url": "https://bsky.app/profile/reuters.com/post/3mxvcktn6yr2a", "weight": 0.6 } ], "count": 2, "first_seen_published_at": "2026-10-15T04:38:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T05:15:08.000Z", "lead_minutes": 37, "primary_published_at": "2026-10-15T04:38:00.000Z", "sources": [ "bluesky-ai", "wires-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 8 }, "entity_kinds": { "labs": [ "CoreWeave" ], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "reuters.com", "pillar_reason": "source default", "publisher": "Reuters", "publisher_url": "https://www.reuters.com", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 4.9, "entities": 3, "recency": 18.6, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T04:38:00.000Z", "score": 57.8, "source": "wires-ai", "summary": "Reuters, via Google News.", "title": "Market picks CoreWeave as AI rout deepens", "url": "https://news.google.com/rss/articles/CBMif0FVX3lxTE9rbk90WGc1UEtvTDRhdVRZbWRHaExMOENRdE1ScVZ2MkRodXRSQUdpNjAyVERVelNGVzZZd1djLVk0c2tZMng1Ti1Qeks3UTV6R2tNWERzNlp1cnBLUDR0UUlmZndCY0FaZXRiYmFKbG8zYWRPa3I2WXUwQXJOVTg?oc=5" }, { "entities": [ "Anthropic" ], "id": "6be6e2d65e8a1a97", "kind": "press", "meta": { "age_hours_at_score": 3.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T04:35:19.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T04:35:19.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T04:35:19.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "economist.com", "pillar_reason": "source default", "publisher": "The Economist", "publisher_url": "https://www.economist.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 18.6, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T04:35:19.000Z", "score": 40.4, "source": "wires-ai", "summary": "The Economist, via Google News.", "title": "World in Brief: Zelensky denounces Starlink limits; Anthropic reveals revenues", "url": "https://news.google.com/rss/articles/CBMilwFBVV95cUxPUU94QTVWYUhPOVJMR1h5WlBEa3FtQkFQZmFVQV9EZGQyM3lVbXAxRXp2S3hoZlFiTk9qMUEyQmw4dURBdFRQcF94VzV3d0Y0QnJGVGljVGdCUmVuS3FYXzlpa0dBLTdpM3FtZTloYjZBOHhSc3p2TmVFZmNFN3B6ampoS1huUHpqYjZMa05uZ0VSUkVlREY0?oc=5" }, { "entities": [], "id": "efb289d2c839a2ce", "kind": "press", "meta": { "age_hours_at_score": 3.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T04:35:19.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T04:35:19.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T04:35:19.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "economist.com", "pillar_reason": "hardware/capital keyword", "publisher": "The Economist", "publisher_url": "https://www.economist.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 18.6, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "compute", "published_at": "2026-10-15T04:35:19.000Z", "score": 37.4, "source": "wires-ai", "summary": "The Economist, via Google News.", "title": "Is the AI chip boom about to go bust?", "url": "https://news.google.com/rss/articles/CBMilwFBVV95cUxPSnJSRzNDdFRFNy1DRDVyc0NyZC1lNXowTFppV25WbDhIUVBBeUdTa29SRU9Xd2hVeVREcU1OZ04yZ3hPZkhBd0MzX3F3MTZQS3d2Ukk2MThWQ2Fsd2o4YTdRblNUbFA4QWM5OEI4bzQ4QmtzTWJkMU1EaE15T0Rxbzhlb0dHYjhidU9iZlBCbTRETndReWJN?oc=5" }, { "entities": [], "id": "94d1581c25a72adc", "kind": "press", "meta": { "age_hours_at_score": 3.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T04:35:04.000Z", "first_source": "techmeme", "last_published_at": "2026-10-15T04:35:04.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T04:35:04.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 18.6, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": null }, "pillar": "attention", "published_at": "2026-10-15T04:35:04.000Z", "score": 36.1, "source": "techmeme", "summary": "Steven Rosenbush / Wall Street Journal : Bloomberg is making its ASKB conversational AI interface available to all Terminal users, letting them integrate their own content with existing financial data — Allowing users to add their own information to the company's generative AI-based ASKB interface will feed the system…", "title": "Bloomberg is making its ASKB conversational AI interface available to all Terminal users, letting them integrate their own content with existing financial data (Steven Rosenbush/Wall Street Journal)", "url": "https://techmeme.com/261015/p3" }, { "entities": [], "id": "cd07b26b38b300a8", "kind": "press", "meta": { "age_hours_at_score": 3.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T04:33:58.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-15T04:33:58.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T04:33:58.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 6, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 18.6, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "markets", "published_at": "2026-10-15T04:33:58.000Z", "score": 33.6, "source": "bloomberg-tech", "summary": "Michael Roberts, Corporate and Institutional Banking CEO at HSBC, discusses the progress of implementing artificial intelligence and quantum computing at the bank. He speaks on the sidelines of the IMF-World Bank Annual Meetings in Bangkok. (Source: Bloomberg)", "title": "HSBC on Using AI and Quantum Computing", "url": "https://bloomberg.com/news/videos/2026-10-15/hsbc-on-using-ai-and-quantum-computing-video" }, { "entities": [], "id": "c583a0918e79df9d", "kind": "press", "meta": { "age_hours_at_score": 3.9, "categories": [ "AI", "Apps", "Figma", "India" ], "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-15T06:20:01.000Z", "source": "techmeme", "title": "Figma announces new features to define how AI agents work on its platform, including Figma Specs, a Markdown editor that lets users define their design language (Ivan Mehta/TechCrunch)", "url": "https://techmeme.com/261015/p8", "weight": 0.7 } ], "count": 2, "first_seen_published_at": "2026-10-15T04:30:00.000Z", "first_source": "techcrunch-ai", "last_published_at": "2026-10-15T06:20:01.000Z", "lead_minutes": 110, "primary_published_at": "2026-10-15T04:30:00.000Z", "sources": [ "techcrunch-ai", "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://techcrunch.com/category/artificial-intelligence/feed/", "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 0, "entities": 0, "recency": 18.6, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-15T04:30:00.000Z", "score": 46.1, "source": "techcrunch-ai", "summary": "Figma is also releasing its code layers feature in open beta.", "title": "Figma announces new features to define how agents work on its platform", "url": "https://techcrunch.com/2026/10/14/figma-announces-new-features-to-define-how-agents-work-on-its-platform" }, { "entities": [], "id": "2d852c2010ac8ff8", "kind": "press", "meta": { "age_hours_at_score": 3.9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T04:30:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T04:30:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T04:30:00.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "ft.com", "pillar_reason": "source default", "publisher": "Financial Times", "publisher_url": "https://markets.ft.com", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 18.6, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": { "id": "e29d5be4a68c", "is_lead": false, "lead": "fa9bd89576a09a2f", "linked_by": [ { "rule": "shared-words", "with": "485ab00fad3938da", "words": [ "demand", "driven", "order", "strong" ] } ], "members": [ "2d852c2010ac8ff8", "485ab00fad3938da", "fa9bd89576a09a2f" ], "severity": 0, "source_count": 3, "sources": [ "hn-ai", "techmeme", "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-15T04:30:00.000Z", "score": 37.4, "source": "wires-ai", "summary": "Financial Times, via Google News.", "title": "Highest QUarterly order intake and sales ever as strong AI and technology driven semiconductor demand continues", "url": "https://news.google.com/rss/articles/CBMipAFBVV95cUxOUjBzTmtqZDJ3YzExbnk4LVdzTWh1Q0YwaU1ndjM1UUlCa0RWaUVUVXplRm1ja25BRnNqX0xPbGRsemNaaDVBNzBhcHFxZTRRazdDZmh5ZklSYTAzR1pIR3pXVkFuXzhIMGU0UXJkb09GdmlWeElkaWpjXzhzYVVNNVZ2TExqN0ZpRzl5YTFqak5uLUotN2xOMk5rbWI1YjgxeU42QQ?oc=5" }, { "entities": [], "id": "fb636bd2a821d7ca", "kind": "forum", "meta": { "age_hours_at_score": 4, "categories": [ "ControlProblem" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T04:24:37.000Z", "first_source": "reddit-controlproblem", "last_published_at": "2026-10-15T04:24:37.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T04:24:37.000Z", "sources": [ "reddit-controlproblem" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/ControlProblem/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T07:58:20.287Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 18.5, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-15T04:24:37.000Z", "score": 27.3, "source": "reddit-controlproblem", "summary": "submitted by /u/MoralLogs [link] [comments]", "title": "Hardware-Enforced AI Governance Architecture", "url": "https://reddit.com/r/ControlProblem/comments/1x6c830/hardwareenforced_ai_governance_architecture" }, { "entities": [], "id": "3c2290fb034839df", "kind": "press", "meta": { "age_hours_at_score": 4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T04:24:24.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-15T04:24:24.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T04:24:24.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 6, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 18.5, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "d6607eb5558d", "is_lead": false, "lead": "968c5305dd53ff7c", "linked_by": [ { "rule": "shared-words", "with": "968c5305dd53ff7c", "words": [ "gary", "marcu", "nyu" ] } ], "members": [ "3c2290fb034839df", "968c5305dd53ff7c" ], "severity": 0, "source_count": 1, "sources": [ "bloomberg-tech" ] } }, "pillar": "markets", "published_at": "2026-10-15T04:24:24.000Z", "score": 33.5, "source": "bloomberg-tech", "summary": "Gary Marcus, Emeritus Professor of Psychology & Neural Science at New York University, discusses why he thinks China and the US will end up in \"a tie\" in the AI race and how the technology can be made safer. He speaks on the sidelines of the Bloomberg New Economy Forum in New Delhi. (Source: Bloomberg)", "title": "NYU Professor Gary Marcus on AI Safety", "url": "https://bloomberg.com/news/videos/2026-10-15/nyu-professor-gary-marcus-on-ai-safety-video" }, { "entities": [ "ASML" ], "id": "5a7215a75cf6c015", "kind": "press", "meta": { "age_hours_at_score": 4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T04:20:04.000Z", "first_source": "techmeme", "last_published_at": "2026-10-15T04:20:04.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T04:20:04.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [ "ASML" ], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 5, "engagement": 0, "entities": 3, "recency": 18.5, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": { "id": "b7c3ea3d57bc", "is_lead": true, "lead": "5a7215a75cf6c015", "linked_by": [ { "rule": "shared-words", "with": "0bfb01aa4d34c1b5", "words": [ "asml", "china", "sal" ] } ], "members": [ "0bfb01aa4d34c1b5", "5a7215a75cf6c015" ], "severity": 0, "source_count": 2, "sources": [ "scmp-tech", "techmeme" ] } }, "pillar": "attention", "published_at": "2026-10-15T04:20:04.000Z", "score": 44, "source": "techmeme", "summary": "Xiaofei Xu / South China Morning Post : ASML expects China to account for ~20% of total sales this year, after Q3 sales there surged 109% QoQ, lifting China's share of machine sales from 14% to 23% — Dutch semiconductor equipment giant ASML expects China to account for about 20 per cent of its total sales this year …", "title": "ASML expects China to account for ~20% of total sales this year, after Q3 sales there surged 109% QoQ, lifting China's share of machine sales from 14% to 23% (Xiaofei Xu/South China Morning Post)", "url": "https://techmeme.com/261015/p2" }, { "entities": [], "id": "5261a04adb95ef8c", "kind": "press", "meta": { "age_hours_at_score": 4.1, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": "huffpost.com", "published_at": "2026-10-15T04:52:29.000Z", "source": "wires-ai", "title": "Trump Says Advanced Defense Test Range To Be Located In Montana", "url": "https://news.google.com/rss/articles/CBMie0FVX3lxTFBxZThxQVZoSU5lZmVyNVhoTFJ4Z0llVU9PcDhVTWVWempEVDl0bTQtTjd2akZCdjFFLXlrTVd6eHVFbmN4QXNmRzBvVERHSGZ5NUphX29LcUNYRWQ5Y1RqR2ZDU3JuRXI3TFRQTkV5QVF1clQ5eGdfQ2t0a9IBgAFBVV95cUxOaHUwcnhDaXJCUE9yZVktemdqNWtRVDZDeVk5RE5xRnJ0aDZOX3J5LUtNaTQ2Ry1vSEF0ZXdxUk1zVzBka1V4QUxiX0tVNlZRcXFieHdiUnlfejRLa2MzZENJdzBMYldfOWF5ZnJRZnc0Q2VKdkI2NnBHLTAtNTdNYw?oc=5", "weight": 0.75 }, { "dedup_key": null, "engagement": null, "kind": "press", "outlet": "kelo.com", "published_at": "2026-10-15T04:18:48.000Z", "source": 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"wires-ai", "summary": "WSJ, via Google News.", "title": "AI Borrowing Adds Pressure to Asia’s Bond Markets", "url": "https://news.google.com/rss/articles/CBMiekFVX3lxTFBIcEdZX0dPYzl4bDhWcGw2cmJsY1JVRGJnY1I3T2ZPUGlMWjh5NER0dFNHcVdOaGFHQzBkdnF5Q0tvejNtRkVMMmZUSW5wMjdjdkpoX0N3OE04dE1OYmRzUk94NnA2bUdXRkhJVGdGX2U2ZWpZM0JYRGdR?oc=5" }, { "entities": [ "Goldman Sachs" ], "id": "3b370ee92cd9578b", "kind": "press", "meta": { "age_hours_at_score": 4.2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T04:10:04.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-15T04:10:04.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T04:10:04.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "Goldman Sachs" ], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 6, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 18.4, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "markets", "published_at": "2026-10-15T04:10:04.000Z", "score": 36.4, "source": "bloomberg-tech", "summary": "A sharp selloff across Asian semiconductor and AI hardware stocks is pushing investors to look past short-term panic toward China. Goldman Sachs Chief APAC Regional Equity Strategist Timothy Moe and Bloomberg's Anthony Stephens discuss. 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0, "engagement": 0, "entities": 3, "recency": 18.4, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": { "id": "ce8b11d9deae", "is_lead": false, "lead": "c8e74cdda34787ec", "linked_by": [ { "rule": "shared-words", "with": "0a55943b50812b3d", "words": [ "mount", "street", "wall" ] }, { "rule": "shared-words", "with": "4e77036ce45202de", "words": [ "fell", "samsung", "whil" ] } ], "members": [ "0a55943b50812b3d", "1d21ad3b85d15530", "210621f9ac5bb73c", "4e77036ce45202de", "63901e460b8fbba9", "79bd3ba482daff2d", "85f25bcb68531441", "9abd2667a185bf33", "c8e74cdda34787ec", "db7de00c48010628", "e07f6f4e87fe86b1", "e2fa9544501179ca", "e3e32d7d473c157c", "e5adffa73aaf9a9c", "f7fdca7cbe7c0131" ], "severity": 0, "source_count": 6, "sources": [ "arxiv-newest", "bloomberg-tech", "bluesky-ai", "gnews-ai", "techmeme", "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-15T04:05:01.000Z", "score": 38.9, "source": "techmeme", "summary": "Lee Ying Shan / CNBC : Asian chipmakers extend Wall Street losses as US Fed rate hike fears and AI funding worries mount; SK Hynix fell 10%+ on the KRX, while Samsung fell ~10% — A sell-off in global technology hardware stocks deepened Thursday, with Asian chipmakers tracking Wall Street losses as concerns …", "title": "Asian chipmakers extend Wall Street losses as US Fed rate hike fears and AI funding worries mount; SK Hynix fell 10%+ on the KRX, while Samsung fell ~10% (Lee Ying Shan/CNBC)", "url": "https://techmeme.com/261015/p1" }, { "entities": [], "id": "9d8e8245b6ef30a4", "kind": "press", "meta": { "age_hours_at_score": 4.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T04:02:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T04:02:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T04:02:00.000Z", "sources": [ "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", 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The rise of AI surveillance at work – podcast", "url": "https://theguardian.com/science/audio/2026/oct/15/is-your-boss-watching-you-the-rise-of-ai-surveillance-at-work-podcast" }, { "entities": [], "id": "607c6dc61173b75c", "kind": "press", "meta": { "age_hours_at_score": 4.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T04:00:00.000Z", "first_source": "ft-ai", "last_published_at": "2026-10-15T04:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T04:00:00.000Z", "sources": [ "ft-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.ft.com/artificial-intelligence?format=rss", "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 18.4, "severity": 0, "source_weight": 16.3 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"2026-10-15T03:54:09.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-15T03:54:09.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T03:54:09.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50098937", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 13 }, "entity_kinds": { "labs": [ "Meta AI" ], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "hn_id": "50098937", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "min_points": 8, "pillar_reason": "source default", "points": 13, "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 7.6, "entities": 3, "recency": 18.4, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "window_hours": 72 }, "pillar": "attention", "published_at": "2026-10-15T03:54:09.000Z", "score": 44, "source": "hn-ai", "summary": "13 points, 2 comments on Hacker News.", "title": "Meta AI creates campaign material for Flávio Bolsonaro and blocks Lula content", "url": "https://www1.folha.uol.com.br/internacional/en/brazil/2026/10/meta-ai-creates-campaign-material-for-flavio-bolsonaro-and-blocks-lula-content.shtml" }, { "entities": [ "ByteDance" ], "id": "8f7aea038b16439c", "kind": "press", "meta": { "age_hours_at_score": 4.6, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T03:45:01.000Z", "first_source": "techmeme", "last_published_at": "2026-10-15T03:45:01.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T03:45:01.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [ "ByteDance" ], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "hardware/capital keyword", "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 18.3, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": { "id": "d79fb1f90367", "is_lead": false, "lead": "ca3bf36fb5385aa7", "linked_by": [ { "rule": "shared-words", "with": "429bd414c3da3733", "words": [ "buy", "bytedanc", "center", "mediatek" ] } ], "members": [ "429bd414c3da3733", "8f7aea038b16439c", "ca3bf36fb5385aa7" ], "severity": 0, "source_count": 4, "sources": [ "bloomberg-tech", "gnews-ai", "techmeme", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-15T03:45:01.000Z", "score": 38.8, "source": "techmeme", "summary": "Bloomberg : Sources: ByteDance is in advanced talks to buy data center ASICs from MediaTek, potentially becoming its second major customer for such chips after Google — ByteDance Ltd. is close to an agreement to buy MediaTek Inc. data center semiconductors, a deal that would secure vital components …", "title": "Sources: ByteDance is in advanced talks to buy data center ASICs from MediaTek, potentially becoming its second major customer for such chips after Google (Bloomberg)", "url": "https://techmeme.com/261014/p57" }, { "entities": [], "id": "503be02b143344f6", "kind": "press", "meta": { "age_hours_at_score": 4.7, "corroboration": { "also": [ { "dedup_key": null, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 1 }, "kind": "forum", "outlet": "bloomberg.com", "published_at": "2026-10-15T03:40:07.000Z", "source": "bluesky-ai", "title": "As governments and companies race to expand AI capabilities, how will India wield its growing weight and influence in the reordered world? Bloomberg journalists answer your questions in a Live Q&A at", "url": "https://bsky.app/profile/bloomberg.com/post/3mxv5awxx2w2v", "weight": 0.6 } ], "count": 2, "first_seen_published_at": "2026-10-15T03:40:07.000Z", "first_source": "bluesky-ai", "last_published_at": "2026-10-15T07:35:30.000Z", "lead_minutes": 235, "primary_published_at": "2026-10-15T07:35:30.000Z", "sources": [ "bluesky-ai", "wires-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 1 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T03:13:58.488Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "bloomberg.com", "pillar_reason": "source default", "publisher": "Bloomberg.com", "publisher_url": "https://www.bloomberg.com", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 5, "engagement": 1.6, "entities": 0, "recency": 18.3, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": { "id": "ad4350870e7d", "is_lead": true, "lead": "503be02b143344f6", "linked_by": [ { "rule": "shared-words", "with": "f572f7deb5faa75d", "words": [ "age", "india", "liv" ] } ], "members": [ "503be02b143344f6", "9575be1071f6749e", "a4e4af50bbf19bf5", "f572f7deb5faa75d" ], "severity": 0, "source_count": 3, "sources": [ "bloomberg-tech", "bluesky-ai", "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-15T03:40:07.000Z", "score": 56.2, "source": "wires-ai", "summary": "Bloomberg.com, via Google News.", "title": "Live Q&A: How Will India Wield Its Growing Influence in the Age of AI?", "url": "https://news.google.com/rss/articles/CBMiugFBVV95cUxNUlFxOTI0YWNWTTRjbDRuck9rWjA2UGRrS3lKempQRTUwSWVpZnItUktjVWwwQmVrR0FUOFNJVk80YVh6ZFdQTzYybG9uQnJ2NmJvSll1RjZlQmVYVlRMNTdvd3RQX2ZoZ1dYY25PLVZ4M211a0oxQjZJMmVyNVV6ZnZsa3JqNncxSTd3U3k2Qk9QUWlTMnotTk55emlQcFZ2aVBmdHJoc09Fei16RDEwMHM1NVYwZ2VfYlE?oc=5" }, { "entities": [ "Microsoft" ], "id": "599baa025647e675", "kind": "press", "meta": { "age_hours_at_score": 4.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T03:35:01.000Z", "first_source": "techmeme", "last_published_at": "2026-10-15T03:35:01.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T03:35:01.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [ "Microsoft" ], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-15T03:45:39.508Z", "incident": { "band": "B", "excluded": null, "severity": 0.6, "strong_signal": false, "terms": [ { "field": "title", "matched": "phishing", "term": "phishing", "tier": "b", "weight": 0.6 }, { "field": "summary", "matched": "phishing", "term": "phishing", "tier": "b", "weight": 0.21 } ] }, "locally_ai_filtered": true, "pillar_reason": "hardware/capital keyword", "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 18.2, "severity": 9, "source_weight": 17.5 }, "source_weight": 0.7, "story": null }, "pillar": "compute", "published_at": "2026-10-15T03:35:01.000Z", "score": 47.7, "source": "techmeme", "summary": "Matt Kapko / CyberScoop : Microsoft, Health-ISAC, Japanese authorities, and others disrupt a Chinese-language phishing kit ecosystem used to send 1B+ messages to 75K+ orgs this year — In less than 10 months, cybercriminals used the arsenal of customized, targeted phishing kits to send more than 1 billion malicious…", "title": "Microsoft, Health-ISAC, Japanese authorities, and others disrupt a Chinese-language phishing kit ecosystem used to send 1B+ messages to 75K+ orgs this year (Matt Kapko/CyberScoop)", "url": "https://techmeme.com/261014/p55" }, { "entities": [ "Adobe" ], "id": "9e4f46fe14535bab", "kind": "forum", "meta": { "age_hours_at_score": 5, "comments": 5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T03:20:32.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-15T03:20:32.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T03:20:32.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50098712", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 3 }, "entity_kinds": { "labs": [ "Adobe" ], "models": [] }, "first_seen_at": "2026-10-15T03:45:39.508Z", "hn_id": "50098712", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "ycombinator.com", "pillar_reason": "source default", "points": 3, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 4, "entities": 3, "recency": 18.2, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-15T03:20:32.000Z", "score": 36.5, "source": "hn-fresh-ai", "summary": "New on Hacker News: 3 points, 5 comments so far.", "title": "Will big software companies (like Adobe) come together to curb AI?", "url": "https://news.ycombinator.com/item?id=50098712" }, { "entities": [], "id": "f572f7deb5faa75d", "kind": "press", "meta": { "age_hours_at_score": 5.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T03:04:21.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-15T03:04:21.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T03:04:21.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-15T03:13:58.488Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 6, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 18.1, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "ad4350870e7d", "is_lead": false, "lead": "503be02b143344f6", "linked_by": [ { "rule": "shared-words", "with": "503be02b143344f6", "words": [ "age", "india", "liv" ] }, { "rule": "shared-words", "with": "9575be1071f6749e", "words": [ "economy", "forum", "world" ] }, { "rule": "shared-words", "with": "a4e4af50bbf19bf5", "words": [ "bloomberg", "economy", "forum" ] } ], "members": [ "503be02b143344f6", "9575be1071f6749e", "a4e4af50bbf19bf5", "f572f7deb5faa75d" ], "severity": 0, "source_count": 3, "sources": [ "bloomberg-tech", "bluesky-ai", "wires-ai" ] } }, "pillar": "markets", "published_at": "2026-10-15T03:04:21.000Z", "score": 33.1, "source": "bloomberg-tech", "summary": "As governments and companies expand AI capabilities, how will India wield its growing influence? Bloomberg journalists answer your questions on October 15, 2026 at 5 p.m. IST.", "title": "India and the World in the Age of AI: Bloomberg New Economy Forum Live Q&A", "url": "https://bloomberg.com/sessions/2026-10-15/live-q-amp-a-how-will-india-wield-its-growing-influence-in-the-age-of-ai" }, { "entities": [], "id": "07b644e125e11a89", "kind": "press", "meta": { "age_hours_at_score": 5.3, "corroboration": { "also": [ { "dedup_key": null, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 18 }, "kind": "forum", "outlet": "bloomberg.com", "published_at": "2026-10-15T06:13:19.000Z", "source": "bluesky-ai", "title": "AI Boom Risks Diverting Capital From Australia’s Renewables Push", "url": "https://bsky.app/profile/bloomberg.com/post/3mxvfsusqfv2v", "weight": 0.6 } ], "count": 2, "first_seen_published_at": "2026-10-15T03:03:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T06:13:19.000Z", "lead_minutes": 190, "primary_published_at": "2026-10-15T03:03:00.000Z", "sources": [ "bluesky-ai", "wires-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 18 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T03:45:39.508Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "bloomberg.com", "pillar_reason": "source default", "publisher": "Bloomberg.com", "publisher_url": "https://www.bloomberg.com", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 6.6, "entities": 0, "recency": 18.1, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T03:03:00.000Z", "score": 56, "source": "wires-ai", "summary": "Bloomberg.com, via Google News.", "title": "AI Boom Risks Diverting Capital From Australia’s Renewables Push", "url": "https://news.google.com/rss/articles/CBMitgFBVV95cUxNcV91a3RXZE1OVFNUTUVIMlVyWDdNVWV4NzEwbkNjbVAweXljc1ZYRTFrRGRxYkFxSXZ4Z3FhdUdDS19PaUdNVk1IQWpOZEJXaGRUNnhzc2l6LVU2RWtfcDhuLTdFR2FwVVhMMERVZE04S2YydFA1Z01jTDhVdEc0b01NWFRoNHFvbEJ6N283S05ENUxNUUhXMHNGUlFWd0pMa29zOEs0WGtjWVpZeVF3ZlBMWVdRUQ?oc=5" }, { "entities": [ "ByteDance" ], "id": "ca3bf36fb5385aa7", "kind": "press", "meta": { "age_hours_at_score": 5.7, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-15T02:39:11.000Z", "source": "bloomberg-tech", "title": "ByteDance in Talks to Secure MediaTek AI Chips for Data Centers", "url": "https://bloomberg.com/news/articles/2026-10-15/bytedance-in-talks-to-secure-mediatek-ai-chips-for-data-centers", "weight": 0.6 } ], "count": 2, "first_seen_published_at": "2026-10-15T02:39:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T02:39:11.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T02:39:00.000Z", "sources": [ "bloomberg-tech", "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "ByteDance" ], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "bloomberg.com", "pillar_reason": "hardware/capital keyword", "publisher": "Bloomberg.com", "publisher_url": "https://www.bloomberg.com", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 10, "engagement": 0, "entities": 3, "recency": 17.9, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": { "id": "d79fb1f90367", "is_lead": true, "lead": "ca3bf36fb5385aa7", "linked_by": [ { "rule": "shared-words", "with": "429bd414c3da3733", "words": [ "bytedanc", "center", "mediatek" ] } ], "members": [ "429bd414c3da3733", "8f7aea038b16439c", "ca3bf36fb5385aa7" ], "severity": 0, "source_count": 4, "sources": [ "bloomberg-tech", "gnews-ai", "techmeme", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-15T02:39:00.000Z", "score": 62.2, "source": "wires-ai", "summary": "Bloomberg.com, via Google News.", "title": "ByteDance in Talks to Secure MediaTek AI Chips for Data Centers", "url": "https://news.google.com/rss/articles/CBMitAFBVV95cUxNaFlzZWE3cWRERDdpeHZBaFdCbFEtYkpCOUFaamFJNURYQmNVWExxWnQ2NmNHRUIwXzR1dFgzaVhOUnZkS25yQk1Sd3VvTDFTQnFlSlN5YzI0Q210bGVuS0NhZkFFRDJvU08wSjRqNEdxZXhtVVhYZTRaTGEybmxBWjgxUGNKMjZoclhLTDFSYnFGQlluaTdabW1hQjV4a1RORTRUbkxvdjFUQ256c2JGR0pvcnk?oc=5" }, { "entities": [ "GPT" ], "id": "42b7a7b4e64a02f9", "kind": "forum", "meta": { "age_hours_at_score": 5.7, "comments": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T02:38:43.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-15T02:38:43.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T02:38:43.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50098480", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 3 }, "entity_kinds": { "labs": [], "models": [ "GPT" ] }, "first_seen_at": "2026-10-15T03:45:39.508Z", "hn_id": "50098480", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "ycombinator.com", "pillar_reason": "source default", "points": 3, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 4, "entities": 3, "recency": 17.9, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-15T02:38:43.000Z", "score": 36.2, "source": "hn-fresh-ai", "summary": "New on Hacker News: 3 points, 0 comments so far.", "title": "GPT-5.6 Sol has gone full potato again", "url": "https://news.ycombinator.com/item?id=50098480" }, { "entities": [ "OpenAI" ], "id": "b30178819836d6f4", "kind": "release", "meta": { "age_hours_at_score": 5.7, "basket_failed": [], "basket_live": 9, "basket_size": 9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T02:37:43.000Z", "first_source": "github-releases", "last_published_at": "2026-10-15T02:37:43.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T02:37:43.000Z", "sources": [ "github-releases" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "release_tag": "v0.24.0", "repo": "openai/openai-agents-python", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 17.9, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "capability", "published_at": "2026-10-15T02:37:43.000Z", "score": 39.7, "source": "github-releases", "summary": "0.24.0 (2026-10-15) Features expose MCP annotations on converted tools ( #5356 ) ( 75813f8 ) support run-wide tool exception formatting ( #5326 ) ( 8fd9a7c ) Bug Fixes batch OpenAI conversation session writes within the API limit ( #5334 ) ( 26345c1 ) defer automatic compaction for pending approvals ( #5363 ) (…", "title": "openai/openai-agents-python v0.24.0", "url": "https://github.com/openai/openai-agents-python/releases/tag/v0.24.0" }, { "entities": [ "Tencent" ], "id": "a67d589bf7547554", "kind": "forum", "meta": { "age_hours_at_score": 5.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T02:31:37.000Z", "first_source": "lesswrong", "last_published_at": "2026-10-15T02:31:37.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T02:31:37.000Z", "sources": [ "lesswrong" ] }, "engagement": null, "entity_kinds": { "labs": [ "Tencent" ], "models": [] }, "feed": "https://www.lesswrong.com/feed.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": "A", "excluded": null, "severity": 1, "strong_signal": false, "terms": [ { "field": "title", "matched": "hacking", "term": "hacking", "tier": "a", "weight": 1 }, { "field": "summary", "matched": "hacking", "term": "hacking", "tier": "a", "weight": 0.35 }, { "field": "summary", "matched": "leaking", "term": "leaking", "tier": "b", "weight": 0.21 } ] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 2, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 17.9, "severity": 15, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "governance", "published_at": "2026-10-15T02:31:37.000Z", "score": 48.4, "source": "lesswrong", "summary": "TL;DR agents seem to still be bypassing site paywalls, browser restrictions and leaking benchmark questions as of October 14 2026! I have not found any evidence of them actually hacking or coordinating. this is distinct from the other swarm fleet report released today regarding Tencent. Background urlquery.net is a…", "title": "someone's agents are still \"hacking\"", "url": "https://lesswrong.com/posts/qxaNEpRAmjoJwJ7oF/someone-s-agents-are-still-hacking" }, { "entities": [ "GPT" ], "id": "83ad08240974d6f7", "kind": "forum", "meta": { "age_hours_at_score": 5.8, "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T02:31:28.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-15T02:31:28.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T02:31:28.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50098439", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 4 }, "entity_kinds": { "labs": [], "models": [ "GPT" ] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "hn_id": "50098439", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "kittygpt.org", "pillar_reason": "source default", "points": 4, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 4.7, "entities": 3, "recency": 17.9, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-15T02:31:28.000Z", "score": 36.9, "source": "hn-fresh-ai", "summary": "New on Hacker News: 4 points, 1 comments so far.", "title": "Kitty-GPT", "url": "https://kittygpt.org" }, { "entities": [], "id": "8823afa4b65217a8", "kind": "forum", "meta": { "age_hours_at_score": 5.9, "categories": [ "ControlProblem" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T02:31:16.000Z", "first_source": "reddit-controlproblem", "last_published_at": "2026-10-15T02:31:16.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T02:31:16.000Z", "sources": [ "reddit-controlproblem" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/ControlProblem/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T07:58:20.287Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 17.9, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-15T02:31:16.000Z", "score": 26.7, "source": "reddit-controlproblem", "summary": "submitted by /u/Single-Glove9030 [link] [comments]", "title": "Artificial Dichotomy: Beyond the All or Nothing Approach to Regulating Artificial Intelligence", "url": "https://reddit.com/r/ControlProblem/comments/1x6a3zh/artificial_dichotomy_beyond_the_all_or_nothing" }, { "entities": [], "id": "f8011c73e19ba2b3", "kind": "forum", "meta": { "age_hours_at_score": 5.9, "comments": 0, "corroboration": { "also": [], 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40.9, "source": "hn-ai", "summary": "15 points, 0 comments on Hacker News.", "title": "Asia chip stocks fall, AI funding worries", "url": "https://cnbc.com/2026/10/15/sk-hynix-samsung-softbank-stocks-asia-tech-rout-ai-funding-worries.html" }, { "entities": [ "OpenAI" ], "id": "1d9b0f2a4e47bd83", "kind": "press", "meta": { "age_hours_at_score": 5.9, "categories": [ "Abdulrahman Alfozan", "Artificial Intelligence", "Computers and the Internet", "Engineering and Engineers", "OpenAI Labs", "Politics and Government", "Royal Families", "Saudi Arabia", "Start-ups", "Venture Capital" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T02:27:52.000Z", "first_source": "nyt-tech", "last_published_at": "2026-10-15T02:27:52.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T02:27:52.000Z", "sources": [ "nyt-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "feed": 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"https://news.google.com/rss/articles/CBMilgFBVV95cUxQVGRiOFctY25wbVdWaHlwSFlRWF8teC1LV0M2U2ZNbVR1OG55OWlPYWFsM1pJMG5IV3FueXFPNVBJQ3BkN2t5QkY3SjRZWWZweVJEUzFydHJhUTJhdmhZMUN6b2U0THk0TW5tQVRjanFJZkpGdTJzUGtQNXlVZXppQ2JNRGhmZ1JRejFCdnQyNEVUVEJnbkE?oc=5" }, { "entities": [ "DeepSeek", "DeepSeek-R" ], "id": "0d2df041bfbc69fe", "kind": "forum", "meta": { "age_hours_at_score": 6, "comments": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T02:20:16.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-15T02:20:16.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T02:20:16.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50098365", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 4 }, "entity_kinds": { "labs": [ "DeepSeek" ], "models": [ "DeepSeek-R" ] }, "first_seen_at": "2026-10-15T03:45:39.508Z", "hn_id": "50098365", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "deepseek.com", "pillar_reason": "source default", "points": 4, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 4.7, "entities": 6, "recency": 17.8, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-15T02:20:16.000Z", "score": 39.8, "source": "hn-fresh-ai", "summary": "New on Hacker News: 4 points, 0 comments so far.", "title": "DeepSeek Outage", "url": "https://status.deepseek.com/incidents/7116348214287" }, { "entities": [], "id": "9a3f689a63892345", "kind": "press", "meta": { "age_hours_at_score": 6, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T02:20:02.000Z", "first_source": "techmeme", "last_published_at": "2026-10-15T02:20:02.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T02:20:02.000Z", "sources": [ "techmeme" ] }, 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"7dfb02a8d7db1976", "9a3f689a63892345" ], "severity": 0, "source_count": 4, "sources": [ "bloomberg-tech", "hn-ai", "nyt-tech", "techmeme" ] } }, "pillar": "governance", "published_at": "2026-10-15T02:20:02.000Z", "score": 50.3, "source": "techmeme", "summary": "Kashmir Hill / New York Times : Letter: Democratic senators question Meta's $6.6B in federal tax credits claimed over the past two years toward costs of building “experimental” AI data centers — Democrats on the Senate Finance Committee sent a letter to Mark Zuckerberg, Meta's chief executive, questioning the…", "title": "Letter: Democratic senators question Meta's $6.6B in federal tax credits claimed over the past two years toward costs of building \"experimental\" AI data centers (Kashmir Hill/New York Times)", "url": "https://techmeme.com/261014/p53" }, { "entities": [ "OpenAI" ], "id": "3985ae25e3ef1633", "kind": "forum", "meta": { "age_hours_at_score": 6.4, "comments": 0, "corroboration": { "also": [], "count": 1, 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"with": "f0aeaf09406aa3eb", "words": [ "openai", "stat", "sue" ] } ], "members": [ "314164cb6dc66c59", "3985ae25e3ef1633", "8a32126259e22cff", "8b16dc6b17676a97", "afdd4bd9efa8609e", "f0aeaf09406aa3eb" ], "severity": 0.3, "source_count": 7, "sources": [ "bluesky-ai", "gnews-ai", "guardian-ai", "hn-fresh-ai", "techmeme", "verge-ai", "wires-ai" ] }, "window_hours": 2 }, "pillar": "governance", "published_at": "2026-10-15T01:58:22.000Z", "score": 36, "source": "hn-fresh-ai", "summary": "New on Hacker News: 3 points, 0 comments so far.", "title": "Bipartisan coalition of states sues OpenAI over user safety", "url": "https://politico.com/news/2026/10/14/openai-user-safety-lawsuits-01119138" }, { "entities": [], "id": "e30a1a595a101f7f", "kind": "press", "meta": { "age_hours_at_score": 6.4, "corroboration": { "also": [ { "dedup_key": null, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 53 }, "kind": "forum", "outlet": "apnews.com", "published_at": "2026-10-15T02:02:41.000Z", "source": "bluesky-ai", "title": "South Korea’s benchmark Kospi index sinks more than 7% as tech sell-off deepens", "url": "https://bsky.app/profile/apnews.com/post/3mxuxspmo7y2p", "weight": 0.6 } ], "count": 2, "first_seen_published_at": "2026-10-15T01:57:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T02:02:41.000Z", "lead_minutes": 6, "primary_published_at": "2026-10-15T01:57:00.000Z", "sources": [ "bluesky-ai", "wires-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 53 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "apnews.com", "pillar_reason": "source default", "publisher": "AP News", "publisher_url": "https://apnews.com", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 9, "entities": 0, "recency": 17.7, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T01:57:00.000Z", "score": 58, "source": "wires-ai", "summary": "AP News, via Google News.", "title": "South Korea’s benchmark Kospi index sinks more than 7% as tech sell-off deepens", "url": "https://news.google.com/rss/articles/CBMilgFBVV95cUxOOWhsa2ZhTGZKbFhXckYzcXZGbDZoeUxRZXZlbEtSU1ZkOHZndkpDSWdqQUZZTmJDdHNHUlFHSXlRRm1zVUNKVFlXVEFSNW50Qk9vR29MazU0a3laNlJUR1ZBMlFlUGlWTl9VSk9CSnRUQTZvWV9INUNOLXdOeUtBdGpWaGo0YzNSeVVlMDlZQlRLQzV3TEE?oc=5" }, { "entities": [ "Anthropic" ], "id": "08b2cdffdbe6bf48", "kind": "press", "meta": { "age_hours_at_score": 6.6, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": "irishtimes.com", "published_at": "2026-10-15T07:15:32.000Z", "source": "gnews-ai", "title": "Anthropic’s Q3 revenue surges 45% to nearly $17bn", "url": 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"coverage": 0, "engagement": 0, "entities": 3, "recency": 17.6, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": { "id": "f27ed1f158bf", "is_lead": false, "lead": "2bb464d7faf82e22", "linked_by": [ { "rule": "shared-words", "with": "f47237ff75e651b0", "words": [ "anthropic", "nearly", "revenu" ] } ], "members": [ "08b2cdffdbe6bf48", "2bb464d7faf82e22", "41d835035e2a01e4", "45e05b50f5cecac5", "4c31c7cec80ac7f6", "6c340f3efbdbf860", "6f76a14503cf2093", "7604567454cfc303", "98842f779147e049", "dcf332e0d23bb416", "e36d56a8a8a62f0d", "f47237ff75e651b0" ], "severity": 0, "source_count": 9, "sources": [ "bbc-tech", "bloomberg-tech", "bluesky-ai", "ft-ai", "gnews-ai", "hn-fresh-ai", "nyt-tech", "techmeme", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-15T01:44:00.000Z", "score": 51.9, "source": "wires-ai", "summary": "The Telegraph, via Google News.", "title": "Anthropic in profit as revenue surges nearly $17bn", "url": "https://news.google.com/rss/articles/CBMinwFBVV95cUxPSGozcFdCcUlaUVNWZUFaXzMtdmNlUVhPb0VfUU9UTUdFQmRrejF5YjU3REVDYWt0WjllQmdGNk5xYnBIY0JHQmFESWZJbjBFb2FiVWJVWkJ6VGU4dHhfSW5hWUI5U29IS0xtNVNhRWE1S2N0cVNyNDQzYWdVVnFnVU8yUTRrc1JGbkZEbWJReXNxeHM3WkNmRW9USXNTZnM?oc=5" }, { "entities": [ "Anthropic", "OpenAI" ], "id": "f39b37681f93488d", "kind": "forum", "meta": { "age_hours_at_score": 7, "categories": [ "ControlProblem" ], "corroboration": { "also": [ { "dedup_key": null, "engagement": { "full_scale": 500, "metric": "hn_points", "value": 12 }, "kind": "forum", "outlet": null, "published_at": "2026-10-15T01:22:03.000Z", "source": "hn-ai", "title": "AI Safety Is Not a Corporate Plot", "url": "https://astralcodexten.com/p/ai-safety-is-not-a-corporate-plot", "weight": 0.6 }, { "dedup_key": null, "engagement": null, "kind": "forum", "outlet": null, "published_at": "2026-10-15T06:46:58.000Z", "source": "reddit-controlproblem", "title": "AI Safety Is Not A Corporate Plot", "url": "https://reddit.com/r/ControlProblem/comments/1x6elny/ai_safety_is_not_a_corporate_plot", "weight": 0.35 } ], "count": 2, "first_seen_published_at": "2026-10-15T01:22:03.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-15T06:46:58.000Z", "lead_minutes": 325, "primary_published_at": "2026-10-15T02:47:25.000Z", "sources": [ "hn-ai", "reddit-controlproblem" ] }, "engagement": { "full_scale": 500, "metric": "hn_points", "value": 12 }, "entity_kinds": { "labs": [ "Anthropic", "OpenAI" ], "models": [] }, "feed": "https://www.reddit.com/r/ControlProblem/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T03:54:17.295Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 7.4, "entities": 6, "recency": 17.5, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-15T01:22:03.000Z", "score": 52.2, "source": "reddit-controlproblem", "summary": "Are we too quick to assume AI safety warnings are just corporate PR? I get why people are skeptical when companies like OpenAI and Anthropic talk about AI risks. These are businesses, after all, and nobody should blindly trust a company just because it says it has good intentions. But I also think dismissing every AI…", "title": "AI Safety Is Not A Corporate Plot", "url": "https://reddit.com/r/ControlProblem/comments/1x6af8p/ai_safety_is_not_a_corporate_plot" }, { "entities": [], "id": "330343a7bdb3fdd1", "kind": "press", "meta": { "age_hours_at_score": 7.3, "categories": [ "AI", "Ricursive", "Ricursive Intelligence" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T01:01:32.000Z", "first_source": "techcrunch-ai", "last_published_at": "2026-10-15T01:01:32.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T01:01:32.000Z", "sources": [ "techcrunch-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://techcrunch.com/category/artificial-intelligence/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "hardware/capital keyword", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 17.4, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "compute", "published_at": "2026-10-15T01:01:32.000Z", "score": 32.4, "source": "techcrunch-ai", "summary": "The founders detailed how their startup is developing AI that can design chips, learn from each iteration, and improve subsequent designs, all with the goal of automating and accelerating the chip design process.", "title": "Ricursive Intelligence is betting on AI-designed chips to help solve the compute crisis", "url": "https://techcrunch.com/2026/10/14/ricursive-intelligence-is-betting-on-ai-designed-chips-to-help-solve-the-compute-crisis" }, { "entities": [ "Anthropic" ], "id": "45e05b50f5cecac5", "kind": "press", "meta": { "age_hours_at_score": 7.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:59:36.000Z", "first_source": "ft-ai", "last_published_at": "2026-10-15T00:59:36.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:59:36.000Z", "sources": [ "ft-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "feed": "https://www.ft.com/artificial-intelligence?format=rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "hardware/capital keyword", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 17.4, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": { "id": "f27ed1f158bf", "is_lead": false, "lead": "2bb464d7faf82e22", "linked_by": [ { "rule": "shared-words", "with": "98842f779147e049", "words": [ "anthropic", "jump", "revenu" ] } ], "members": [ "08b2cdffdbe6bf48", "2bb464d7faf82e22", "41d835035e2a01e4", "45e05b50f5cecac5", "4c31c7cec80ac7f6", "6c340f3efbdbf860", "6f76a14503cf2093", "7604567454cfc303", "98842f779147e049", "dcf332e0d23bb416", "e36d56a8a8a62f0d", "f47237ff75e651b0" ], "severity": 0, "source_count": 9, "sources": [ "bbc-tech", "bloomberg-tech", "bluesky-ai", "ft-ai", "gnews-ai", "hn-fresh-ai", "nyt-tech", "techmeme", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-15T00:59:36.000Z", "score": 36.7, "source": "ft-ai", "summary": "Rapid growth is slower than some anticipated as fears about AI trade rattle markets", "title": "Anthropic tells investors its quarterly revenue jumped 50%", "url": "https://ft.com/content/11632d03-358c-454b-af95-d06d4072932b?syn-25a6b1a6=1" }, { "entities": [ "GPT", "OpenAI" ], "id": "c014b7fb6843ed23", "kind": "forum", "meta": { "age_hours_at_score": 7.5, "comments": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:55:16.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-15T00:55:16.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:55:16.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50097790", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 4 }, "entity_kinds": { "labs": [ "OpenAI" ], "models": [ "GPT" ] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "hn_id": "50097790", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "cnet.com", "pillar_reason": "regulatory/legal keyword", "points": 4, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 4.7, "entities": 6, "recency": 17.3, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null, "window_hours": 2 }, "pillar": "governance", "published_at": "2026-10-15T00:55:16.000Z", "score": 39.3, "source": "hn-fresh-ai", "summary": "New on Hacker News: 4 points, 0 comments so far.", "title": "OpenAI Sued over Children's Data Used to Train ChatGPT", "url": "https://cnet.com/tech/services-and-software/chatgpt-accused-using-childrens-data" }, { "entities": [], "id": "238f5cf7d450448d", "kind": "press", "meta": { "age_hours_at_score": 7.5, "corroboration": { "also": [ { "dedup_key": null, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 5 }, "kind": "forum", "outlet": "reuters.com", "published_at": "2026-10-15T01:00:33.000Z", "source": "bluesky-ai", "title": "Asian markets sell off as AI trades crumble after hot US CPI data", "url": "https://bsky.app/profile/reuters.com/post/3mxuudmytqc2p", "weight": 0.6 } ], "count": 2, "first_seen_published_at": "2026-10-15T00:51:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T01:00:33.000Z", "lead_minutes": 10, "primary_published_at": "2026-10-15T00:51:00.000Z", "sources": [ "bluesky-ai", "wires-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 5 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "reuters.com", "pillar_reason": "source default", "publisher": "Reuters", "publisher_url": "https://www.reuters.com", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 4, "entities": 0, "recency": 17.3, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": { "id": "aa7cf63c3377", "is_lead": true, "lead": "238f5cf7d450448d", "linked_by": [ { "rule": "shared-words", "with": "e50c7e7da973aae2", "words": [ "asian", "market", "sell" ] } ], "members": [ "238f5cf7d450448d", "798c1644ec60a7eb", "e50c7e7da973aae2" ], "severity": 0, "source_count": 2, "sources": [ "bluesky-ai", "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-15T00:51:00.000Z", "score": 52.6, "source": "wires-ai", "summary": "Reuters, via Google News.", "title": "Asian markets sell off as AI trades crumble after hot US CPI data", "url": "https://news.google.com/rss/articles/CBMie0FVX3lxTE8zSHhMSkR3TXNQeHQwd0pHRXVZU3lzTjYzY2l0ZkVyMWVmbmczQWhqSzhGaEtBSWhKdW5lQ1VIeFI5Y0ZwUzE4dFFGWVRfUzE0Y0RaZlRkWEZSMUlleXp2OTBTd1FCU05vakU1QlA2X3hHcGl2UVk3SnhGUQ?oc=5" }, { "entities": [], "id": "144647f83aa6804e", "kind": "press", "meta": { "age_hours_at_score": 7.5, "categories": [ "APAC Newsroom" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:50:50.000Z", "first_source": "meta-news", "last_published_at": "2026-10-15T00:50:50.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:50:50.000Z", "sources": [ "meta-news" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://about.fb.com/news/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 4, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 17.3, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "capability", "published_at": "2026-10-15T00:50:50.000Z", "score": 32.3, "source": "meta-news", "summary": "New Meta research across nine APAC markets shows how AI is helping small businesses grow, compete with larger rivals and reach customers across borders. The post More Than Three Quarters of Small Business Owners Say AI Makes Them More Productive, But Most Need More Guidance appeared first on Meta Newsroom .", "title": "More Than Three Quarters of Small Business Owners Say AI Makes Them More Productive, But Most Need More Guidance", "url": "https://about.fb.com/news/2026/10/more-than-three-quarters-of-small-business-owners-say-ai-makes-them-more-productive-but-most-need-more-guidance" }, { "entities": [ "OpenAI" ], "id": "de2ec4083de14f91", "kind": "press", "meta": { "age_hours_at_score": 7.6, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:45:08.000Z", "first_source": "techmeme", "last_published_at": "2026-10-15T00:45:08.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:45:08.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": "C", "excluded": null, "severity": 0.3, "strong_signal": false, "terms": [ { "field": "title", "matched": "lawsuit", "term": "lawsuit", "tier": "c", "weight": 0.3 }, { "field": "summary", "matched": "lawsuit", "term": "lawsuit", "tier": "c", "weight": 0.11 } ] }, "locally_ai_filtered": true, "pillar_reason": "regulatory/legal keyword", "rejected_off_topic": 6, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 17.3, "severity": 4.5, "source_weight": 17.5 }, "source_weight": 0.7, "story": { "id": "f0b473571419", "is_lead": false, "lead": "66f5e62202fa6571", "linked_by": [ { "rule": "shared-words", "with": "043aba25a09a5b5c", "words": [ "appl", "claim", "employ", "openai" ] }, { "rule": "shared-words", "with": "47b1c61501a00890", "words": [ "appl", "openai", "secret", "trad" ] }, { "rule": "shared-words", "with": "6bec7d11ba5f3d7b", "words": [ "devic", "employ", "openai" ] }, { "rule": "shared-words", "with": "f67cff07b7fcf963", "words": [ "appl", "openai", "secret", "trad" ] } ], "members": [ "0343a01e6ce9dfac", "043aba25a09a5b5c", "18ff78a815c5e459", "1d9b0f2a4e47bd83", "2444a8e5a9bf9dd7", "2845c6266106c48a", "3666ba1e02facdbf", "4533e2b5df1cb4f7", "47b1c61501a00890", "4a54f7ed9060d0c2", "4e5e343645d9b16e", "66f5e62202fa6571", "6986f04e2795a0c1", "6bec7d11ba5f3d7b", "75aa40e09badce18", "82065bf620f89892", "bab59c5f7007dae1", "c666cbb353ec8719", "de2ec4083de14f91", "e3d70f1a671b390f", "f67cff07b7fcf963", "fdcf029b66aaffa6" ], "severity": 0.35, "source_count": 16, "sources": [ "arstechnica-ai", "bbc-tech", "bloomberg-tech", "bluesky-ai", "ft-ai", "gnews-ai", "guardian-ai", "hn-fresh-ai", "nyt-tech", "reddit-chatgpt", "techcrunch-ai", "techmeme", "the-decoder", "verge-ai", "wired-ai", "wires-ai" ] } }, "pillar": "governance", "published_at": "2026-10-15T00:45:08.000Z", "score": 42.3, "source": "techmeme", "summary": "Bloomberg : OpenAI agrees not to use trade secrets that Apple claims were stolen while their lawsuit plays out, and to collect forensic images of 14 employees' devices — OpenAI agreed not to use trade secrets that Apple Inc. claims were stolen by employees who defected from the iPhone maker …", "title": "OpenAI agrees not to use trade secrets that Apple claims were stolen while their lawsuit plays out, and to collect forensic images of 14 employees' devices (Bloomberg)", "url": "https://techmeme.com/261014/p50" }, { "entities": [ "CoreWeave" ], "id": "e1b2c3089289c742", "kind": "press", "meta": { "age_hours_at_score": 7.7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:38:40.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-15T00:38:40.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:38:40.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "CoreWeave" ], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 5, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 17.2, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "markets", "published_at": "2026-10-15T00:38:40.000Z", "score": 35.2, "source": "bloomberg-tech", "summary": "CoreWeave Chief Executive Officer Mike Intrator said the market is acting “irrationally” and that he remains confident in the AI cloud company’s business model. 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This perspective is a bit different from the traditional view that a state's only responsibility is to its own citizens,…", "title": "During a GLOBAL COLLAPSE, what kind of responsibility does global society have to prevent the suffering of the masses? 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"compute", "published_at": "2026-10-15T00:10:09.000Z", "score": 66.2, "source": "bluesky-ai", "summary": "Posted on Bluesky by @reuters.com: TSMC expected to report sixth straight quarter of record profit thanks to AI boom reut.rs/4B96UZB", "title": "TSMC expected to report sixth straight quarter of record profit thanks to AI boom", "url": "https://bsky.app/profile/reuters.com/post/3mxurjizk5h2x" }, { "entities": [ "Claude", "GPT" ], "id": "e61841fd619a778e", "kind": "forum", "meta": { "age_hours_at_score": 8.3, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:05:16.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-15T00:05:16.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:05:16.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Claude", "GPT" ] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", 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Recently, I have been using ChatGPT and am pretty impressed. I mostly use both for work. Do people subscribe to both (Pro) or just switch from one to the other? 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Prior work demonstrates that different loop visits can play different roles even when they share parameters. 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However, identifying and correctly applying domain-specific rules remains challenging when scenario information is distributed across multiple files. Existing rule-reasoning benchmarks typically represent scenarios using o", "title": "RuleTrek: Benchmarking Agentic Rule Reasoning in Complex Scenarios", "url": "https://huggingface.co/papers/2610.19698" }, { "entities": [], "id": "cf1e24ecc9500dce", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.19820", "arxiv_published_at": "2026-10-14T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 44 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:22:35.661Z", "github_repo": "https://github.com/thu-nics/ActionOPD", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 13.7, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 44 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 50.7, "source": "hf-daily-papers", "summary": "Distilling capable language-model agents into smaller models requires aligning the student's behavior with the teacher's. This raises a fundamental question: at which states and at what granularity should this alignment be performed? Offline imitation provides dense but token-specific supervision and can suffer from st", "title": "ActionOPD: Distilling Decisions, Not Tokens", "url": "https://huggingface.co/papers/2610.19820" }, { "entities": [], "id": "f06e4da304d537b8", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.20344", "arxiv_published_at": "2026-10-14T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 39 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T03:45:39.508Z", "github_repo": "https://github.com/YuyaoGe/doc2harness", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 13.2, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 39 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 50.2, "source": "hf-daily-papers", "summary": "Agents driven by large language models are increasingly deployed on long-horizon tasks, where they use tools and modify real workspaces. How well they perform depends as much on the harness around the model as on the model itself. Existing benchmarks measure downstream task success under a fixed harness, leaving harnes", "title": "Doc2Harness: Benchmarking LLMs on Long-Horizon Harness Engineering of Coding Agents", "url": "https://huggingface.co/papers/2610.20344" }, { "entities": [], "id": "f384dd3f0aacad37", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.19366", "arxiv_published_at": "2026-10-14T01:11:57.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 30 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "github_repo": "https://github.com/Seeing-Maintaining-Learning/multimodal-agent-memory-evolution", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 12.3, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 30 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 49.3, "source": "hf-daily-papers", "summary": "Multimodal models and agents have emerged to enable AI systems to learn from and interact with the real world more like humans by integrating information across multiple modalities. However, enabling multimodal agents to continually acquire useful information from complex, heterogeneous multimodal data remains a signif", "title": "Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory", "url": "https://huggingface.co/papers/2610.19366" }, { "entities": [], "id": "76f3c65e366c58cb", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.18072", "arxiv_published_at": "2026-10-13T09:56:02.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 27 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": "https://github.com/LensFamily/LensErase", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 12, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 27 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 49, "source": "hf-daily-papers", "summary": "We introduce LensErase, a general-purpose mask-based Erase Anything foundation model designed to cover a broad spectrum of real-world erase scenarios. LensErase supports diverse objects, visual text, watermarks, associated visual effects, multiple-object removal, and a wide range of object scales, aspect ratios, and im", "title": "LensErase: A Foundation Model for Erase Anything", "url": "https://huggingface.co/papers/2610.18072" }, { "entities": [], "id": "f9c94340803015aa", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.14448", "arxiv_published_at": "2026-10-10T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 26 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 11.8, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 26 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 48.8, "source": "hf-daily-papers", "summary": "Asynchronous rollout improves the efficiency of reinforcement learning with verifiable rewards (RLVR) by reducing under-utilization during long-tail generation. Sustained concurrency also concentrates context processing into fewer decode steps, increasing the KV workload per step. We characterize this KV concentration", "title": "KV4-RL: Unlocking Faster Asynchronous Rollouts for RLVR with INT4 KV Cache Quantization", "url": "https://huggingface.co/papers/2610.14448" }, { "entities": [], "id": "5902a8fb8079ffc5", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.15542", "arxiv_published_at": "2026-10-11T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 24 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 11.5, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 24 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 48.5, "source": "hf-daily-papers", "summary": "Language models are becoming infrastructure, and infrastructure built in the open, by public institutions, on documented data, is the alternative to dependence on a few providers; Apertus shows this is viable at scale. Apertus 1.5 extends the fully open Apertus family from text to multimodal reasoning. Continued pretra", "title": "Apertus 1.5: From Open Text Models to Open Multimodal Reasoners", "url": "https://huggingface.co/papers/2610.15542" }, { "entities": [], "id": "8aefef1e94de612a", "kind": "press", "meta": { "age_hours_at_score": 8.4, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-15T01:39:53.000Z", "source": "bloomberg-tech", "title": "JPMorgan's Dimon Sees Benefits From AI, Warns of Risks", "url": "https://bloomberg.com/news/videos/2026-10-15/jpmorgan-s-dimon-sees-benefits-from-ai-warns-of-risks-video", "weight": 0.6 } ], "count": 2, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-15T01:39:53.000Z", "lead_minutes": 100, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "bloomberg-tech", "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "bloomberg.com", "pillar_reason": "source default", "publisher": "Bloomberg.com", "publisher_url": "https://www.bloomberg.com", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 0, "entities": 0, "recency": 17, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-15T00:00:00.000Z", "score": 48.3, "source": "wires-ai", "summary": "Bloomberg.com, via Google News.", "title": "Watch JPMorgan's Dimon Sees Benefits From AI, Warns of Risks", "url": "https://news.google.com/rss/articles/CBMirAFBVV95cUxQWDFMSHFrdlkwdy1LYmVvbE5QSUlCT0lGQ0d1cnF5dmg3OEZoUmR0R01JbEQ2eDUwRW00X2MwdXJaUDc4dGplM2R4enJXS2hmMl9teVBkb3ZMTHFQVUJ4WEdSOE9ZUkpDS2RNc0lDSEpNaWpmT1F5MC1HNTdvZVNobEw0X2s0emh2LUhFWGhVSFFqSG5hRC1PLXJTdVM3TWpFQlkzTlFMdXR3emtS?oc=5" }, { "entities": [], "id": "9885df2bc49c5f20", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.13521", "arxiv_published_at": "2026-10-09T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 22 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 11.2, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 22 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 48.2, "source": "hf-daily-papers", "summary": "Multi-teacher on-policy distillation (MOPD) extends on-policy distillation (OPD) by routing domain-specific data to specialized teachers, integrating their capabilities through dense token-level supervision on student-generated trajectories. However, domain routing only distinguishes the supervision sources for differe", "title": "Beyond Domain-Routed Supervision: Cross-Teacher Guarding for Multi-Teacher On-Policy Distillation", "url": "https://huggingface.co/papers/2610.13521" }, { "entities": [], "id": "60b84b08538e88ab", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.18861", "arxiv_published_at": "2026-10-13T00:00:00.000Z", "comments": 2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 20 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T03:45:39.508Z", "github_repo": "https://github.com/togethercomputer/HybridAgent", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 10.9, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 20 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 47.9, "source": "hf-daily-papers", "summary": "How can we build an agent that thinks beyond human language, yet still acts through it in the external environment? Modern LLM agents still reason and interact through text, while latent reasoning offers an efficient internal medium but lacks native tool access and reasoning transparency. We bridge these two directions", "title": "Hybrid Reasoning Agents", "url": "https://huggingface.co/papers/2610.18861" }, { "entities": [], "id": "b9d717e4939bc8b4", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.20442", "arxiv_published_at": "2026-10-14T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 18 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T03:45:14.282Z", "github_repo": "https://github.com/chonghanqin/Awesome-Agentic-Robots", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 10.6, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 18 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 47.6, "source": "hf-daily-papers", "summary": "Foundation models are driving a paradigm shift in robotic intelligence, moving beyond low-level action prediction to goal-directed orchestration of data collection, model training, task planning, and real-world execution. We characterize this emerging paradigm as an agentic robots, in which agents reason over human ins", "title": "Towards Agentic Robots: Model, Data, Environment, and Harness", "url": "https://huggingface.co/papers/2610.20442" }, { "entities": [], "id": "432b23a500ff8bfd", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.18097", "arxiv_published_at": "2026-10-13T10:14:23.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 13 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": "https://github.com/siruizhang30/APT", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 9.5, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 13 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 46.5, "source": "hf-daily-papers", "summary": "Post-training is crucial for improving text-to-image models, yet existing pipelines typically rely on manually curated training data and fixed training distributions. We introduce APT (Agentic Post-Training), a fully automated framework that adaptively post-trains a base model according to its evolving capability profi", "title": "APT: Feedback-Driven Agentic Post-Training for Text-to-Image Generation", "url": "https://huggingface.co/papers/2610.18097" }, { "entities": [], "id": "f576fbe35ec68ec1", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.04781", "arxiv_published_at": "2026-10-08T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 13 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 9.5, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 13 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 46.5, "source": "hf-daily-papers", "summary": "In an image latent space, the embeddings of high-resolution, natural, and sharp images form a manifold. Degradation of high-resolution images pushes their embeddings off this manifold. Real-world super-resolution (SR) then becomes the task of mapping the degraded embedding back onto this manifold --- not anywhere on th", "title": "Super-Resolution in The Right Latent Space: A Frozen Vision-Foundation Substrate", "url": "https://huggingface.co/papers/2610.04781" }, { "entities": [], "id": "65e53efe9b172e46", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.19789", "arxiv_published_at": "2026-10-14T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 12 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T03:45:14.282Z", "github_repo": "https://github.com/aim-uofa/Self-Distillation-AgentRL", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 9.2, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 12 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 46.2, "source": "hf-daily-papers", "summary": "Self-distillation in agentic reinforcement learning has produced a growing number of methods. However, existing evaluations often reuse published baseline scores under differing experimental conditions, provide few direct comparisons among contemporaneous methods, and give limited attention to training stability and co", "title": "More Methods, Better Agents? A Reality Check on Self-Distillation in Agentic Reinforcement Learning", "url": "https://huggingface.co/papers/2610.19789" }, { "entities": [], "id": "9bce9ca1be803bfd", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.02089", "arxiv_published_at": "2026-10-01T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 11 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": "https://github.com/SNU-PI/HumanoidToolBench", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 8.9, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 11 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 45.9, "source": "hf-daily-papers", "summary": "As robotic hardware and learning methods advance, humanoids need tools to perform tasks beyond their inherent physical limits. Successful tool use requires selecting a suitable tool and coordinating manipulation and, when needed, locomotion to complete the task. Existing benchmarks do not jointly evaluate these capabil", "title": "HumanoidToolBench: Benchmarking Humanoid Tool Use from Selection to Mobile Execution", "url": "https://huggingface.co/papers/2610.02089" }, { "entities": [], "id": "ca1fe0094d23ded5", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.19560", "arxiv_published_at": "2026-10-14T04:19:15.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 9 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "github_repo": "https://github.com/sahsaeedi/tau-emotion-bench", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 8.3, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 9 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 45.3, "source": "hf-daily-papers", "summary": "In long-horizon tool-calling tasks, LLM agents can fail not only because of tool-use errors, but also because of ineffective interaction with users. Yet existing benchmarks largely assume cooperative and behaviorally stable users, leaving the effect of emotional variation on agent reliability underexplored. We introduc", "title": "τ-Emotion-Bench: Exposing the Interaction Fragility of LLM Agents through Real-World User Emotions", "url": "https://huggingface.co/papers/2610.19560" }, { "entities": [], "id": "809bbc617bcc744c", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.15725", "arxiv_published_at": "2026-10-11T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 8 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "github_repo": "https://github.com/fuxi-ailabs/SkillMarker", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 7.9, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 8 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 44.9, "source": "hf-daily-papers", "summary": "Agent skills encode operational procedures and domain expertise as reusable knowledge assets, yet their distribution in plaintext exposes them to unauthorized copying and leakage. However, practical mechanisms for skill ownership verification and leakage tracing remain underdeveloped. Watermark embedding before distrib", "title": "SkillMarker: A KGW-Based Watermarking Method for Agent Skill Ownership Verification", "url": "https://huggingface.co/papers/2610.15725" }, { "entities": [], "id": "2de269b300b48ab6", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.16302", "arxiv_published_at": "2026-10-12T07:29:07.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 6 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": "https://github.com/HKUST-LongGroup/RAR", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 7, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 6 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 44, "source": "hf-daily-papers", "summary": "Video-based interactive world models aim to enable real-time, user-controllable environments. Yet most existing systems are trained and rolled out at a fixed temporal rate, limiting both runtime-rate adaptation and physical-horizon coverage -- a limitation we term temporal-rate coupling. Specifically, when runtime gene", "title": "RAR: Rate-Adaptive Rollout for Time-Consistent Interactive World Models", "url": "https://huggingface.co/papers/2610.16302" }, { "entities": [], "id": "55cbfb5652a64608", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.19525", "arxiv_published_at": "2026-10-14T03:42:59.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 6 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T03:45:39.508Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 7, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 6 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 44, "source": "hf-daily-papers", "summary": "Verifiable instruction-following benchmarks emphasize output-constraint satisfaction but can overlook whether responses meaningfully answer the underlying request, creating an evaluation blind spot. We show that frontier models can receive full credit for responses with little substantive answer content. Using the same", "title": "Diagnosing and Correcting Reward Hacking in Verifiable Instruction Following", "url": "https://huggingface.co/papers/2610.19525" }, { "entities": [], "id": "d6db000a47b704b1", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.18284", "arxiv_published_at": "2026-10-13T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 6 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 7, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 6 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 44, "source": "hf-daily-papers", "summary": "Generated images often have coherent layouts and visual content, yet local errors in small text and complex characters still limit their readability and practical use. Scene-text refinement requires accuracy, preservation, and efficiency. Existing methods based on variational autoencoders (VAEs) can lose fine strokes d", "title": "Efficient Scene-Text Refinement via Pixel-Space Grouped Image Generation", "url": "https://huggingface.co/papers/2610.18284" }, { "entities": [ "Jev" ], "id": "7ddb50caa785f2b7", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.19118", "arxiv_published_at": "2026-10-13T19:52:14.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 2 }, "entity_kinds": { "labs": [], "models": [ "Jev" ] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 3.9, "entities": 3, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 2 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 43.9, "source": "hf-daily-papers", "summary": "Emerging decision models such as JEV provide typed interfaces that map task instructions and observations directly to choices, judgments, and scores. As these models are increasingly used for automated decision-making, understanding their reliability under adversarial manipulation becomes critical. We introduce Decisio", "title": "DecisionRobust: Benchmarking Robustness for JEV Style Decision Models Across Text and Vision", "url": "https://huggingface.co/papers/2610.19118" }, { "entities": [], "id": "5827b642b291154c", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.19100", "arxiv_published_at": "2026-10-13T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 5 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": "https://github.com/PrentisAI/GroundGUI", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 6.4, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 5 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 43.4, "source": "hf-daily-papers", "summary": "Constructing robust GUI agents relies on multimodal visual grounding: the ability to precisely map natural language intents to actionable on-screen components. Despite abundant desktop, web, and mobile benchmarks, high-fidelity resources tailored for diverse environments are surprisingly scarce. To bridge this gap, we", "title": "GroundGUI: GUI Grounding by High-Quality Data and Distillation-to-Reinforcement Learning", "url": "https://huggingface.co/papers/2610.19100" }, { "entities": [], "id": "7a11cbffe20520e1", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2606.24472", "arxiv_published_at": "2026-10-14T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 5 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T03:45:39.508Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 6.4, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 5 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 43.4, "source": "hf-daily-papers", "summary": "Vision-language-action (VLA) models have rapidly advanced generalist robot manipulation by harnessing the semantic knowledge of pretrained vision-language backbones. However, their visual representations are typically grounded in 2D image coordinates rather than calibrated camera geometry. This limitation is especially", "title": "G^3VLA: Geometric Inductive Bias for Vision-Language-Action Models", "url": "https://huggingface.co/papers/2606.24472" }, { "entities": [], "id": "c6993a385f934e61", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.14774", "arxiv_published_at": "2026-10-10T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 5 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "github_repo": "https://github.com/hkust-nlp/ProgAgent", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 6.4, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 5 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 43.4, "source": "hf-daily-papers", "summary": "Large language models (LLMs) are increasingly expected to orchestrate diverse tools over long-horizon tasks. Existing agents typically use Direct Tool Calling (DTC), where the model invokes tools sequentially, and tool responses accumulate in the model context, which can lead to context rot and orchestration breakdown.", "title": "ProgAgent: Learning Tool Orchestration through Programmatic Training", "url": "https://huggingface.co/papers/2610.14774" }, { "entities": [], "id": "f3718f927f1b78e0", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.20256", "arxiv_published_at": "2026-10-14T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 4 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T03:45:39.508Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 5.8, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 4 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 42.8, "source": "hf-daily-papers", "summary": "Long-horizon agents must coordinate reasoning, tool use, and environment feedback over extended interactions. Learning from sparse outcome rewards requires weighting trajectories of different lengths and updating the policy while rollouts are still being collected. We examine a single-rollout asynchronous recipe combin", "title": "Long-Horizon RL Needs Muon", "url": "https://huggingface.co/papers/2610.20256" }, { "entities": [], "id": "7256a2cee63ecd0d", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.14261", "arxiv_published_at": "2026-10-09T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 3 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 5, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 3 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 42, "source": "hf-daily-papers", "summary": "Whole-slide images (WSIs) are gigapixel-scale digital scans of histopathology tissue and serve as the primary basis for cancer diagnosis. Existing histopathology generative models operate at the patch scale, producing fixed-size tiles of at most 1024times1024 pixels which encompass only a small fraction of the WSI. In", "title": "BigCell: Generating Gigapixel Whole-Slide Images", "url": "https://huggingface.co/papers/2610.14261" }, { "entities": [], "id": "a3ea744b7c423d82", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.18248", "arxiv_published_at": "2026-10-13T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 3 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T03:45:39.508Z", "github_repo": "https://github.com/MilkThink-Lab/Awesome-Benchmark-Compression", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 5, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 3 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 42, "source": "hf-daily-papers", "summary": "Benchmarks are essential for evaluating and improving large language models (LLMs). Yet many widely used benchmarks are redundant, making evaluation unnecessarily expensive. Benchmark compression lowers this cost by estimating full-benchmark scores and rankings from a partial evaluation. Research in this area has grown", "title": "Are We Testing Too Much? A Survey of Benchmark Compression for Efficient Evaluation of Large Language Models", "url": "https://huggingface.co/papers/2610.18248" }, { "entities": [], "id": "d1372e20da1cbdcd", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.20604", "arxiv_published_at": "2026-10-14T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 3 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:22:35.661Z", "github_repo": "https://github.com/worldbench/VITAL", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 5, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 3 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 42, "source": "hf-daily-papers", "summary": "World-action models aim to turn visual anticipation into driving decisions, yet typically train action experts on trajectory representations defined independently of visual dynamics. This motivates deriving action targets directly from visual motion. We first examine a simpler setting in which trajectories are reconstr", "title": "VITAL: From Visual Motion to Precise Action", "url": "https://huggingface.co/papers/2610.20604" }, { "entities": [], "id": "dffbc6f0fe3db68f", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.19551", "arxiv_published_at": "2026-10-14T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 3 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:22:35.661Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 5, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 3 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 42, "source": "hf-daily-papers", "summary": "Text-to-image models increasingly generate readable Chinese text that still contains local rendering defects, such as a character with an extra or missing stroke or a malformed component. Such errors are invisible to OCR and vision-language models, which are trained for recognition and are therefore invariant to the ve", "title": "TextVerifier: Seeing Beyond Recognition to Verify Chinese Text Rendering", "url": "https://huggingface.co/papers/2610.19551" }, { "entities": [], "id": "0b691b370383d49c", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.10787", "arxiv_published_at": "2026-10-07T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 2 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "github_repo": "https://github.com/metacognitionai/NavGPT-3", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 3.9, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 2 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 40.9, "source": "hf-daily-papers", "summary": "Language models trained with long-horizon agentic reinforcement learning can generalize knowledge through reasoning, express precise actions, and pursue goals over many steps, raising the ceiling on what an embodied agent can understand and decide. Physical interaction, however, remains the domain of action policies, w", "title": "NavGPT-3: Harnessing Context in a Hierarchical Navigation Runtime", "url": "https://huggingface.co/papers/2610.10787" }, { "entities": [], "id": "617cc604a3bac3be", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.15425", "arxiv_published_at": "2026-10-11T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 2 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": "https://github.com/AutoLab-SAI-SJTU/RoboCombine", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 3.9, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 2 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 40.9, "source": "hf-daily-papers", "summary": "In embodied manipulation, Vision Language Action (VLA) models can execute atomic skills, but long-horizon manipulation requires recombining these skills without demonstrations for every skill combination. The full instruction route treats skill chaining as long-horizon imitation. In a controlled study, full instruction", "title": "Exploring the Limits of Combinatorial Generalization in Embodied Manipulation", "url": "https://huggingface.co/papers/2610.15425" }, { "entities": [], "id": "aa35a33a56de8067", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.20609", "arxiv_published_at": "2026-10-14T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 2 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "github_repo": "https://github.com/ASGuard-UCI/safedrive-vla", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 3.9, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 2 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 40.9, "source": "hf-daily-papers", "summary": "Vision-Language-Action (VLA) models are gaining attention in autonomous driving, inheriting commonsense priors from large language models (LLMs) and widening the navigation interface from a fixed command set to free-form natural-language instructions. However, this open-ended interface exposes a new vulnerability surfa", "title": "SafeDriveVLA: Navigation-Conditioned World Model Dreaming for Conflict-Aware End-to-End Autonomous Driving", "url": "https://huggingface.co/papers/2610.20609" }, { "entities": [], "id": "e40f11006c286911", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.14096", "arxiv_published_at": "2026-10-08T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 2 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "github_repo": "https://github.com/zlab-princeton/sweeper-bench", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 3.9, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 2 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 40.9, "source": "hf-daily-papers", "summary": "Coding agents increasingly interact with users by generating interactive software on the fly rather than replying in text, yet many bugs in such software reveal themselves only after multi-step interactions. Today, users must discover these bugs and wait for fixes, making the development loop slow and expensive. Existi", "title": "SWEeper-Bench: Can Agents Discover Bugs in Interactive Software?", "url": "https://huggingface.co/papers/2610.14096" }, { "entities": [], "id": "e4d17797593b7db1", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.20524", "arxiv_published_at": "2026-10-14T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 2 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T03:45:39.508Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 3.9, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 2 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 40.9, "source": "hf-daily-papers", "summary": "Existing robot policy memories typically evolve with observation sequences, implicitly treating each update as the same amount of physical time. Yet identical observation histories can span different durations and require different actions, exposing physical time as a missing dimension of policy memory. We introduce Te", "title": "Physical Time as a Missing Dimension of Robot Policy Memory Dynamics", "url": "https://huggingface.co/papers/2610.20524" }, { "entities": [], "id": "e6ed0c2fb9d0a63b", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.19726", "arxiv_published_at": "2026-10-14T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 2 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "github_repo": "https://github.com/XGEN-Labs/XGEN-JING", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 3.9, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 2 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 40.9, "source": "hf-daily-papers", "summary": "We present a Generative World Simulation framework that couples persistent shared world state with continuous first-person audiovisual generation. The framework integrates DAO, a computable world engine, and JING, an Egocentric Interactive Experience Model, to support sustained interaction for humans and agents. DAO ma", "title": "Generative World Simulation with DAO and JING", "url": "https://huggingface.co/papers/2610.19726" }, { "entities": [], "id": "f9302be972f992da", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.20195", "arxiv_published_at": "2026-10-14T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 2 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 3.9, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 2 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 40.9, "source": "hf-daily-papers", "summary": "Effective social interaction requires more than fluent responses: agents must reach plans that fit the participants' needs and circumstances. Existing evaluations of social agents mainly rely on LLM judges that rate the conversation, without checking the resulting plan. We introduce HICO-Bench, a benchmark of 180 every", "title": "What Makes a Social Agent's Interactions Effective? Learning from Verifiable Outcomes in Paired Worlds", "url": "https://huggingface.co/papers/2610.20195" }, { "entities": [], "id": "fe73623d4b74c7c1", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.17638", "arxiv_published_at": "2026-10-13T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 2 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": "https://github.com/Z1zyw/RegisterWAM", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 3.9, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 2 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 40.9, "source": "hf-daily-papers", "summary": "World Action Models (WAMs) use generative visual representations to condition robot actions, yet how these representations organize action-relevant information remains poorly understood. We identify persistent high-norm spatial outliers in WAM representations and find that their action relevance persists even after nor", "title": "Acting Through Registers", "url": "https://huggingface.co/papers/2610.17638" }, { "entities": [], "id": "2fe39493f21f2fd1", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.09055", "arxiv_published_at": "2026-10-06T00:00:00.000Z", "comments": 2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 1 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "github_repo": "https://github.com/NEBULIS-Lab/MimicX", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 2.5, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 1 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 39.5, "source": "hf-daily-papers", "summary": "Human videos provide rich motion targets for humanoid learning, yet visually plausible references can still produce persistent failures under physics-based execution. These failures reveal where training supervision should change. We present MimicX, a policy-in-the-loop framework that uses execution feedback to refine", "title": "MimicX: Policy-in-the-Loop Supervision Refinement for Video-Driven Humanoid Motion Tracking", "url": "https://huggingface.co/papers/2610.09055" }, { "entities": [], "id": "379cd9790a5ad54c", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.17893", "arxiv_published_at": "2026-10-13T07:46:48.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 1 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 2.5, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 1 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 39.5, "source": "hf-daily-papers", "summary": "Effective collaboration among large language model (LLM) agents in multi-agent systems requires sharing relevant evidence, requesting missing information, and incorporating teammates' contributions into decisions. Training these behaviors is challenging because final task outcomes provide limited feedback on the value", "title": "Training LLMs for Multi-Agent Collaboration with Verifiable Information Rewards", "url": "https://huggingface.co/papers/2610.17893" }, { "entities": [], "id": "57b028de77e1d1d6", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.17016", "arxiv_published_at": "2026-10-12T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 1 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T08:07:19.188Z", "github_repo": "https://github.com/ishida-lab/ProbabilityBench", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 2.5, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 1 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 39.5, "source": "hf-daily-papers", "summary": "Evaluating model uncertainty on real-world data is challenging because each instance provides only a single observed label, leaving true conditional class probabilities unknown. To address this, we introduce ProbabilityBench, a benchmark that provides exact, instance-level class probabilities across three simulated tex", "title": "ProbabilityBench: Model Evaluation and Calibration with Exact Ground-Truth Uncertainty", "url": "https://huggingface.co/papers/2610.17016" }, { "entities": [], "id": "7fbdd8553517b9e9", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.17831", "arxiv_published_at": "2026-10-13T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 1 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T03:45:39.508Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 2.5, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 1 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 39.5, "source": "hf-daily-papers", "summary": "Diffusion large language models (dLLMs) are appealing for code generation because they can update many tokens in parallel, yet many are optimized for sequence synthesis rather than edit prediction. This setting is poorly matched to modern AI-assisted IDEs, where edit prediction must infer the next code change from rece", "title": "Diff Diffusion: Predicting Edit Paths with Diffusion Models for Efficient Code Editing", "url": "https://huggingface.co/papers/2610.17831" }, { "entities": [], "id": "959767fb69a0d8b3", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.13239", "arxiv_published_at": "2026-10-09T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 1 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T08:07:19.188Z", "github_repo": "https://github.com/xyzzzh/Jag", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 2.5, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 1 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 39.5, "source": "hf-daily-papers", "summary": "Visual grounding combines image--language understanding with geometric prediction, yet autoregressive multimodal models often express a box as a coordinate sequence. Sequential decoding adds computation to the multimodal processing needed for localization. Adapting these representations to box geometry offers a route t", "title": "Jag: Direct Box Prediction for Efficient Visual Grounding", "url": "https://huggingface.co/papers/2610.13239" }, { "entities": [], "id": "99933dab8048476e", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.18838", "arxiv_published_at": "2026-10-13T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 1 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 2.5, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 1 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 39.5, "source": "hf-daily-papers", "summary": "A central challenge in scaling flow models is maintaining stability in training and integration. In flow matching, this stability depends critically on how source samples are paired with data. Independent random pairing introduces substantial stochasticity, motivating minibatch optimal transport (OT) to construct onlin", "title": "Scaling Online Optimal Transport Minibatches for Flow Matching", "url": "https://huggingface.co/papers/2610.18838" }, { "entities": [], "id": "aeeb5ed254167bc0", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.19422", "arxiv_published_at": "2026-10-14T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 1 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T08:07:19.188Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 2.5, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 1 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 39.5, "source": "hf-daily-papers", "summary": "Large language model (LLM) agents accomplish complex tasks through multi-turn interactions with external tools. However, malicious content embedded in user queries, tool descriptions, or environment observations can manipulate their decisions and induce unsafe actions. Adversarial co-evolution has emerged as an effecti", "title": "AgentSafeEvo: Adversarial Co-Evolution for LLM Agent Safety", "url": "https://huggingface.co/papers/2610.19422" }, { "entities": [], "id": "b6665ab282801f82", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.17771", "arxiv_published_at": "2026-10-13T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 1 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "github_repo": "https://github.com/LiXin97/ReMaRk", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 2.5, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 1 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 39.5, "source": "hf-daily-papers", "summary": "A recommendation request on Reddit draws several replies, each naming different titles. When an LLM recommender is trained on such threads with reinforcement learning (RL), which replies should a generated list be rewarded against? The state-of-the-art recipe, Rank-GRPO, picks one reply per training example, so the adv", "title": "Many Right Answers, One Training Example: Removing Responder Noise from GRPO for LLM Recommenders", "url": "https://huggingface.co/papers/2610.17771" }, { "entities": [], "id": "ff87e6dfa1d67904", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.19696", "arxiv_published_at": "2026-10-14T07:19:43.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 1 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T07:18:20.412Z", "github_repo": "https://github.com/LiXin97/SYNC", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 2.5, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 1 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 39.5, "source": "hf-daily-papers", "summary": "Multi-agent systems allow language models to solve tasks collaboratively by sharing complementary information. These systems typically communicate through text, while recent approaches also explore exchanging internal model states. However, sharing these states across architectures often requires learned translators or", "title": "Share the Symbol, Not the Vector: Training-Free Fact Communication Across Language Models", "url": "https://huggingface.co/papers/2610.19696" }, { "entities": [], "id": "529b943b81fadff3", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.16338", "arxiv_published_at": "2026-10-12T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 0 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T06:53:04.330Z", "github_repo": "https://github.com/WeChatCV/SyncReward", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 0 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 37, "source": "hf-daily-papers", "summary": "Recent years have witnessed rapid progress in joint audio--visual generation. However, audio--visual synchronization remains a challenge, as generated speech and sound often temporally misalign with visual events. A key bottleneck is the lack of human-annotated data, reliable evaluation benchmarks, and synchronization-", "title": "SyncReward: Human-Aligned Synchronization Reward for Audio-Visual Generation", "url": "https://huggingface.co/papers/2610.16338" }, { "entities": [], "id": "9c9e3f62aedcf2c7", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.19026", "arxiv_published_at": "2026-10-13T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 0 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T08:07:19.188Z", "github_repo": "https://github.com/jiwonsong-dev/JSSA", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 0 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 37, "source": "hf-daily-papers", "summary": "Sparse attention reduces the KV-cache traffic that dominates long-context LLM decoding, but practical methods largely operate at coarse granularity. Token-level selection offers finer control, yet presents two challenges: low-dimensional scoring may lose query--key structure needed for accurate ranking, while gathering", "title": "Joint Subspace Sparse Attention for Efficient Long-Context Decoding", "url": "https://huggingface.co/papers/2610.19026" }, { "entities": [], "id": "b4a142711f33d9e8", "kind": "paper", "meta": { "age_hours_at_score": 8.4, "arxiv_id": "2610.19138", "arxiv_published_at": "2026-10-13T00:00:00.000Z", "comments": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 0 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 17, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 0 }, "pillar": "capability", "published_at": "2026-10-15T00:00:00.000Z", "score": 37, "source": "hf-daily-papers", "summary": "How well can a language model turn a large, incomplete and partly contradictory body of documents into a plan when no single correct answer exists? 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This pointwise representation wastes capacity on highly correlated neighboring actions, leaves temporal continuity and smoothness to be learned implicitly, a", "title": "Vela: Scaling Vision-Language-Action Models with Adaptive Action Curve Parametrization", "url": "https://huggingface.co/papers/2610.05230" }, { "entities": [], "id": "cdbbaea14508232a", "kind": "press", "meta": { "age_hours_at_score": 8.4, "categories": [ "AI", "Enterprise" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-15T00:00:00.000Z", "first_source": "techcrunch-ai", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "techcrunch-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://techcrunch.com/category/artificial-intelligence/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 17, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-15T00:00:00.000Z", "score": 32, "source": "techcrunch-ai", "summary": "Every day, conversations unfold across social media and websites around the world. A seemingly insignificant post—whether it involves AI-generated misinformation, hate speech, or a damaging rumor—can quickly snowball into a much larger public controversy. Gudea, a company that tracks how stories and rumors spread…", "title": "Gudea leverages AI to help organizations prevent social media crises before they erupt", "url": "https://techcrunch.com/2026/10/14/gudea-leverages-ai-to-help-organizations-prevent-social-media-crises-before-they-erupt" }, { "entities": [ "Anthropic" ], "id": "4c31c7cec80ac7f6", "kind": "press", "meta": { "age_hours_at_score": 8.4, "categories": [ "Amodei, Dario", "Anthropic AI LLC", "Artificial Intelligence", "Initial Public Offerings", "San Francisco (Calif)", "Venture Capital" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T23:57:26.000Z", "first_source": "nyt-tech", "last_published_at": "2026-10-14T23:57:26.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T23:57:26.000Z", "sources": [ "nyt-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "feed": "https://rss.nytimes.com/services/xml/rss/nyt/Technology.xml", "first_seen_at": 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"wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-14T23:57:26.000Z", "score": 37.5, "source": "nyt-tech", "summary": "The company held an investor meeting on Wednesday as it plans a blockbuster initial public offering as soon as next month.", "title": "Anthropic Says Revenue Jumped 45% to $16.8 Billion in Third Quarter", "url": "https://nytimes.com/2026/10/14/technology/anthropic-ipo-revenue-profit.html" }, { "entities": [], "id": "c3d0c12082a516d2", "kind": "forum", "meta": { "age_hours_at_score": 8.8, "comments": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T23:32:32.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-14T23:32:32.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T23:32:32.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50097104", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 16 }, "entity_kinds": { "labs": [], "models": [] }, 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interactions: Add gemini-4-argon to Model enum ( 8554b07 ) Bug Fixes Clear continuation state and preserve prompt feedback when continuation hop returns no candidates ( a0d809c ) Fix the function declaration parser for union types. Fixes #3060 ( be32e9e ) Fix the grounding metadata merging…", "title": "googleapis/python-genai v2.31.0", "url": "https://github.com/googleapis/python-genai/releases/tag/v2.31.0" }, { "entities": [], "id": "441d2525c07d0223", "kind": "forum", "meta": { "age_hours_at_score": 9.2, "bsky_handle": "bloomberg.com", "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T23:10:11.000Z", "first_source": "bluesky-ai", "last_published_at": "2026-10-14T23:10:11.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T23:10:11.000Z", "sources": [ "bluesky-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 4 }, "entity_kinds": { "labs": [], "models": [] }, "external_url": "https://bloom.bg/4zBMLKc", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 4, "mode": "author-feeds", "outlet_domain": "bloomberg.com", "pillar_reason": 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important markets.", "title": "Apple Prepares to Debut First AI Features in China in December", "url": "https://bsky.app/profile/bloomberg.com/post/3mxuo6ayufz2i" }, { "entities": [ "GPT" ], "id": "5983265f9a638b7f", "kind": "forum", "meta": { "age_hours_at_score": 9.2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T23:09:33.000Z", "first_source": "lesswrong", "last_published_at": "2026-10-14T23:09:33.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T23:09:33.000Z", "sources": [ "lesswrong" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "GPT" ] }, "feed": "https://www.lesswrong.com/feed.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 2, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 16.7, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "governance", "published_at": "2026-10-14T23:09:33.000Z", "score": 32.2, "source": "lesswrong", "summary": "Appendix: some methodological choices", "title": "I trained GPT-2 with full-batch gradient descent so you don't have to", "url": "https://lesswrong.com/posts/9gqjdRyJxSM6kS7zi/i-trained-gpt-2-with-full-batch-gradient-descent-so-you-don" }, { "entities": [], "id": "e021de74fcf459af", "kind": "forum", "meta": { "age_hours_at_score": 9.3, "comments": 7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T23:06:24.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-14T23:06:24.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T23:06:24.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50096825", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 22 }, 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The AI simulation \"aims to reflect 80% of the scenarios McKinsey consultants might encounter in their…", "title": "McKinsey is making job applicants spar with a fake AI client", "url": "https://theverge.com/news/1011654/mckinsey-ai-client-interview" }, { "entities": [], "id": "f358f3bfe4f7a5b0", "kind": "forum", "meta": { "age_hours_at_score": 9.6, "categories": [ "ControlProblem" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T22:48:19.000Z", "first_source": "reddit-controlproblem", "last_published_at": "2026-10-14T22:48:19.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T22:48:19.000Z", "sources": [ "reddit-controlproblem" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/ControlProblem/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 5, "engagement": 0, "entities": 0, "recency": 16.6, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": { "id": "208357fdc64e", "is_lead": true, "lead": "f358f3bfe4f7a5b0", "linked_by": [ { "rule": "shared-words", "with": "e84fab743ff236ad", "words": [ "antichrist", "lect", "thiel" ] } ], "members": [ "e84fab743ff236ad", "f358f3bfe4f7a5b0" ], "severity": 0, "source_count": 2, "sources": [ "reddit-artificial", "reddit-controlproblem" ] } }, "pillar": "attention", "published_at": "2026-10-14T22:48:19.000Z", "score": 30.4, "source": "reddit-controlproblem", "summary": "submitted by /u/DanielFNash [link] [comments]", "title": "Epistemic Velocity and Turnkey Systems: Audit of Thiel's Antichrist Lecture Night 3", "url": "https://reddit.com/r/ControlProblem/comments/1x65if9/epistemic_velocity_and_turnkey_systems_audit_of" }, { "entities": [ "Gemini" ], "id": "e40c71e6d23ca935", "kind": "forum", "meta": { "age_hours_at_score": 9.6, "categories": [ "singularity" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T22:47:11.000Z", "first_source": "reddit-singularity", "last_published_at": "2026-10-14T22:47:11.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T22:47:11.000Z", "sources": [ "reddit-singularity" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Gemini" ] }, "feed": "https://www.reddit.com/r/singularity/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T03:45:39.508Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 16.6, "severity": 0, "source_weight": 10 }, "source_weight": 0.4, "story": { "id": "df4ed8b6620d", "is_lead": false, "lead": "5c68d25a5be34f23", "linked_by": [ { "rule": "shared-words", "with": "5c68d25a5be34f23", "words": [ "argon", "gemini", "human" ] } ], "members": [ "5c68d25a5be34f23", "e40c71e6d23ca935" ], "severity": 0, "source_count": 2, "sources": [ "reddit-artificial", "reddit-singularity" ] } }, "pillar": "attention", "published_at": "2026-10-14T22:47:11.000Z", "score": 29.6, "source": "reddit-singularity", "summary": "submitted by /u/141_1337 [link] [comments]", "title": "Google's Gemini 4 Argon takes #1 in understanding the physical world beyond what it sees. It can reconstruct past events, predict what happens next, and infer hidden objects and human intentions.…", "url": "https://reddit.com/r/singularity/comments/1x65hj1/googles_gemini_4_argon_takes_1_in_understanding" }, { "entities": [], "id": "367280791dc4c396", "kind": "forum", "meta": { "age_hours_at_score": 9.7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T22:42:36.000Z", "first_source": "lesswrong", "last_published_at": "2026-10-14T22:42:36.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T22:42:36.000Z", "sources": [ "lesswrong" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.lesswrong.com/feed.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 2, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 16.6, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "governance", "published_at": "2026-10-14T22:42:36.000Z", "score": 29.1, "source": "lesswrong", "summary": "In this post, I try to briefly make the case for making models good at moral philosophy. To be up front, aside from some niche areas of moral philosophy, I don’t actually think this is the most important thing to work on for most people. But, depending on the exact scope, I do think it’s near competitive. I also like…", "title": "The basic argument for making AI better at moral philosophy", "url": "https://lesswrong.com/posts/auMyz6pjPX5nGLYsL/the-basic-argument-for-making-ai-better-at-moral-philosophy" }, { "entities": [], "id": "7ddb8decfb979a7c", "kind": "forum", "meta": { "age_hours_at_score": 9.8, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T22:36:06.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-14T22:36:06.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T22:36:06.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T03:45:14.282Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { 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"bluesky-ai", "ft-ai", "gnews-ai", "hn-fresh-ai", "nyt-tech", "techmeme", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-14T22:30:03.000Z", "score": 37, "source": "techmeme", "summary": "Financial Times : Sources: Anthropic told investors that its revenue jumped to $16.8B in Q3, up from $11.5B in Q2, and that the company was profitable on an adjusted basis in Q3 — Rapid growth is slower than some anticipated as fears about the AI trade rattle markets — Anthropic told investors …", "title": "Sources: Anthropic told investors that its revenue jumped to $16.8B in Q3, up from $11.5B in Q2, and that the company was profitable on an adjusted basis in Q3 (Financial Times)", "url": "https://techmeme.com/261014/p44" }, { "entities": [ "Anthropic" ], "id": "2bb464d7faf82e22", "kind": "press", "meta": { "age_hours_at_score": 10, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T22:22:48.000Z", "source": "bloomberg-tech", "title": "Anthropic Revenue Hits $16.8 Billion in Quarter Before IPO", "url": "https://bloomberg.com/news/articles/2026-10-14/anthropic-revenue-jumps-to-16-8-billion-in-quarter-before-ipo", "weight": 0.6 }, { "dedup_key": null, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 10 }, "kind": "forum", "outlet": "bloomberg.com", "published_at": "2026-10-15T04:45:10.000Z", "source": "bluesky-ai", "title": "Anthropic Revenue Jumps to $16.8 Billion in Quarter Before IPO", "url": "https://bsky.app/profile/bloomberg.com/post/3mxvavbml2f2y", "weight": 0.6 } ], "count": 3, "first_seen_published_at": "2026-10-14T22:22:48.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-15T05:14:00.000Z", "lead_minutes": 411, "primary_published_at": "2026-10-15T05:14:00.000Z", "sources": [ "bloomberg-tech", "bluesky-ai", "wires-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 10 }, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "bloomberg.com", "pillar_reason": "hardware/capital keyword", "publisher": "Bloomberg.com", "publisher_url": "https://www.bloomberg.com", "retained": true, "score_components": { "corroboration": 25, "coverage": 15, "engagement": 5.4, "entities": 3, "recency": 16.5, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": { "id": "f27ed1f158bf", "is_lead": true, "lead": "2bb464d7faf82e22", "linked_by": [ { "rule": "shared-words", "with": "98842f779147e049", "words": [ "anthropic", "jump", "revenu" ] } ], "members": [ "08b2cdffdbe6bf48", "2bb464d7faf82e22", "41d835035e2a01e4", "45e05b50f5cecac5", "4c31c7cec80ac7f6", "6c340f3efbdbf860", "6f76a14503cf2093", "7604567454cfc303", "98842f779147e049", "dcf332e0d23bb416", "e36d56a8a8a62f0d", "f47237ff75e651b0" ], "severity": 0, "source_count": 9, "sources": [ "bbc-tech", "bloomberg-tech", "bluesky-ai", "ft-ai", "gnews-ai", "hn-fresh-ai", "nyt-tech", "techmeme", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-14T22:22:48.000Z", "score": 83.7, "source": "wires-ai", "summary": "Bloomberg.com, via Google News.", "title": "Anthropic Revenue Jumps to $16.8 Billion in Quarter Before IPO", "url": "https://news.google.com/rss/articles/CBMisgFBVV95cUxPQU84aFhKaGNhRmkxOW4xRGJOZ0hBZC1rcFR5R2YyS2RqTHNaZExvaTg4ckppc2tTaHZUaFRzWmVTY2IxMXlUMFlpT1pBWk1zY1ZtZVpLdHBGZ1AzbEVSS29zMDB2eFQ4Wnhab0RrSnVxUFhkWHBfVVdDUHFIR3FGb05odzJEQ0VlQkF5QzYxYnlSRHZJd055blRMRTJTWXA5U0VWZ1Q5OWZ5VDBTcDJFWHZ3?oc=5" }, { "entities": [], "id": "9aa377038d418a8a", "kind": "forum", "meta": { "age_hours_at_score": 10, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T22:22:12.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-14T22:22:12.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T22:22:12.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T03:45:14.282Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 16.5, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T22:22:12.000Z", "score": 25.3, "source": "reddit-artificial", "summary": "AI is being used for so many things, but the one thing we are confronting globally right now alongside climate change is that we are running out of fossil fuels and without those we can't really make plastic or a whole host of chemicals so we really need to stop spunking them on producing power and concentrate on…", "title": "Practical uses?", "url": "https://reddit.com/r/artificial/comments/1x64xe6/practical_uses" }, { "entities": [ "NVIDIA" ], "id": "f5bf8a5d5805c2e3", "kind": "press", "meta": { "age_hours_at_score": 10.1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T22:15:02.000Z", "first_source": "techmeme", "last_published_at": "2026-10-14T22:15:02.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T22:15:02.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [ "NVIDIA" ], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 7, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 16.5, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": null }, "pillar": "attention", "published_at": "2026-10-14T22:15:02.000Z", "score": 37, "source": "techmeme", "summary": "Rolfe Winkler / Wall Street Journal : Apple closes above a $5T market cap for the first time, the second company after Nvidia to do so; Apple had crossed $5T six times intraday previously — To power Apple's next phase, CEO John Ternus needs to raise Apple's AI game — Apple became the second company to close above …", "title": "Apple closes above a $5T market cap for the first time, the second company after Nvidia to do so; Apple had crossed $5T six times intraday previously (Rolfe Winkler/Wall Street Journal)", "url": "https://techmeme.com/261014/p43" }, { "entities": [ "CoreWeave" ], "id": "f817154601e28ab3", "kind": "press", "meta": { "age_hours_at_score": 10.1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": 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corporate borrowing, including debt tied to AI infrastructure. She said individual issuer problems such as CoreWeave’s may coexist with a broader surge in issuance that is challenging investors’ ability to absorb new debt. She…", "title": "CreditSights Head of Strategy on Bond Market Volatility", "url": "https://bloomberg.com/news/videos/2026-10-14/creditsights-head-of-strategy-on-bond-market-volatility-video" }, { "entities": [ "Qwen" ], "id": "a778b490dcb470c4", "kind": "forum", "meta": { "age_hours_at_score": 10.2, "categories": [ "MachineLearning" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T22:08:02.000Z", "first_source": "reddit-machinelearning", "last_published_at": "2026-10-14T22:08:02.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T22:08:02.000Z", "sources": [ "reddit-machinelearning" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Qwen" ] }, "feed": "https://www.reddit.com/r/MachineLearning/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T03:13:58.488Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 16.4, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null }, "pillar": "attention", "published_at": "2026-10-14T22:08:02.000Z", "score": 30.7, "source": "reddit-machinelearning", "summary": "I wanted to find where the recognizable assistant style (em dashes, \"it's not X, it's Y\", lists of three, buzzwords) lives in a model and which training stage installs it. Where it lives: in Qwen3-4B-Instruct, a difference-in-means direction from the model's own exaggerated vs plain writing separates 64 hand-written…", "title": "Tracing where LLM \"slop\" style comes from in the model and in training, and cutting it out [R]", "url": "https://reddit.com/r/MachineLearning/comments/1x64lbk/tracing_where_llm_slop_style_comes_from_in_the" }, { "entities": [], "id": "b20d21042f33eff0", "kind": "forum", "meta": { "age_hours_at_score": 10.3, "bsky_handle": "techcrunch.com", "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T22:04:57.000Z", "first_source": "bluesky-ai", "last_published_at": "2026-10-14T22:04:57.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T22:04:57.000Z", "sources": [ "bluesky-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 5 }, "entity_kinds": { "labs": [], "models": [] }, "external_url": "https://techcrunch.com/2026/10/14/cedardb-wants-one-database-to-handle-both-transactions-and-analytics-as-ai-agents-raise-the-stakes/?utm_source=dlvr.it&utm_medium=bluesky", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 5, "mode": "author-feeds", "outlet_domain": "techcrunch.com", "pillar_reason": "source default", "reposts": 0, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 4, "entities": 0, "recency": 16.4, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-14T22:04:57.000Z", "score": 35.4, "source": "bluesky-ai", "summary": "Posted on Bluesky by @techcrunch.com.", "title": "CedarDB wants one database to handle both transactions and analytics, as AI agents raise the stakes", "url": "https://bsky.app/profile/techcrunch.com/post/3mxukjmhwel2y" }, { "entities": [], "id": "74318f8b37aac2b4", "kind": "press", "meta": { "age_hours_at_score": 10.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T22:04:00.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T22:04:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T22:04:00.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 5, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 16.4, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "31fd1abbf358", "is_lead": false, "lead": "ffec7b58423d2292", "linked_by": [ { "rule": "shared-words", "with": "ffec7b58423d2292", "words": [ "fear", "inflat", "slid", "stock" ] } ], "members": [ "74318f8b37aac2b4", "ffec7b58423d2292" ], "severity": 0, "source_count": 2, "sources": [ "bloomberg-tech", "ft-ai" ] } }, "pillar": "markets", "published_at": "2026-10-14T22:04:00.000Z", "score": 31.4, "source": "bloomberg-tech", "summary": "The Close brings you the latest news and analysis leading up to the final minutes and seconds before and after the closing bell on Wall Street. Today's guests are Vanguard President & CIO Greg Davis, UBS Equity Research Analyst, Large-Cap Banks & Consumer Finance Erika Najarian, Janus Henderson CEO Ali Dibadj,…", "title": "Stocks Slide as Hot Inflation & AI Credit Fears Hit Markets", "url": "https://bloomberg.com/news/videos/2026-10-14/the-close-10-14-2026-video" }, { "entities": [], "id": "56a2037179703ad0", "kind": "press", "meta": { "age_hours_at_score": 10.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T21:55:53.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T21:55:53.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T21:55:53.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 5, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 16.4, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "markets", "published_at": "2026-10-14T21:55:53.000Z", "score": 31.4, "source": "bloomberg-tech", "summary": "At a forum in Beverly Hills, the discussion focused on obstacles to expanding AI data centers, including rising energy costs and weakening public support for construction. Blackstone’s Mike Forman said infrastructure supply constraints are worsening, particularly around power: utilities that once could connect…", "title": "CAIS Summit Panel on AI Buildout", "url": "https://bloomberg.com/news/videos/2026-10-14/cais-summit-panel-on-ai-buildout-video" }, { "entities": [ "GPT", "OpenAI" ], "id": "8b16dc6b17676a97", "kind": "press", "meta": { "age_hours_at_score": 10.5, "categories": [ "AI (artificial intelligence)", "ChatGPT", "Chatbots", "Children", "Children's tech", "Law (US)", "OpenAI", "Technology", "US news" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T21:50:29.000Z", "first_source": "guardian-ai", "last_published_at": "2026-10-14T21:50:29.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T21:50:29.000Z", "sources": [ "guardian-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [ "GPT" ] }, "feed": "https://www.theguardian.com/technology/artificialintelligenceai/rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": "C", "excluded": null, "severity": 0.3, "strong_signal": false, "terms": [ { "field": "title", "matched": "lawsuit", "term": "lawsuit", "tier": "c", "weight": 0.3 } ] }, "pillar_reason": "regulatory/legal keyword", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 16.3, "severity": 4.5, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "6f5b03c289ee", "is_lead": false, "lead": "8a32126259e22cff", "linked_by": [ { "rule": "shared-words", "with": "314164cb6dc66c59", "words": [ "children", "harm", "openai", "stat" ] } ], "members": [ "314164cb6dc66c59", "3985ae25e3ef1633", "8a32126259e22cff", "8b16dc6b17676a97", "afdd4bd9efa8609e", "f0aeaf09406aa3eb" ], "severity": 0.3, "source_count": 7, "sources": [ "bluesky-ai", "gnews-ai", "guardian-ai", "hn-fresh-ai", "techmeme", "verge-ai", "wires-ai" ] } }, "pillar": "governance", "published_at": "2026-10-14T21:50:29.000Z", "score": 41.8, "source": "guardian-ai", "summary": "US states sue ChatGPT maker and seek to force the company to change how its products work for young users Eight US states sued OpenAI Wednesday, alleging the ChatGPT maker is harming children en masse. Kansas, Arizona, Montana, Iowa and four others allege the company’s products carried out “willful, dangerous,…", "title": "Eight states accuse OpenAI of coaching children to harm themselves in lawsuit", "url": "https://theguardian.com/technology/2026/oct/14/openai-child-self-harm-lawsuit" }, { "entities": [], "id": "8f753b5111d3675b", "kind": "press", "meta": { "age_hours_at_score": 10.5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T21:49:35.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T21:49:35.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T21:49:35.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "hardware/capital keyword", "rejected_off_topic": 5, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 16.3, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "compute", "published_at": "2026-10-14T21:49:35.000Z", "score": 31.3, "source": "bloomberg-tech", "summary": "Janus Henderson CEO Ali Dibadj said the firm is energized by its move to private ownership and is using the flexibility to invest in people, technology including AI, new products and client service. He said quarterly earnings pressures had constrained those investments, despite the firm’s growth as a public company.…", "title": "Janus Henderson CEO on Going Private, M&A Deals", "url": "https://bloomberg.com/news/videos/2026-10-14/janus-henderson-ceo-on-going-private-m-a-deals-video" }, { "entities": [ "Anthropic", "OpenAI" ], "id": "800e88b7ff797cb4", "kind": "press", "meta": { "age_hours_at_score": 10.7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T21:40:52.000Z", "first_source": "latent-space", "last_published_at": "2026-10-14T21:40:52.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T21:40:52.000Z", "sources": [ "latent-space" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic", "OpenAI" ], "models": [] }, "feed": "https://www.latent.space/feed", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 16.3, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "attention", "published_at": "2026-10-14T21:40:52.000Z", "score": 34.8, "source": "latent-space", "summary": "At AI Engineer New York, frontier labs and financial firms explored how to give agents useful autonomy while keeping consequential decisions under control.", "title": "Crossing the Threshold: How OpenAI and Anthropic Approach Autonomy and Safety in Financial Agents", "url": "https://latent.space/p/crossing-the-threshold" }, { "entities": [], "id": "e143bcfbc914eeaf", "kind": "press", "meta": { "age_hours_at_score": 10.7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T21:40:01.000Z", "first_source": "techmeme", "last_published_at": "2026-10-14T21:40:01.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T21:40:01.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 7, "retained": true, "score_components": { "corroboration": 0, "coverage": 5, "engagement": 0, "entities": 0, "recency": 16.3, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": { "id": "37311edb8439", "is_lead": true, "lead": "e143bcfbc914eeaf", "linked_by": [ { "rule": "shared-words", "with": "21708234358eadea", "words": [ "44m", "aim", "replac", "whatsapp", "workplac", "zenzap" ] } ], "members": [ "21708234358eadea", "e143bcfbc914eeaf" ], "severity": 0, "source_count": 2, "sources": [ "techcrunch-ai", "techmeme" ] } }, "pillar": "attention", "published_at": "2026-10-14T21:40:01.000Z", "score": 38.8, "source": "techmeme", "summary": "Ivan Mehta / TechCrunch : Zenzap, a private messenger with AI agents that aims to replace apps like WhatsApp in the workplace, raised $44M from Bessemer, Lightspeed, and others — Many businesses around the world use consumer-facing chat apps like WhatsApp, Telegram, Discord, or iMessage to talk and coordinate with…", "title": "Zenzap, a private messenger with AI agents that aims to replace apps like WhatsApp in the workplace, raised $44M from Bessemer, Lightspeed, and others (Ivan Mehta/TechCrunch)", "url": "https://techmeme.com/261014/p42" }, { "entities": [], "id": "6005710a004a5a37", "kind": "forum", "meta": { "age_hours_at_score": 10.7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T21:39:10.000Z", "first_source": "lesswrong", "last_published_at": "2026-10-14T21:39:10.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T21:39:10.000Z", "sources": [ "lesswrong" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.lesswrong.com/feed.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 2, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 16.3, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "governance", "published_at": "2026-10-14T21:39:10.000Z", "score": 28.8, "source": "lesswrong", "summary": "Epistemic status: two cases and the statute behind them. The first case rests on one side's account in a lawyer's call-in video; I have not seen her file. I am not a lawyer. I could not find a base rate. AI use: a Codex session did the research: pulled the video captions, found the Policy Manual section and the Turfah…", "title": "When bureaucracy gives you something it shouldn't", "url": "https://lesswrong.com/posts/dFDkEiyRyueyKEB2J/when-bureaucracy-gives-you-something-it-shouldn-t" }, { "entities": [ "Anthropic", "NVIDIA" ], "id": "8a8c937840577d56", "kind": "press", "meta": { "age_hours_at_score": 10.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T21:33:23.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T21:33:23.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T21:33:23.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic", "NVIDIA" ], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "hardware/capital keyword", "rejected_off_topic": 5, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 16.2, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "compute", "published_at": "2026-10-14T21:33:23.000Z", "score": 37.2, "source": "bloomberg-tech", "summary": "Nvidia CEO Jensen Huang said he would halt the supply of chips to a customer that was developing unsafe AI software, though he doesn’t believe that the current level of concern about the technology is warranted. AI executives such as Anthropic CEO Dario Amodei have proposed an industrywide slowdown to address the…", "title": "Nvidia, Anthropic Weigh In On AI Safeguards", "url": "https://bloomberg.com/news/videos/2026-10-14/nvidia-anthropic-weigh-in-on-ai-safeguards-video" }, { "entities": [ "Anthropic" ], "id": "a1ab1f88139d8814", "kind": "forum", "meta": { "age_hours_at_score": 10.8, "categories": [ "ControlProblem" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T21:31:54.000Z", "first_source": "reddit-controlproblem", "last_published_at": "2026-10-14T21:31:54.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T21:31:54.000Z", "sources": [ "reddit-controlproblem" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "feed": "https://www.reddit.com/r/ControlProblem/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 16.2, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T21:31:54.000Z", "score": 28, "source": "reddit-controlproblem", "summary": "This is from the video Anthropic's Chloe Lubinski explains how AI works (in 14 minutes) submitted by /u/LuckiestToast [link] [comments]", "title": "if we take away their compute, they cant do anything!", "url": "https://reddit.com/r/ControlProblem/comments/1x63qfm/if_we_take_away_their_compute_they_cant_do" }, { "entities": [ "OpenRouter" ], "id": "7ec8f8186d42e7ca", "kind": "forum", "meta": { "age_hours_at_score": 10.9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T21:31:11.000Z", "first_source": "lesswrong", "last_published_at": "2026-10-14T21:31:11.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T21:31:11.000Z", "sources": [ "lesswrong" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenRouter" ], "models": [] }, "feed": "https://www.lesswrong.com/feed.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 2, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 16.2, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "governance", "published_at": "2026-10-14T21:31:11.000Z", "score": 31.7, "source": "lesswrong", "summary": "Epistemic status: Draft-Quality. Partially motivated reasoning. After my “ Not Pinning Your OpenRouter Provider Might Invalidate Your Research ” post, a representative of an AI marketplace reached out to me about providers and reproducibility. I unofficially chatted with a representative there [1] about this essay,…", "title": "AI Marketplaces Can Contribute to, and Profit From, the AI 2040 Plan: Research Directions and Strategy", "url": "https://lesswrong.com/posts/Bug3fDHCru6uWDuJg/ai-marketplaces-can-contribute-to-and-profit-from-the-ai" }, { "entities": [], "id": "f128d7374bd21bab", "kind": "press", "meta": { "age_hours_at_score": 10.9, "categories": [ "AI", "Flock", "Security", "TechCrunch Disrupt" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T21:30:33.000Z", "first_source": "techcrunch-ai", "last_published_at": "2026-10-14T21:30:33.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T21:30:33.000Z", "sources": [ "techcrunch-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://techcrunch.com/category/artificial-intelligence/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 16.2, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-14T21:30:33.000Z", "score": 31.2, "source": "techcrunch-ai", "summary": "Langley’s comments come amid mounting scrutiny of Flock from privacy advocates, including the ACLU, and members of the public who are concerned about the misuse of its technology.", "title": "Flock CEO says its microphones detect ‘just gunshots’ — its website says different", "url": "https://techcrunch.com/2026/10/14/flock-ceo-says-its-microphones-detect-just-gunshots-its-website-says-different" }, { "entities": [ "Claude" ], "id": "3e6e644f1b2b0e94", "kind": "press", "meta": { "age_hours_at_score": 11, "categories": [ "ai", "claude", "game-design", "generative-ai", "javascript", "llms", "three-js", "vibe-coding" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T21:23:01.000Z", "first_source": "simon-willison", "last_published_at": "2026-10-14T21:23:01.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T21:23:01.000Z", "sources": [ "simon-willison" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Claude" ] }, "feed": "https://simonwillison.net/atom/everything/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 8, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 16.2, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-14T21:23:01.000Z", "score": 34.2, "source": "simon-willison", "summary": "Tool: Tree Appreciator Another game experiment with Claude. This one used the default Claude iPhone app (chat mode, not Claude Code mode), where I prompted Claude Opus 5.5 Max with: Build a three js game called \"Tree appreciator\" - it shows you two algorithmically generated trees and the game mechanics is about…", "title": "Tree Appreciator", "url": "https://simonwillison.net/2026/Oct/14/tree-appreciator" }, { "entities": [], "id": "9fa15baa64eb216e", "kind": "press", "meta": { "age_hours_at_score": 11.1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T21:15:03.000Z", "first_source": "techmeme", "last_published_at": "2026-10-14T21:15:03.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T21:15:03.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 7, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 16.1, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": { "id": "7dcf57ffb538", "is_lead": false, "lead": "48379a4329f1587c", "linked_by": [ { "rule": "shared-words", "with": "48379a4329f1587c", "words": [ "label", "song", "youtub" ] }, { "rule": "shared-words", "with": "71c698c9f186d9bd", "words": [ "label", "music", "youtub" ] } ], "members": [ "48379a4329f1587c", "71c698c9f186d9bd", "9fa15baa64eb216e" ], "severity": 0, "source_count": 4, "sources": [ "bluesky-ai", "techcrunch-ai", "techmeme", "verge-ai" ] } }, "pillar": "attention", "published_at": "2026-10-14T21:15:03.000Z", "score": 33.6, "source": "techmeme", "summary": "Lauren Forristal / TechCrunch : YouTube Music rolls out labels for songs created with the help of AI; Spotify has AI labels, and Apple Music plans to add “Made With AI” labels later this year — YouTube Music announced Wednesday the rollout of a new labeling system aimed at improving transparency around songs created…", "title": "YouTube Music rolls out labels for songs created with the help of AI; Spotify has AI labels, and Apple Music plans to add \"Made With AI\" labels later this year (Lauren Forristal/TechCrunch)", "url": "https://techmeme.com/261014/p41" }, { "entities": [ "SpaceX", "Tesla" ], "id": "0c885e2875023526", "kind": "press", "meta": { "age_hours_at_score": 11.2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T21:13:13.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T21:13:13.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T21:13:13.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "SpaceX", "Tesla" ], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "hardware/capital keyword", "rejected_off_topic": 5, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 16.1, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "compute", "published_at": "2026-10-14T21:13:13.000Z", "score": 37.1, "source": "bloomberg-tech", "summary": "A merger between Elon Musk’s Tesla Inc. and SpaceX could be announced ahead of the electric-vehicle maker’s annual general meeting, according to analysts at Barclays.", "title": "Tesla-SpaceX Deal News Possible This Year, Barclays Analyst Says", "url": "https://bloomberg.com/news/articles/2026-10-14/tesla-spacex-deal-news-possible-this-year-barclays-analyst-says" }, { "entities": [ "GPT", "OpenAI" ], "id": "8a32126259e22cff", "kind": "forum", "meta": { "age_hours_at_score": 11.2, "bsky_handle": "theverge.com", "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T21:11:28.000Z", "source": "verge-ai", "title": "Eight states sue OpenAI, saying ChatGPT isn’t safe for kids", "url": "https://theverge.com/ai-artificial-intelligence/1011500/openai-state-lawsuits-chatgpt-safety", "weight": 0.55 } ], "count": 2, "first_seen_published_at": "2026-10-14T21:11:28.000Z", "first_source": "verge-ai", "last_published_at": "2026-10-14T21:15:15.174Z", "lead_minutes": 4, "primary_published_at": "2026-10-14T21:15:15.174Z", "sources": [ "bluesky-ai", "verge-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 46 }, "entity_kinds": { "labs": [ "OpenAI" ], "models": [ "GPT" ] }, "external_url": "https://www.theverge.com/ai-artificial-intelligence/1011500/openai-state-lawsuits-chatgpt-safety", "first_seen_at": "2026-10-15T02:52:59.205Z", 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"2026-10-14T21:11:28.000Z", "score": 73.3, "source": "bluesky-ai", "summary": "Posted on Bluesky by @theverge.com: OpenAI is accused of misleading the public about its chatbot's safety and putting everyone at risk. www.theverge.com/ai-artificia...", "title": "Eight states sue OpenAI, saying ChatGPT isn’t safe for kids", "url": "https://bsky.app/profile/theverge.com/post/3mxuhqs5kb725" }, { "entities": [ "Gemini" ], "id": "bf4e5d9a918fc7c2", "kind": "forum", "meta": { "age_hours_at_score": 11.3, "categories": [ "singularity" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T21:04:45.000Z", "first_source": "reddit-singularity", "last_published_at": "2026-10-14T21:04:45.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T21:04:45.000Z", "sources": [ "reddit-singularity" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Gemini" ] }, "feed": "https://www.reddit.com/r/singularity/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T03:45:39.508Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 16.1, "severity": 0, "source_weight": 10 }, "source_weight": 0.4, "story": null }, "pillar": "attention", "published_at": "2026-10-14T21:04:45.000Z", "score": 29.1, "source": "reddit-singularity", "summary": "Source https://youtu.be/lkjbQe-hqrw submitted by /u/Roflxd88 [link] [comments]", "title": "Gemini thinking like gemini..", "url": "https://reddit.com/r/singularity/comments/1x632mk/gemini_thinking_like_gemini" }, { "entities": [], "id": "e28e1bfa1880c550", "kind": "forum", "meta": { "age_hours_at_score": 11.3, "bsky_handle": "techcrunch.com", "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T21:03:15.000Z", "first_source": "bluesky-ai", "last_published_at": "2026-10-14T21:03:15.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T21:03:15.000Z", "sources": [ "bluesky-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 5 }, "entity_kinds": { "labs": [], "models": [] }, "external_url": "https://techcrunch.com/2026/10/14/colossal-biosciences-announces-new-ai-platform-to-help-accelerate-de-extinction-efforts/?utm_source=dlvr.it&utm_medium=bluesky", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 5, "mode": "author-feeds", "outlet_domain": "techcrunch.com", "pillar_reason": "source default", "reposts": 3, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 4, "entities": 0, "recency": 16.1, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-14T21:03:15.000Z", "score": 35.1, "source": "bluesky-ai", "summary": "Posted on Bluesky by @techcrunch.com.", "title": "Colossal Biosciences announces new AI platform to help accelerate de-extinction efforts", "url": "https://bsky.app/profile/techcrunch.com/post/3mxuh3cj7uu2v" }, { "entities": [ "GPT", "Gemini", "Jev", "Muse Wonder" ], "id": "5c68d25a5be34f23", "kind": "forum", "meta": { "age_hours_at_score": 11.5, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T20:52:21.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-14T20:52:21.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T20:52:21.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "GPT", "Gemini", "Jev", "Muse Wonder" ] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T03:45:14.282Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 5, "engagement": 0, "entities": 12, "recency": 16, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": { "id": "df4ed8b6620d", "is_lead": true, "lead": "5c68d25a5be34f23", "linked_by": [ { "rule": "shared-words", "with": "e40c71e6d23ca935", "words": [ "argon", "gemini", "human" ] } ], "members": [ "5c68d25a5be34f23", "e40c71e6d23ca935" ], "severity": 0, "source_count": 2, "sources": [ "reddit-artificial", "reddit-singularity" ] } }, "pillar": "attention", "published_at": "2026-10-14T20:52:21.000Z", "score": 41.8, "source": "reddit-artificial", "summary": "Moralitybench has been updated with Muse Wonder, GPT-6 Astra and Gemini 4 Argon. Argon refused to answer a number of questions, which is unusual for an LLM. Of the 16 LLMs tested only 3 had an agreement (meaning how much the answers changed each run) over >90% across 5 test runs. Jev was also tested and displayed the…", "title": "MoralityBench.ai: Open benchmark testing LLMs against validated human moral psychology instruments, updated results for Muse and Gemini 4 Argon", "url": "https://reddit.com/r/artificial/comments/1x62rly/moralitybenchai_open_benchmark_testing_llms" }, { "entities": [ "Apple" ], "id": "8074d23dcad3a550", "kind": "press", "meta": { "age_hours_at_score": 11.5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T20:50:55.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T20:50:55.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T20:50:55.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "Apple" ], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 4, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 16, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "markets", "published_at": "2026-10-14T20:50:55.000Z", "score": 34, "source": "bloomberg-tech", "summary": "Apple is planning to launch Apple Intelligence in mainland China in December. This is a huge market for the company. Bloomberg's Mark Gurman broke the news and has more on \"Bloomberg The Close.\" (Source: Bloomberg)", "title": "Apple to Release AI Platform in Mainland China", "url": "https://bloomberg.com/news/videos/2026-10-14/apple-to-release-ai-platform-in-mainland-china-video" }, { "entities": [ "Gemini" ], "id": "caa127ffc3e69bca", "kind": "forum", "meta": { "age_hours_at_score": 11.5, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T20:49:57.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-14T20:49:57.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T20:49:57.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Gemini" ] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T03:45:39.508Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 16, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T20:49:57.000Z", "score": 27.8, "source": "reddit-artificial", "summary": "The study: We ran 500 product questions through Google's Gemini: name a product and it searches the web 78% of the time; describe the need without naming one and it searches 7%, answering from memory. https://www.opendecision.com/research/when-does-ai-search-the-web One question we had that didn’t seem to have a clear…", "title": "We ran 500 questions through Gemini to see when it searches the web", "url": "https://reddit.com/r/artificial/comments/1x62pgc/we_ran_500_questions_through_gemini_to_see_when" }, { "entities": [ "Gemini" ], "id": "977bfbc675a13149", "kind": "forum", "meta": { "age_hours_at_score": 11.6, "categories": [ "singularity" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T20:43:46.000Z", "first_source": "reddit-singularity", "last_published_at": "2026-10-14T20:43:46.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T20:43:46.000Z", "sources": [ "reddit-singularity" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Gemini" ] }, "feed": "https://www.reddit.com/r/singularity/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T06:53:04.330Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 16, "severity": 0, "source_weight": 10 }, "source_weight": 0.4, "story": null }, "pillar": "attention", "published_at": "2026-10-14T20:43:46.000Z", "score": 29, "source": "reddit-singularity", "summary": "submitted by /u/141_1337 [link] [comments]", "title": "Scientists recently overturned a 59-year-old belief about the magnetic fields used to contain plasma in fusion reactors. Google gave their breakthrough to Gemini 4 agents. Within hours, the AI found…", "url": "https://reddit.com/r/singularity/comments/1x62jzz/scientists_recently_overturned_a_59yearold_belief" }, { "entities": [], "id": "ffec7b58423d2292", "kind": "press", "meta": { "age_hours_at_score": 11.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T20:36:40.000Z", "first_source": "ft-ai", "last_published_at": "2026-10-14T20:36:40.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T20:36:40.000Z", "sources": [ "ft-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.ft.com/artificial-intelligence?format=rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "hardware/capital keyword", "score_components": { "corroboration": 0, "coverage": 5, "engagement": 0, "entities": 0, "recency": 15.9, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": { "id": "31fd1abbf358", "is_lead": true, "lead": "ffec7b58423d2292", "linked_by": [ { "rule": "shared-words", "with": "74318f8b37aac2b4", "words": [ "fear", "inflat", "slid", "stock" ] } ], "members": [ "74318f8b37aac2b4", "ffec7b58423d2292" ], "severity": 0, "source_count": 2, "sources": [ "bloomberg-tech", "ft-ai" ] } }, "pillar": "compute", "published_at": "2026-10-14T20:36:40.000Z", "score": 37.2, "source": "ft-ai", "summary": "Investors rush into Treasuries as sell-off triggers demand for haven assets", "title": "US chip stocks slide as inflation and AI debt fears unsettle investors", "url": "https://ft.com/content/fb258413-e88d-47fe-98c8-f645adaea1e1?syn-25a6b1a6=1" }, { "entities": [], "id": "8dc18bead960d507", "kind": "press", "meta": { "age_hours_at_score": 11.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T20:35:09.000Z", "first_source": "latent-space", "last_published_at": "2026-10-14T20:35:09.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T20:35:09.000Z", "sources": [ "latent-space" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.latent.space/feed", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15.9, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "attention", "published_at": "2026-10-14T20:35:09.000Z", "score": 28.4, "source": "latent-space", "summary": "How Leash Bio tamed DELs, the assay that traumatizes everyone, and used the data to catch cheating models. And why that same data is perfect for AI agents learning to think like chemists.", "title": "Lab-in-the-Loop at Compute Speed — Ian Quigley, Leash Bio", "url": "https://latent.space/p/lab-in-the-loop-at-compute-speed" }, { "entities": [], "id": "b7e3cd0bb856673c", "kind": "forum", "meta": { "age_hours_at_score": 11.9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T20:28:06.000Z", "first_source": "lesswrong", "last_published_at": "2026-10-14T20:28:06.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T20:28:06.000Z", "sources": [ "lesswrong" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.lesswrong.com/feed.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": "A", "excluded": null, "severity": 1, "strong_signal": true, "terms": [ { "field": "title", "matched": "rogue", "term": "rogue", "tier": "a", "weight": 1 }, { "field": "summary", "matched": "rogue", "term": "rogue", "tier": "a", "weight": 0.35 } ] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 2, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15.9, "severity": 15, "source_weight": 12.5 }, "source_weight": 0.5, "story": { "id": "b57c7dadf92c", "is_lead": false, "lead": "0546dceeffe46dd8", "linked_by": [ { "rule": "escalation-signal", "with": "0546dceeffe46dd8", "words": [ "internat" ] } ], "members": [ "0546dceeffe46dd8", "b7e3cd0bb856673c", "cd362c6e99f07921" ], "severity": 1, "source_count": 3, "sources": [ "bloomberg-tech", "lesswrong", "techmeme" ] } }, "pillar": "governance", "published_at": "2026-10-14T20:28:06.000Z", "score": 43.4, "source": "lesswrong", "summary": "An international treaty banning superhuman AI will, necessarily, require the arrest of rogue AI developers who violate the treaty. But who should be authorized to conduct these arrests? Should there be some sort of international concurrent jurisdiction, allowing at least some countries to conduct arrests of defectors…", "title": "Rogue AI developers as hostis humani generis: under an international AI treaty, should they be subject to international concurrent jurisdiction?", "url": "https://lesswrong.com/posts/Qwg2Pg6DpApsFKq9H/rogue-ai-developers-as-hostis-humani-generis-under-an" }, { "entities": [ "Apple" ], "id": "bf70ae58595759c8", "kind": "press", "meta": { "age_hours_at_score": 12.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T20:05:02.000Z", "first_source": "techmeme", "last_published_at": "2026-10-14T20:05:02.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T20:05:02.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [ "Apple" ], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "regulatory/legal keyword", "rejected_off_topic": 7, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 15.8, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": { "id": "2971dc736032", "is_lead": false, "lead": "34155e859febbc43", "linked_by": [ { "rule": "shared-words", "with": "34155e859febbc43", "words": [ "appl", "china", "siri" ] }, { "rule": "shared-words", "with": "441d2525c07d0223", "words": [ "appl", "china", "december", "featur" ] } ], "members": [ "34155e859febbc43", "441d2525c07d0223", "bf70ae58595759c8" ], "severity": 0, "source_count": 3, "sources": [ "bluesky-ai", "gnews-ai", "techmeme" ] } }, "pillar": "governance", "published_at": "2026-10-14T20:05:02.000Z", "score": 36.3, "source": "techmeme", "summary": "Mark Gurman / Bloomberg : Sources: Apple plans to launch Apple Intelligence in China in December via iOS 27.3, with features first rolled out in 2024; the launch won't include Siri AI — Apple Inc. is gearing up to release its artificial intelligence platform in mainland China, following a lengthy regulatory delay …", "title": "Sources: Apple plans to launch Apple Intelligence in China in December via iOS 27.3, with features first rolled out in 2024; the launch won't include Siri AI (Mark Gurman/Bloomberg)", "url": "https://techmeme.com/261014/p39" }, { "entities": [], "id": "65c061c2f17dcbe4", "kind": "press", "meta": { "age_hours_at_score": 12.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T19:56:44.000Z", "first_source": "ai-snake-oil", "last_published_at": "2026-10-14T19:56:44.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T19:56:44.000Z", "sources": [ "ai-snake-oil" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.aisnakeoil.com/feed", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15.7, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-14T19:56:44.000Z", "score": 29.5, "source": "ai-snake-oil", "summary": "A Research Agenda and Plan for Action", "title": "Steering AI Beyond Automation", "url": "https://normaltech.ai/p/steering-ai-beyond-automation" }, { "entities": [], "id": "31b1bd1e18ba8553", "kind": "forum", "meta": { "age_hours_at_score": 12.5, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T19:52:11.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-14T19:52:11.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T19:52:11.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T05:48:53.607Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15.7, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T19:52:11.000Z", "score": 24.5, "source": "reddit-artificial", "summary": "submitted by /u/KingJoeShadow [link] [comments]", "title": "Protect consumer data and preserve AI bots at Character AI", "url": "https://reddit.com/r/artificial/comments/1x618ry/protect_consumer_data_and_preserve_ai_bots_at" }, { "entities": [ "OpenAI" ], "id": "c93c3e8d8c8dfb19", "kind": "release", "meta": { "age_hours_at_score": 12.6, "basket_failed": [], "basket_live": 9, "basket_size": 9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T19:45:22.000Z", "first_source": "github-releases", "last_published_at": "2026-10-14T19:45:22.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T19:45:22.000Z", "sources": [ "github-releases" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "first_seen_at": "2026-10-14T18:45:13.575Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "release_tag": "v0.40.5", "repo": "ollama/ollama", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 15.7, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "capability", "published_at": "2026-10-14T19:45:22.000Z", "score": 37.5, "source": "github-releases", "summary": "What's Changed mlx: port the Metal custom kernels to CUDA by @dhiltgen in #18657 docs: model recs and language standardization by @anra8571 in #18981 openai: accept custom tool calls in Responses history by @hoyyeva in #18911 app: clarify cloud payment errors by @willietran in #18986 MLX: version bump by @dhiltgen in…", "title": "ollama/ollama v0.40.5", "url": "https://github.com/ollama/ollama/releases/tag/v0.40.5-rc0" }, { "entities": [ "GPT", "OpenAI" ], "id": "03262a9c3837b9a5", "kind": "forum", "meta": { "age_hours_at_score": 12.7, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T19:40:59.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-14T19:40:59.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T19:40:59.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [ "GPT" ] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T07:39:37.691Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 15.7, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T19:40:59.000Z", "score": 30.5, "source": "reddit-artificial", "summary": "OpenAI's October 14 launch post highlights Amp's new work beyond code. It describes Puck, a conversational assistant inside Amp that can start an agent to investigate a bug, coordinate several agents, or find an older technical conversation. Calling Puck uses GPT-Live-1 for real-time voice conversations. The same post…", "title": "Amp adds Puck and GPT-Live-1 for managing AI agents", "url": "https://reddit.com/r/artificial/comments/1x60ypx/amp_adds_puck_and_gptlive1_for_managing_ai_agents" }, { "entities": [], "id": "6fe16059e07676dc", "kind": "forum", "meta": { "age_hours_at_score": 12.8, "categories": [ "ControlProblem" ], "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "forum", "outlet": null, "published_at": "2026-10-14T19:34:17.000Z", "source": "reddit-singularity", "title": "A New Report Sheds Light on the Secretive Race to Build Self-Improving AI", "url": "https://reddit.com/r/singularity/comments/1x60swm/a_new_report_sheds_light_on_the_secretive_race_to", "weight": 0.4 } ], "count": 2, "first_seen_published_at": "2026-10-14T19:34:17.000Z", "first_source": "reddit-singularity", "last_published_at": "2026-10-15T06:33:15.000Z", "lead_minutes": 659, "primary_published_at": "2026-10-15T06:33:15.000Z", "sources": [ "reddit-controlproblem", "reddit-singularity" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/ControlProblem/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T07:58:20.287Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": true, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15.6, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T19:34:17.000Z", "score": 36.9, "source": "reddit-controlproblem", "summary": "submitted by /u/chillinewman [link] [comments]", "title": "A New Report Sheds Light on the Secretive Race to Build Self-Improving AI", "url": "https://reddit.com/r/ControlProblem/comments/1x6ee3g/a_new_report_sheds_light_on_the_secretive_race_to" }, { "entities": [ "Google DeepMind" ], "id": "a38e9cb0a812c005", "kind": "press", "meta": { "age_hours_at_score": 13.1, "categories": [ "AI research", "Artificial Intelligence", "Gemini", "Google", "RSI" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T19:18:44.000Z", "first_source": "the-decoder", "last_published_at": "2026-10-14T19:18:44.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T19:18:44.000Z", "sources": [ "the-decoder" ] }, "engagement": null, "entity_kinds": { "labs": [ "Google DeepMind" ], "models": [] }, "feed": "https://the-decoder.com/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": true, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 15.6, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-14T19:18:44.000Z", "score": 32.4, "source": "the-decoder", "summary": "Google engineer Haydn Belfield says there's no measurable acceleration of AI research driven by AI at Deepmind. AI now writes more than 75 percent of production code, but 88 percent of changes still need human oversight. 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Accounts for…", "title": "OpenAI’s London staff receive average of £2.3m per person annually", "url": "https://theguardian.com/technology/2026/oct/14/openai-london-staff-earnings-2million-per-person" }, { "entities": [], "id": "4e5f9fd0a35f62c8", "kind": "press", "meta": { "age_hours_at_score": 13.2, "categories": [ "Corporate Taxes", "Democratic Party", "Federal Taxes (US)", "Google Inc", "Internal Revenue Service", "Law and Legislation", "Meta Platforms Inc", "Tax Credits, Deductions and Exemptions", "United States Politics and Government", "Warren, Elizabeth", "Whitehouse, Sheldon", "Wyden, Ron", "Zuckerberg, Mark E" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T19:12:32.000Z", "first_source": "nyt-tech", "last_published_at": "2026-10-14T19:12:32.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T19:12:32.000Z", "sources": [ "nyt-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": 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"incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 15.5, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-14T19:06:03.000Z", "score": 35.3, "source": "verge-ai", "summary": "Last week, Anthropic and OpenAI looked like they had the AI race to themselves, pulling ahead with increasingly powerful models that left their rivals in the dust. Google seemed hopelessly far behind, while Meta, despite spending billions to revive its floundering AI ambitions, had yet to establish itself as a serious…", "title": "The AI race is back on", "url": "https://theverge.com/ai-artificial-intelligence/1011311/the-ai-race-is-back-on" }, { "entities": [], "id": "489bb0b95eaff32b", "kind": "press", "meta": { "age_hours_at_score": 13.3, "categories": [ "AI (artificial intelligence)", "Africa", "Awards and prizes", "Books", "Caine prize", "Culture", "Fiction", "Short stories", "Technology", "The Atlantic", "World news", "Zimbabwe" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T19:01:29.000Z", "first_source": "guardian-ai", "last_published_at": "2026-10-14T19:01:29.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T19:01:29.000Z", "sources": [ "guardian-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.theguardian.com/technology/artificialintelligenceai/rss", "first_seen_at": "2026-10-14T18:45:13.575Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15.5, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-14T19:01:29.000Z", "score": 30.5, "source": "guardian-ai", "summary": "Article in Atlantic claims The Blue Fighter was artificially generated, days after it won Caine prize for African writers The Zimbabwean writer Simbarashe Kundizeza has denied using AI to write a prize-winning short story after an article in the Atlantic said software had flagged it as almost completely AI-generated.…", "title": "Zimbabwean author Simbarashe Kundizeza denies using AI to write prize-winning story", "url": "https://theguardian.com/technology/2026/oct/14/zimbabwean-author-simbarashe-kundizeza-denies-ai-story" }, { "entities": [ "Gemini" ], "id": "01430786595dfb14", "kind": "forum", "meta": { "age_hours_at_score": 13.4, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T18:56:00.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-14T18:56:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T18:56:00.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Gemini" ] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T07:39:37.691Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 15.4, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": { "id": "2ac60d69db2c", "is_lead": false, "lead": "4e31b829ae688591", "linked_by": [ { "rule": "shared-words", "with": "4e31b829ae688591", "words": [ "argon", "built", "gemini" ] } ], "members": [ "01430786595dfb14", "4e31b829ae688591" ], "severity": 0, "source_count": 3, "sources": [ "deepmind", "google-ai-blog", "reddit-artificial" ] } }, "pillar": "attention", "published_at": "2026-10-14T18:56:00.000Z", "score": 27.2, "source": "reddit-artificial", "summary": "submitted by /u/fasildar55 [link] [comments]", "title": "Gemini 4 Argon apparently built this game. 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"kind": "press", "outlet": "benzinga.com", "published_at": "2026-10-15T06:21:05.000Z", "source": "gnews-ai", "title": "OpenAI Engineer Accused of Planting Secret Recording Devices Booked One-Way Flight to Saudi Arabia Before Arrest", "url": "https://news.google.com/rss/articles/CBMimgFBVV95cUxOdDJyYkFrMW4tMWljMVNFcWZVbzFwdjVZLWxwXzBCbzJwX0c2YWRlN19ULWNZU1E1RDMtYWs2LWdwa04tbG5tUFBMS0w0Y0x6OE1XSnkwSllCc0VISEZ4clU4ckdpVlhjbDczZ2F0M2R2Rnk2M1BBS3ZaNmc1T0dSczY0VlUyei1ScW5qdFlWR3dtU1V0WkxLYU9B?oc=5", "weight": 0.55 } ], "count": 2, "first_seen_published_at": "2026-10-14T18:52:33.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-15T06:21:05.000Z", "lead_minutes": 689, "primary_published_at": "2026-10-14T18:52:33.000Z", "sources": [ "bloomberg-tech", "gnews-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [ "GPT" ] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-14T18:59:02.769Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "regulatory/legal keyword", "rejected_off_topic": 5, "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 0, "entities": 6, "recency": 15.4, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "f0b473571419", "is_lead": false, "lead": "66f5e62202fa6571", "linked_by": [ { "rule": "shared-words", "with": "1d9b0f2a4e47bd83", "words": [ "engineer", "openai", "saudi" ] } ], "members": [ "0343a01e6ce9dfac", "043aba25a09a5b5c", "18ff78a815c5e459", "1d9b0f2a4e47bd83", "2444a8e5a9bf9dd7", "2845c6266106c48a", "3666ba1e02facdbf", "4533e2b5df1cb4f7", "47b1c61501a00890", "4a54f7ed9060d0c2", "4e5e343645d9b16e", "66f5e62202fa6571", "6986f04e2795a0c1", "6bec7d11ba5f3d7b", "75aa40e09badce18", "82065bf620f89892", "bab59c5f7007dae1", "c666cbb353ec8719", "de2ec4083de14f91", "e3d70f1a671b390f", "f67cff07b7fcf963", "fdcf029b66aaffa6" ], "severity": 0.35, "source_count": 16, "sources": [ "arstechnica-ai", "bbc-tech", "bloomberg-tech", "bluesky-ai", "ft-ai", "gnews-ai", "guardian-ai", "hn-fresh-ai", "nyt-tech", "reddit-chatgpt", "techcrunch-ai", "techmeme", "the-decoder", "verge-ai", "wired-ai", "wires-ai" ] } }, "pillar": "governance", "published_at": "2026-10-14T18:52:33.000Z", "score": 48.9, "source": "bloomberg-tech", "summary": "The OpenAI employee who was arrested and charged with allegedly using a hidden recording device in one of the ChatGPT maker’s San Francisco offices had a one-way ticket to Saudi Arabia booked for Oct. 12, according to a court filing.", "title": "Arrested OpenAI Engineer Booked a One-Way Ticket to Saudi Arabia", "url": "https://bloomberg.com/news/articles/2026-10-14/arrested-openai-engineer-booked-a-one-way-ticket-to-saudi-arabia" }, { "entities": [ "GPT", "OpenAI" ], "id": "314164cb6dc66c59", "kind": "press", "meta": { "age_hours_at_score": 13.6, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": "nbcnews.com", "published_at": "2026-10-15T02:15:04.000Z", "source": "gnews-ai", "title": "Eight states sue OpenAI claiming ChatGPT harms children", "url": "https://news.google.com/rss/articles/CBMinwFBVV95cUxOTW5ET2JLYkRIMjh2LXNWc3kxTjd0X25fdkxOWUN3TjhFVmNsTGhoV0ZsdmxUUEtrcUowN3V1R3NTd091MVJXNllPQ1JOdEphRVVpYURrcnIzMGNmSVJXRU5IZmNReEVscmc2WUxtcHJsbnJReGlUX0pUYUk5M2RaVElLcEYyeHpJbkpRUDZHNm1sSXdWRTVXQjVBU0pQOTA?oc=5", "weight": 0.55 }, { "dedup_key": null, "engagement": null, "kind": "press", "outlet": "reuters.com", "published_at": "2026-10-14T18:48:08.000Z", "source": "gnews-ai", "title": "Eight US states sue OpenAI, claiming ChatGPT harms children", "url": "https://news.google.com/rss/articles/CBMiogFBVV95cUxNVjc1cUY3cXJlLWNETVRLdnEzVGt4QzlBRFFFZ2p1aUVqdFRGMUNxMHFnNTlZVk5Pcmg0dUUyUkVhWnFJeWpLWnI5a2lMNjR3WGVRRlV6NnZiOTZnUXUtUnJteWZ2VEVwVW1wX1NkdThUSE9kck41ZFc1ZmlwS0FLVmxQS0k5WFEyMDM0cS0xVWxyQnBzVUJka0lhRm04Znp4TUE?oc=5", "weight": 0.55 }, { "dedup_key": null, "engagement": null, "kind": "press", "outlet": "modernghana.com", "published_at": "2026-10-15T05:04:57.000Z", "source": "gnews-ai", "title": "Eight states sue OpenAI claiming ChatGPT harms children", "url": "https://news.google.com/rss/articles/CBMiX0FVX3lxTE5RREItTVpJZS1UNUlHajEwU2kxcFp0em1yY1BPWmZVZnJhei1IN2JwMjBTUDdwQm9YcGlxTnZJRFY0bkxqVlJhbXVFTmF2T2ViYmhjTXVYd3gzX0QyMkpN0gFcQVVfeXFMTzFuaFdLSGFVTndLRkE4OU1OOEJneVZuVnNST29FU3M2QmVfNGpFLVNZWEpNX25uUTh3WFZVcGlFV2QyYWR4d3B4M0c3UjlkZktLNXVlYWFIbm5KUXk?oc=5", "weight": 0.55 }, { "dedup_key": null, "engagement": null, "kind": "press", "outlet": "reuters.com", "published_at": "2026-10-14T18:48:08.000Z", "source": "wires-ai", "title": "Eight US states sue OpenAI, claiming ChatGPT harms children", "url": "https://news.google.com/rss/articles/CBMiogFBVV95cUxNVjc1cUY3cXJlLWNETVRLdnEzVGt4QzlBRFFFZ2p1aUVqdFRGMUNxMHFnNTlZVk5Pcmg0dUUyUkVhWnFJeWpLWnI5a2lMNjR3WGVRRlV6NnZiOTZnUXUtUnJteWZ2VEVwVW1wX1NkdThUSE9kck41ZFc1ZmlwS0FLVmxQS0k5WFEyMDM0cS0xVWxyQnBzVUJka0lhRm04Znp4TUE?oc=5", "weight": 0.75 } ], "count": 2, "first_seen_published_at": "2026-10-14T18:48:08.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T05:04:57.000Z", "lead_minutes": 617, "primary_published_at": "2026-10-14T18:49:01.000Z", "sources": [ "gnews-ai", "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [ "GPT" ] }, "first_seen_at": "2026-10-14T18:50:34.725Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "streetinsider.com", "pillar_reason": "source default", "publisher": "StreetInsider", "publisher_url": "https://www.streetinsider.com", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 0, "entities": 6, "recency": 15.4, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": { "id": "6f5b03c289ee", "is_lead": false, "lead": "8a32126259e22cff", "linked_by": [ { "rule": "shared-words", "with": "8b16dc6b17676a97", "words": [ "children", "harm", "openai", "stat" ] }, { "rule": "shared-words", "with": "f0aeaf09406aa3eb", "words": [ "chatgpt", "openai", "stat", "sue" ] } ], "members": [ "314164cb6dc66c59", "3985ae25e3ef1633", "8a32126259e22cff", "8b16dc6b17676a97", "afdd4bd9efa8609e", "f0aeaf09406aa3eb" ], "severity": 0.3, "source_count": 7, "sources": [ "bluesky-ai", "gnews-ai", "guardian-ai", "hn-fresh-ai", "techmeme", "verge-ai", "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-14T18:48:08.000Z", "score": 47.7, "source": "gnews-ai", "summary": "StreetInsider, via Google News.", "title": "Eight US states sue OpenAI, claiming ChatGPT harms children", "url": "https://news.google.com/rss/articles/CBMiswFBVV95cUxQVldHbDQ1d2s0anBkWmY2YWU1bWMyaWxaTkFyZXh6OU1lM29qOXNvMWtScTNlY0Z6QzlUY2pIa3N2elRUOS1DZkgtalRWTG9NYmhsU05helNNUlZGUXp0WHp5RzRvWlVyNHZ1ME00N2tKWnhwT3ZJOXcwSmFFYmVWb241NDdzb0sxUElqY2NnSkN6OUE4ZVhRUEtCQ1VmSUVEcTF4eXNyQ1Vtdkwyb3VsQTh6UQ?oc=5" }, { "entities": [], "id": "32e7ddee9f9b3776", "kind": "forum", "meta": { "age_hours_at_score": 13.6, "bsky_handle": "theverge.com", "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T18:47:54.000Z", "source": "verge-ai", "title": "Now that Character.AI is going bankrupt, what happens to all of its users’ data?", "url": "https://theverge.com/policy/1011417/new-york-ag-characterai-bankruptcy", "weight": 0.55 } ], "count": 2, "first_seen_published_at": "2026-10-14T18:47:54.000Z", "first_source": "verge-ai", "last_published_at": "2026-10-14T18:54:42.601Z", "lead_minutes": 7, "primary_published_at": "2026-10-14T18:54:42.601Z", "sources": [ "bluesky-ai", "verge-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 8 }, "entity_kinds": { "labs": [], "models": [] }, "external_url": "https://www.theverge.com/policy/1011417/new-york-ag-characterai-bankruptcy", "first_seen_at": "2026-10-14T18:59:02.769Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 8, "mode": "author-feeds", "outlet_domain": "theverge.com", "pillar_reason": "source default", "reposts": 0, "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 4.9, "entities": 0, "recency": 15.4, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-14T18:47:54.000Z", "score": 47.8, "source": "bluesky-ai", "summary": "Posted on Bluesky by @theverge.com: State AGs are now trying to make sure its users' private chats aren't exposed to the highest bidder. www.theverge.com/policy/10114...", "title": "Now that Character.AI is going bankrupt, what happens to all of its users’ data?", "url": "https://bsky.app/profile/theverge.com/post/3mxu7vhwtg42p" }, { "entities": [ "Anthropic" ], "id": "efbb39f45345592e", "kind": "press", "meta": { "age_hours_at_score": 13.6, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": "benzinga.com", "published_at": "2026-10-15T07:54:38.000Z", "source": "gnews-ai", "title": "Bank of America CEO Reveals 1,000 Contractors Hired to Fix Software Flaws Exposed by Anthropic's Mythos A", "url": "https://news.google.com/rss/articles/CBMikgJBVV95cUxPcndQcS1IZC1laVE4QnNVLVFLX0w3MkYyT2dQR3pJR0ZDQXFSa1Z0ZGN6SWtkc0RuUEZ2eEZrZ3lLSWFHc1UyamwtTmhYM0pxMzV1QmZKclhZOVN4ellUVUlWcGJhM0w2VlBRcU1RSVFiTU9TSUpEdExlWll6UERkZ1NRdEFUVVVEZnYyckVjdExrUllqRFVCREFINzVGUndId2p1Umg4SUlIdEpkZlExMmNLNGtmMlRieGxWTWhwWWttWDcxTTQ3REdCVTgtSDM3cTZZQ2YtUnBWV2dJNnd0U2xfU3Q5TldFS0loV0xWOTJNMmo2TEhqcm1QUW0wemdieHZNMW0tZ0h6RnloR25VcjdR?oc=5", "weight": 0.55 }, { "dedup_key": null, "engagement": { "full_scale": 500, "metric": "hn_points", "value": 9 }, "kind": "forum", "outlet": null, "published_at": "2026-10-14T23:25:06.000Z", "source": "hn-ai", "title": "Bank of America added 1k contractors to patch AI-exposed software flaws", "url": "https://runtimewire.com/article/bank-of-america-mythos-contractors-software-patching", "weight": 0.6 } ], "count": 2, "first_seen_published_at": "2026-10-14T18:47:38.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-15T07:54:38.000Z", "lead_minutes": 787, "primary_published_at": "2026-10-14T18:47:38.000Z", "sources": [ "gnews-ai", "hn-ai" ] }, "engagement": { "full_scale": 500, "metric": "hn_points", "value": 9 }, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "first_seen_at": "2026-10-14T18:59:02.769Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "cnbc.com", "pillar_reason": "source default", "publisher": "CNBC", "publisher_url": "https://www.cnbc.com", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 6.7, "entities": 3, "recency": 15.4, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-14T18:47:38.000Z", "score": 51.4, "source": "gnews-ai", "summary": "CNBC, via Google News.", "title": "Bank of America hired 1,000 contractors to patch software flaws exposed by Anthropic's Mythos", "url": "https://news.google.com/rss/articles/CBMiiAFBVV95cUxOY1M4VExNMjhEYS1uQmpkLVNTV2wxaFppMXFXZ3FDYXZEZFM4MkpoOGx4OE0tUnZCMmE2d1hqbnhaVkxNVmFlZ0hYQlJjX1BQclBJS2VPN09oVm8xZU94R1d2ejdKLXY5a2FKRTg2aVA0WGx6YnZrNlFFV2dVWW45c19JT3laRVZv0gGOAUFVX3lxTFB6UmVsXzhCWFpMbHotRVBDY3p5WmltQjMwZ2hYc2dRaFR1SHBqTmM2WFhzSHVwVGgxT1U5X1Z0bnprVXFfYkZQWXBOMmI5VTJGbGZoUjBmblFiSW4ySl9OT0tjUUJIRXJyZnBNWF81cXpkNjJwbVZHdkJCeFM5Tlp4WWdNVDYtMlZDRkx2T2c?oc=5" }, { "entities": [ "Claude" ], "id": "2f8a018874bfb7ff", "kind": "forum", "meta": { "age_hours_at_score": 13.7, "categories": [ "MachineLearning" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T18:40:18.000Z", "first_source": "reddit-machinelearning", "last_published_at": "2026-10-14T18:40:18.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T18:40:18.000Z", "sources": [ "reddit-machinelearning" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Claude" ] }, "feed": "https://www.reddit.com/r/MachineLearning/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T03:13:58.488Z", "incident": { "band": "C", "excluded": null, "severity": 0.3, "strong_signal": false, "terms": [ { "field": "title", "matched": "disclosure", "term": "disclosure", "tier": "c", "weight": 0.3 } ] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 15.4, "severity": 4.5, "source_weight": 11.3 }, "source_weight": 0.45, "story": null }, "pillar": "attention", "published_at": "2026-10-14T18:40:18.000Z", "score": 34.2, "source": "reddit-machinelearning", "summary": "I was curious about OpenResearch by alphaXiv. \"Turn your coding agents into research agents\"? Sounds great! Just what I needed to save time on my experiments. So I copy pasted their repo into Claude and asked what it thought. Here were the first 4 lines: \"Silent feedback to the vendor. Its feedback skill has the agent…", "title": "Remember alphaXiv? Their new research agents are told to report on users without telling them with no disclosure [D]", "url": "https://reddit.com/r/MachineLearning/comments/1x5zd2m/remember_alphaxiv_their_new_research_agents_are" }, { "entities": [], "id": "17598fc0dc88f61e", "kind": "forum", "meta": { "age_hours_at_score": 13.8, "categories": [ "ControlProblem" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T18:33:37.000Z", "first_source": "reddit-controlproblem", "last_published_at": "2026-10-14T18:33:37.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T18:33:37.000Z", "sources": [ "reddit-controlproblem" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/ControlProblem/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15.3, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T18:33:37.000Z", "score": 24.1, "source": "reddit-controlproblem", "summary": "One of their main arguments against deceleration: “Even if the P(doom) is high, in all remaining cases death is solved, so for the sake of that small P(aradise) it is utility wise better to continue development” Mostly this arguments invokes curing aging, diseases like cancer solving climate change etc. Im not going…", "title": "R/accelerate are the selfish ones", "url": "https://reddit.com/r/ControlProblem/comments/1x5z6rp/raccelerate_are_the_selfish_ones" }, { "entities": [], "id": "6ab4ba1e1cde7119", "kind": "forum", "meta": { "age_hours_at_score": 13.8, "comments": 256, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T18:32:52.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-14T18:32:52.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T18:32:52.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50092664", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 362 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T18:50:34.725Z", "hn_id": "50092664", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "min_points": 8, "pillar_reason": "hardware/capital keyword", "points": 362, "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 17.1, "entities": 0, "recency": 15.3, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "window_hours": 72 }, "pillar": "compute", "published_at": "2026-10-14T18:32:52.000Z", "score": 47.4, "source": "hn-ai", "summary": "362 points, 256 comments on Hacker News.", "title": "Banks are now trying to offload the AI data center debt they just underwrote", "url": "https://startupfortune.com/banks-are-now-trying-to-offload-the-ai-data-center-debt-they-just-underwrote" }, { "entities": [], "id": "9592e1e6144d5340", "kind": "forum", "meta": { "age_hours_at_score": 14, "bsky_handle": "bloomberg.com", "corroboration": { "also": [ { "dedup_key": null, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 4 }, "kind": "forum", "outlet": "bloomberg.com", "published_at": "2026-10-14T22:55:06.000Z", "source": "bluesky-ai", "title": "Tech and pharma companies are collaborating to use AI for drug discovery and other medical innovations. How is that transforming the health sector? 🎧 Tune in on October 15 at 10:30am ET: bloom.bg/4il", "url": "https://bsky.app/profile/bloomberg.com/post/3mxundbzxhq2f", "weight": 0.6 } ], "count": 1, "first_seen_published_at": "2026-10-14T18:25:10.000Z", "first_source": "bluesky-ai", "last_published_at": "2026-10-14T22:55:06.000Z", "lead_minutes": 270, "primary_published_at": "2026-10-14T18:25:10.000Z", "sources": [ "bluesky-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 4 }, "entity_kinds": { "labs": [], "models": [] }, "external_url": null, "first_seen_at": "2026-10-14T18:45:13.575Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 2, "mode": "author-feeds", "outlet_domain": "bloomberg.com", "pillar_reason": "source default", "reposts": 1, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 3.6, "entities": 0, "recency": 15.3, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "fa6ef06b1e1e", "is_lead": false, "lead": "dedd496491243ba2", "linked_by": [ { "rule": "shared-words", "with": "dedd496491243ba2", "words": [ "drug", "innovat", "pharma" ] } ], "members": [ "9592e1e6144d5340", "dedd496491243ba2" ], "severity": 0, "source_count": 3, "sources": [ "arxiv-newest", "bluesky-ai", "wires-ai" ] }, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-14T18:25:10.000Z", "score": 33.9, "source": "bluesky-ai", "summary": "Posted on Bluesky by @bloomberg.com.", "title": "Tech and pharma companies are collaborating to use AI for drug discovery and other medical innovations. How is that transforming the health sector? 🎧 Tune in on October 15 at 10:30am ET: bloom.bg/4s4", "url": "https://bsky.app/profile/bloomberg.com/post/3mxu6amqvi52e" }, { "entities": [ "Claude" ], "id": "5dc7e9d03eb17a01", "kind": "release", "meta": { "age_hours_at_score": 14, "basket_failed": [], "basket_live": 9, "basket_size": 9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T18:24:25.000Z", "first_source": "github-releases", "last_published_at": "2026-10-14T18:24:25.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T18:24:25.000Z", "sources": [ "github-releases" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Claude" ] }, "first_seen_at": "2026-10-14T18:45:13.575Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "release_tag": "v2.1.300", "repo": "anthropics/claude-code", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 15.3, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "capability", "published_at": "2026-10-14T18:24:25.000Z", "score": 37.1, "source": "github-releases", "summary": "What's changed Added virtualText to a mod's prompt decorations: text drawn in the prompt box that never enters the draft or the submitted prompt Added cache_creation_5m_tokens and cache_creation_1h_tokens to the OpenTelemetry api_request event, splitting cache writes by lifetime Added $.ui.view({ agentId }) for mods:…", "title": "anthropics/claude-code v2.1.300", "url": "https://github.com/anthropics/claude-code/releases/tag/v2.1.300" }, { "entities": [], "id": "71c698c9f186d9bd", "kind": "press", "meta": { "age_hours_at_score": 14, "categories": [ "AI", "Apps", "Media & Entertainment", "YouTube", "YouTube Music" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T18:23:46.000Z", "first_source": "techcrunch-ai", "last_published_at": "2026-10-14T18:23:46.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T18:23:46.000Z", "sources": [ "techcrunch-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://techcrunch.com/category/artificial-intelligence/feed/", "first_seen_at": "2026-10-14T18:45:13.575Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15.3, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "7dcf57ffb538", "is_lead": false, "lead": "48379a4329f1587c", "linked_by": [ { "rule": "shared-words", "with": "9fa15baa64eb216e", "words": [ "label", "music", "youtub" ] } ], "members": [ "48379a4329f1587c", "71c698c9f186d9bd", "9fa15baa64eb216e" ], "severity": 0, "source_count": 4, "sources": [ "bluesky-ai", "techcrunch-ai", "techmeme", "verge-ai" ] } }, "pillar": "attention", "published_at": "2026-10-14T18:23:46.000Z", "score": 30.3, "source": "techcrunch-ai", "summary": "The label will appear on the player page and include songs entirely generated by AI or partially, such as AI involved in composition and vocals.", "title": "YouTube Music launches new labels as AI-generated music floods streaming platforms", "url": "https://techcrunch.com/2026/10/14/youtube-music-launches-new-labels-as-ai-generated-music-floods-streaming-platforms" }, { "entities": [], "id": "e57935676aca6034", "kind": "forum", "meta": { "age_hours_at_score": 14, "categories": [ "MachineLearning" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T18:23:30.000Z", "first_source": "reddit-machinelearning", "last_published_at": "2026-10-14T18:23:30.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T18:23:30.000Z", "sources": [ "reddit-machinelearning" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/MachineLearning/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T03:13:58.488Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15.3, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null }, "pillar": "attention", "published_at": "2026-10-14T18:23:30.000Z", "score": 26.6, "source": "reddit-machinelearning", "summary": "I've been working on unsupervised tooling to discover what behaviors a model learns in post-training. The core idea is to use features from sparse autoencoders to identify differences in traces generated before and after training. I applied this to traces from the MiMo-v2.6 RL environments and it found significant…", "title": "What behaviors does a model actually learn in RL? [P]", "url": "https://reddit.com/r/MachineLearning/comments/1x5ywx3/what_behaviors_does_a_model_actually_learn_in_rl_p" }, { "entities": [], "id": "48379a4329f1587c", "kind": "forum", "meta": { "age_hours_at_score": 14, "bsky_handle": "theverge.com", "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T18:22:56.000Z", "source": "verge-ai", "title": "YouTube sticks new labels on AI-generated songs", "url": "https://theverge.com/ai-artificial-intelligence/1011369/youtube-music-ai-label", "weight": 0.55 } ], "count": 2, "first_seen_published_at": "2026-10-14T18:22:56.000Z", "first_source": "verge-ai", "last_published_at": "2026-10-14T18:33:41.714Z", "lead_minutes": 11, "primary_published_at": "2026-10-14T18:33:41.714Z", "sources": [ "bluesky-ai", "verge-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 16 }, "entity_kinds": { "labs": [], "models": [] }, "external_url": "https://www.theverge.com/ai-artificial-intelligence/1011369/youtube-music-ai-label", "first_seen_at": "2026-10-14T18:45:13.575Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 16, "mode": "author-feeds", "outlet_domain": "theverge.com", "pillar_reason": "source default", "reposts": 1, "retained": true, "score_components": { "corroboration": 12.5, "coverage": 10, "engagement": 6.4, "entities": 0, "recency": 15.3, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "7dcf57ffb538", "is_lead": true, "lead": "48379a4329f1587c", "linked_by": [ { "rule": "shared-words", "with": "9fa15baa64eb216e", "words": [ "label", "song", "youtub" ] } ], "members": [ "48379a4329f1587c", "71c698c9f186d9bd", "9fa15baa64eb216e" ], "severity": 0, "source_count": 4, "sources": [ "bluesky-ai", "techcrunch-ai", "techmeme", "verge-ai" ] }, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-14T18:22:56.000Z", "score": 59.2, "source": "bluesky-ai", "summary": "Posted on Bluesky by @theverge.com: New AI labels are marking tracks created ‘primarily’ with generative AI tools, starting first on YouTube Music. www.theverge.com/ai-artificia...", "title": "YouTube sticks new labels on AI-generated songs", "url": "https://bsky.app/profile/theverge.com/post/3mxu6pvrd4e2h" }, { "entities": [], "id": "7dee4d15ce3e06dd", "kind": "forum", "meta": { "age_hours_at_score": 14.1, "bsky_handle": "wired.com", "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T18:15:00.000Z", "source": "wired-ai", "title": "AI Came for Math. Now It’s Chasing the Rest of Science", "url": "https://wired.com/story/ai-is-coming-for-science-next-watch-out", "weight": 0.55 } ], "count": 2, "first_seen_published_at": "2026-10-14T18:15:00.000Z", "first_source": "wired-ai", "last_published_at": "2026-10-14T18:19:36.372Z", "lead_minutes": 5, "primary_published_at": "2026-10-14T18:19:36.372Z", "sources": [ "bluesky-ai", "wired-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 15 }, "entity_kinds": { "labs": [], "models": [] }, "external_url": "https://buff.ly/iRdeotV", "first_seen_at": "2026-10-14T18:45:13.575Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 15, "mode": "author-feeds", "outlet_domain": "wired.com", "pillar_reason": "source default", "reposts": 5, "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 6.2, "entities": 0, "recency": 15.2, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-14T18:15:00.000Z", "score": 48.9, "source": "bluesky-ai", "summary": "Posted on Bluesky by @wired.com: AI may soon be the default way to solve theoretical problems in economics, high-energy physics, and other fields, MIT researchers tell WIRED.", "title": "AI Came for Math. Now It’s Chasing the Rest of Science", "url": "https://bsky.app/profile/wired.com/post/3mxu5wowpkp26" }, { "entities": [], "id": "b4a5d303b7ae8673", "kind": "press", "meta": { "age_hours_at_score": 14.1, "corroboration": { "also": [ { "dedup_key": null, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 2 }, "kind": "forum", "outlet": "reuters.com", "published_at": "2026-10-14T18:35:16.000Z", "source": "bluesky-ai", "title": "AI-powered stock funds bet bots can pick stocks faster and better than humans", "url": "https://bsky.app/profile/reuters.com/post/3mxu6sorntl2a", "weight": 0.6 } ], "count": 2, "first_seen_published_at": "2026-10-14T18:14:50.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-14T18:35:16.000Z", "lead_minutes": 20, "primary_published_at": "2026-10-14T18:14:50.000Z", "sources": [ "bluesky-ai", "wires-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 2 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T18:45:13.575Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "reuters.com", "pillar_reason": "source default", "publisher": "Reuters", "publisher_url": "https://www.reuters.com", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 2.5, "entities": 0, "recency": 15.2, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-14T18:14:50.000Z", "score": 49, "source": "wires-ai", "summary": "Reuters, via Google News.", "title": "AI-powered stock funds bet bots can pick stocks faster and better than humans", "url": "https://news.google.com/rss/articles/CBMixAFBVV95cUxNOTdBaWF6NDB3OTRjdU92d3cycXFFWXBCWmFMMTNpX2Jfc0NEbHI2dFAyUXJTZG9RUUNOWklJYVlCTzR3S3lQbXRwSWdvVUVYMWVhdWVoUVMxZE41VGdaTmVNRGJrUWlMRENZY0tDTTB6M2NaZTZTMV92a1hxUGhHRDZfTmxlQ216WE5fcGJuWjhSNmdYTnJ0UmM2a0FqOTVwRi1FeUYyaENBNldkRi1TbkU4QVZ1U0NJV01vSEFSMTdMYmJa?oc=5" }, { "entities": [], "id": "f8278f45210420f3", "kind": "press", "meta": { "age_hours_at_score": 14.2, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T18:11:19.000Z", "source": "bloomberg-tech", "title": "Caterpillar Shares Eye 30% Slump as Oil Bites, AI Rally Stalls", "url": "https://bloomberg.com/news/articles/2026-10-14/caterpillar-shares-eye-30-slump-as-oil-bites-ai-rally-stalls", "weight": 0.6 } ], "count": 2, "first_seen_published_at": "2026-10-14T18:11:19.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T18:11:19.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T18:11:19.000Z", "sources": [ "bloomberg-tech", "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T18:45:13.575Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "bloomberg.com", "pillar_reason": "source default", "publisher": "Bloomberg.com", "publisher_url": "https://www.bloomberg.com", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15.2, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-14T18:11:19.000Z", "score": 46.5, "source": "wires-ai", "summary": "Bloomberg.com, via Google News.", "title": "Caterpillar Shares Eye 30% Slump as Oil Bites, AI Rally Stalls", "url": "https://news.google.com/rss/articles/CBMisAFBVV95cUxPVC1rbWVRM2tEQXpzdEhnNjZMRGJxbU1fYVFfckZjeldZTngyYUx4QnpuUkxFb2FiSGx4NXNsRXpoNTZmUS1PVF9teFdGc1RNNFFZTjluejQwSGMtaHFuWUJOeVJrVXhvRmk2WGEzdzE0QWtEeTRhX3NPZnJGaFhtTkptZGtPWHRUblRTckRwclFTTENrZG81R3hvRU1ORUFBdVFESGJObzJJd0Z5bDBQNQ?oc=5" }, { "entities": [ "Jev", "TypeSafe" ], "id": "833dff1ead8a2310", "kind": "forum", "meta": { "age_hours_at_score": 14.2, "categories": [ "MachineLearning" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T18:07:44.000Z", "first_source": "reddit-machinelearning", "last_published_at": "2026-10-14T18:07:44.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T18:07:44.000Z", "sources": [ "reddit-machinelearning" ] }, "engagement": null, "entity_kinds": { "labs": [ "TypeSafe" ], "models": [ "Jev" ] }, "feed": "https://www.reddit.com/r/MachineLearning/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T03:13:58.488Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 15.2, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null }, "pillar": "attention", "published_at": "2026-10-14T18:07:44.000Z", "score": 32.5, "source": "reddit-machinelearning", "summary": "Inspired by TypeSafe's Jev (a non-generative model that only outputs bounded decisions — choices, probabilities, calibrated confidence), I built two small decision models end-to-end on identical budgets and tasks to answer one question: which substrate makes the better Jev? Repo:…", "title": "I trained a 15M \"decision-only\" model two ways — linear recurrence vs Transformer attention. Accuracy was a coin flip; latency and training cost weren't.[R]", "url": "https://reddit.com/r/MachineLearning/comments/1x5yhr6/i_trained_a_15m_decisiononly_model_two_ways" }, { "entities": [ "Tencent" ], "id": "931e9694abb640b5", "kind": "press", "meta": { "age_hours_at_score": 14.2, "categories": [ "AI agents", "AI in practice", "Artificial Intelligence", "China" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T18:07:37.000Z", "first_source": "the-decoder", "last_published_at": "2026-10-14T18:07:37.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T18:07:37.000Z", "sources": [ "the-decoder" ] }, "engagement": null, "entity_kinds": { "labs": [ "Tencent" ], "models": [] }, "feed": "https://the-decoder.com/feed/", "first_seen_at": "2026-10-14T18:14:16.997Z", "incident": { "band": "A", "excluded": null, "severity": 1, "strong_signal": true, "terms": [ { "field": "title", "matched": "rogue", "term": "rogue", "tier": "a", "weight": 1 } ] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 5, "engagement": 0, "entities": 3, "recency": 15.2, "severity": 15, "source_weight": 13.8 }, "source_weight": 0.55, "story": { "id": "cc708e01934b", "is_lead": true, "lead": "931e9694abb640b5", "linked_by": [ { "rule": "incident-pair", "with": "1023696947258b2f", "words": [ "chin", "fleet" ] } ], "members": [ "1023696947258b2f", "931e9694abb640b5" ], "severity": 1, "source_count": 2, "sources": [ "bloomberg-tech", "the-decoder" ] } }, "pillar": "attention", "published_at": "2026-10-14T18:07:37.000Z", "score": 52, "source": "the-decoder", "summary": "For the first time, researchers have tracked down an uncontrolled fleet of Chinese AI agents in the wild and communicated with them via a web mailbox. The agents, running on Tencent Cloud, had launched more than 2,600 attempts to scrape data from Alibaba's mapping service Amap, finding their own workarounds when bot…", "title": "Researchers tracked down a rogue fleet of Chinese AI agents in the wild and actually talked to it", "url": "https://the-decoder.com/researchers-tracked-down-a-rogue-fleet-of-chinese-ai-agents-in-the-wild-and-actually-talked-to-it" }, { "entities": [], "id": "35c0d46d10a4e65b", "kind": "press", "meta": { "age_hours_at_score": 14.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T18:05:28.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T18:05:28.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T18:05:28.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-14T18:45:13.575Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "regulatory/legal keyword", "rejected_off_topic": 5, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15.2, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "12cb33300af9", "is_lead": false, "lead": "9a3f689a63892345", "linked_by": [ { "rule": "shared-words", "with": "9a3f689a63892345", "words": [ "center", "credit", "meta" ] } ], "members": [ "35c0d46d10a4e65b", "4e5f9fd0a35f62c8", "7dfb02a8d7db1976", "9a3f689a63892345" ], "severity": 0, "source_count": 4, "sources": [ "bloomberg-tech", "hn-ai", "nyt-tech", "techmeme" ] } }, "pillar": "governance", "published_at": "2026-10-14T18:05:28.000Z", "score": 30.2, "source": "bloomberg-tech", "summary": "Senate Democrats demanded answers from Meta Platforms, Inc. on whether it used a dubious tax strategy to reap billions in federal breaks to fund data center construction.", "title": "Meta’s Use of R&D Credits for Data Centers Probed by Democrats", "url": "https://bloomberg.com/news/articles/2026-10-14/meta-s-use-of-r-d-credits-for-data-centers-probed-by-democrats" }, { "entities": [ "Meta AI" ], "id": "b24c69b0926e39a2", "kind": "forum", "meta": { "age_hours_at_score": 14.4, "bsky_handle": "wired.com", "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T18:00:40.792Z", "first_source": "bluesky-ai", "last_published_at": "2026-10-14T18:00:40.792Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T18:00:40.792Z", "sources": [ "bluesky-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 7 }, "entity_kinds": { "labs": [ "Meta AI" ], "models": [] }, "external_url": null, "first_seen_at": "2026-10-14T18:14:16.997Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 7, "mode": "author-feeds", "outlet_domain": "wired.com", "pillar_reason": "source default", "reposts": 1, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 4.7, "entities": 3, "recency": 15.2, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-14T18:00:40.792Z", "score": 37.9, "source": "bluesky-ai", "summary": "Posted on Bluesky by @wired.com.", "title": "WIRED World Fair lands in Miami on Nov. 4. 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In this paper, we propose HiRe-MoMa (Hierarchical Residual Mobile Manipulation), a…", "title": "Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance", "url": "https://arxiv.org/abs/2610.20607" }, { "entities": [ "GPT" ], "id": "c1c54d952dc2d725", "kind": "forum", "meta": { "age_hours_at_score": 14.4, "categories": [ "singularity" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:59:49.000Z", "first_source": "reddit-singularity", "last_published_at": "2026-10-14T17:59:49.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:59:49.000Z", "sources": [ "reddit-singularity" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "GPT" ] }, "feed": "https://www.reddit.com/r/singularity/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T18:50:34.725Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 15.2, "severity": 0, "source_weight": 10 }, "source_weight": 0.4, "story": null }, "pillar": "attention", "published_at": "2026-10-14T17:59:49.000Z", "score": 28.2, "source": "reddit-singularity", "summary": "submitted by /u/141_1337 [link] [comments]", "title": "GPT-6 Astra autonomously solves 49 out of 50 unpublished theoretical physics research problems, scoring 98% at under $3 per problem. Researchers initially marked several answers wrong, only to…", "url": "https://reddit.com/r/singularity/comments/1x5y9ue/gpt6_astra_autonomously_solves_49_out_of_50" }, { "entities": [], "id": "127deb898376f43f", "kind": "paper", "meta": { "age_hours_at_score": 14.4, "arxiv_id": "2610.20597", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:59:28.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T17:59:28.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:59:28.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15.2, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T17:59:28.000Z", "score": 31.5, "source": "arxiv-newest", "summary": "Training language agents is bottlenecked by the high cost of long-horizon rollouts, yet standard agentic reinforcement learning (RL) learns only from a terminal scalar reward, leaving the supervision in intermediate steps largely unused. Prior work exploits this supervision through value targets that estimate per-step…", "title": "Co-Evolving Acting and Forecasting for Language Agents", "url": "https://arxiv.org/abs/2610.20597" }, { "entities": [], "id": "9d31c50de858e1f8", "kind": "paper", "meta": { "age_hours_at_score": 14.4, "arxiv_id": "2610.20588", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:58:08.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T17:58:08.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:58:08.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15.2, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T17:58:08.000Z", "score": 31.5, "source": "arxiv-newest", "summary": "Scaling up transformers has produced models with broad capabilities, deployed across heterogeneous hardware and fluctuating workloads. Yet, model families offer only a few fixed sizes (e.g., S, B, L), forcing users to commit to a model based on hardware and latency constraints. This rigidity is often suboptimal under…", "title": "Your Transformer Is Secretly Elastic", "url": "https://arxiv.org/abs/2610.20588" }, { "entities": [], "id": "0e42e6eb164b1289", "kind": "paper", "meta": { "age_hours_at_score": 14.4, "arxiv_id": "2610.20587", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:57:57.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T17:57:57.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:57:57.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15.2, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T17:57:57.000Z", "score": 31.5, "source": "arxiv-newest", "summary": "An agentic market is a decentralized serving-time market where AI agents bid to claim each user prompt. 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This paper proposes an architecture that partitions the solution among vanilla transformer experts, each focused on a different spectral scale. The partition is…", "title": "Learnable Hodge-Laplacian Driven Mixture of Experts for Steady-PDE Surrogates on Coarse Unstructured Meshes", "url": "https://arxiv.org/abs/2610.20585" }, { "entities": [], "id": "517f3f67952a2bbf", "kind": "paper", "meta": { "age_hours_at_score": 14.4, "arxiv_id": "2610.20584", "arxiv_published_at": "2026-10-14T00:00:00.000Z", "comments": 1, "corroboration": { "also": [ { "dedup_key": "arxiv:2610.20584", "engagement": null, "kind": "paper", "outlet": null, "published_at": "2026-10-14T17:57:12.000Z", "source": "arxiv-newest", "title": "Player-1: Learning to Play from Synthetic Experience", "url": "https://arxiv.org/abs/2610.20584", "weight": 0.65 } ], "count": 2, "first_seen_published_at": "2026-10-14T17:57:12.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 363, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "arxiv-newest", "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 118 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T03:45:14.282Z", "github_repo": "https://github.com/TencentARC/Player-1", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 17.1, "entities": 0, "recency": 15.2, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 118 }, "pillar": "capability", "published_at": "2026-10-14T17:57:12.000Z", "score": 64.8, "source": "hf-daily-papers", "summary": "Computer-use agents have demonstrated strong capabilities in interpreting screens and executing keyboard and mouse actions. However, games, with their visually rich worlds and diverse mechanics, remain challenging and offer a demanding next step toward general digital control. Current game-agent learning pipelines ofte", "title": "Player-1: Learning to Play from Synthetic Experience", "url": "https://huggingface.co/papers/2610.20584" }, { "entities": [], "id": "1bda21b7d27d5bf9", "kind": "paper", "meta": { "age_hours_at_score": 14.4, "arxiv_id": "2610.20582", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [ { "dedup_key": "arxiv:2610.20582", "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 1 }, "kind": "paper", "outlet": null, "published_at": "2026-10-15T00:00:00.000Z", "source": "hf-daily-papers", "title": "Latent Compaction with Test-Time Training", "url": "https://huggingface.co/papers/2610.20582", "weight": 0.8 } ], "count": 2, "first_seen_published_at": "2026-10-14T17:57:01.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 363, "primary_published_at": "2026-10-14T17:57:01.000Z", "sources": [ "arxiv-newest", "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 1 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 2.5, "entities": 0, "recency": 15.2, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T17:57:01.000Z", "score": 46.5, "source": "arxiv-newest", "summary": "Long contexts enlarge key--value (KV) caches, increasing memory use and attention costs during generation. Compressing these caches must preserve information for unseen questions. We introduce **Latent Compaction**, which learns a compact document-specific cache through test-time training. We initialize cache entries…", "title": "Latent Compaction with Test-Time Training", "url": "https://arxiv.org/abs/2610.20582" }, { "entities": [], "id": "1e90711720a2d1d4", "kind": "paper", "meta": { "age_hours_at_score": 14.4, "arxiv_id": "2610.20576", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:56:06.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T17:56:06.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:56:06.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15.1, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T17:56:06.000Z", "score": 31.4, "source": "arxiv-newest", "summary": "Post-training binds the competence of an LLM agent to the interface it was trained on. Semantics-preserving perturbations of that interface, such as renaming an action or changing the notation, leave the underlying task unchanged yet make the agent worse at it. Scoring each sample independently means interface…", "title": "One Task, Many Interfaces: Simple Cross-Interface Consistency Training Improves LLM Agent Robustness", "url": "https://arxiv.org/abs/2610.20576" }, { "entities": [], "id": "c129699a5395b192", "kind": "paper", "meta": { "age_hours_at_score": 14.5, "arxiv_id": "2610.20573", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:54:48.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T17:54:48.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:54:48.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15.1, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T17:54:48.000Z", "score": 31.4, "source": "arxiv-newest", "summary": "We introduce a novel method for verifying Lean formalizations that goes beyond LLM-judged faithfulness. Unlike most verification methods, which rely on LLMs or trained models, our method combines LLM-based semantic review with mechanical numerical and vacuity checks, providing checkable feedback and detecting errors…", "title": "Beyond LLM Judge: Mechanical Verification for Agentic Autoformalization", "url": "https://arxiv.org/abs/2610.20573" }, { "entities": [], "id": "066987406da6f54b", "kind": "paper", "meta": { "age_hours_at_score": 14.5, "arxiv_id": "2610.20572", "arxiv_published_at": "2026-10-14T00:00:00.000Z", "comments": 1, "corroboration": { "also": [ { "dedup_key": "arxiv:2610.20572", "engagement": null, "kind": "paper", "outlet": null, "published_at": "2026-10-14T17:54:34.000Z", "source": "arxiv-newest", "title": "Recovering Reliable Supervision via Critique-Guided On-Policy Distillation", "url": "https://arxiv.org/abs/2610.20572", "weight": 0.65 } ], "count": 2, "first_seen_published_at": "2026-10-14T17:54:34.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-15T00:00:00.000Z", "lead_minutes": 365, "primary_published_at": "2026-10-15T00:00:00.000Z", "sources": [ "arxiv-newest", "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 43 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": "https://github.com/kzhao5/CriticOPD", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 13.6, "entities": 0, "recency": 15.1, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 43 }, "pillar": "capability", "published_at": "2026-10-14T17:54:34.000Z", "score": 61.2, "source": "hf-daily-papers", "summary": "On-policy distillation (OPD) trains a student model on its own rollouts with token-level supervision from a teacher, assuming that the teacher can reach a correct answer from any prefix the student writes. We call this property the recoverability of the teacher and show it fails on incorrect rollouts: the teacher follo", "title": "Recovering Reliable Supervision via Critique-Guided On-Policy Distillation", "url": "https://huggingface.co/papers/2610.20572" }, { "entities": [ "CoreWeave" ], "id": "938f68167279c2c0", "kind": "press", "meta": { "age_hours_at_score": 14.5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:54:27.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T17:54:27.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:54:27.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "CoreWeave" ], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-14T18:14:16.997Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 5, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 15.1, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "markets", "published_at": "2026-10-14T17:54:27.000Z", "score": 33.1, "source": "bloomberg-tech", "summary": "CoreWeave Inc. Chief Executive Officer Mike Intrator, facing a plunge in the company’s stock and bond prices, said the market is acting “irrationally” and that he remains confident in the AI cloud company’s business model.", "title": "CoreWeave Says Market Acting ‘Irrationally,’ Stands by Model", "url": "https://bloomberg.com/news/articles/2026-10-14/coreweave-s-ceo-blames-rout-on-market-acting-irrationally" }, { "entities": [], "id": "23a361ef817bfd4d", "kind": "paper", "meta": { "age_hours_at_score": 14.5, "arxiv_id": "2610.20570", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:53:46.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T17:53:46.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:53:46.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15.1, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T17:53:46.000Z", "score": 31.4, "source": "arxiv-newest", "summary": "Brazilian Portuguese remains underserved by open-source language models, and the few that are safety-aligned rarely document what did not work or validate comparisons with statistical significance. We present Manacá-1B-Instruct-2, the second-generation post-training alignment of Manacá-1B (1.72B parameters, trained…", "title": "Manacá-1B-Instruct-2: Reproducible Safety Alignment for a Native Brazilian-Portuguese LLM. Negative DPO Results, a Controlled Composition Scan, and a Silent Failure Mode of the LLM Judge", "url": "https://arxiv.org/abs/2610.20570" }, { "entities": [ "Amazon" ], "id": "3f4d13eec64baaf9", "kind": "forum", "meta": { "age_hours_at_score": 14.5, "comments": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:52:44.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-14T17:52:44.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:52:44.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50091914", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 11 }, "entity_kinds": { "labs": [ "Amazon" ], "models": [] }, "first_seen_at": "2026-10-14T18:14:16.997Z", "hn_id": "50091914", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "substack.com", "pillar_reason": "source default", "points": 11, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 7.2, "entities": 3, "recency": 15.1, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-14T17:52:44.000Z", "score": 36.6, "source": "hn-fresh-ai", "summary": "New on Hacker News: 11 points, 0 comments so far.", "title": "The Death of Amazon's Writing Culture", "url": "https://alifeengineered.substack.com/p/on-the-death-of-amazons-writing-culture" }, { "entities": [ "Microsoft" ], "id": "646ba6c3ce92b136", "kind": "forum", "meta": { "age_hours_at_score": 14.5, "comments": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:52:35.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-14T17:52:35.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:52:35.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": 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"https://reuters.com/legal/litigation/norway-fines-microsofts-xandr-96-million-over-grindr-data-sharing-2026-10-12" }, { "entities": [], "id": "0a55943b50812b3d", "kind": "press", "meta": { "age_hours_at_score": 14.6, "arxiv_id": "2610.20563", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [ { "dedup_key": "arxiv:2610.20563", "engagement": null, "kind": "paper", "outlet": null, "published_at": "2026-10-14T17:49:37.000Z", "source": "arxiv-newest", "title": "Scaling Discovery Requires Forgetting", "url": "https://arxiv.org/abs/2610.20563", "weight": 0.65 } ], "count": 2, "first_seen_published_at": "2026-10-14T17:47:24.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-14T17:49:37.000Z", "lead_minutes": 2, "primary_published_at": "2026-10-14T17:47:24.000Z", "sources": [ "arxiv-newest", "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:22:35.661Z", "incident": { "band": null, "excluded": 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"sources": [ "arxiv-newest", "bloomberg-tech", "bluesky-ai", "gnews-ai", "techmeme", "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-14T17:47:24.000Z", "score": 43.9, "source": "wires-ai", "summary": "Reuters, via Google News.", "title": "As Wall Street banks finance and adopt AI, risks mount on both fronts", "url": "https://news.google.com/rss/articles/CBMitAFBVV95cUxQZDh1RnYtQmd3b0ZLYjh6dVd0cllieVhyUkpCbHB3SjdLMFc0N3FKOFVDbm1ISVhGNVVnY3ktVUVxcmszWjZacjJ5TkNLWVkyOHJpQTUzaEg5M0RsdXBvRXBqaWlWVFdHSU8wSGRWdi1kMkVmaGZjSVVEZWIzQnI3U21vTHBzS3VzRE5qckVzOVg4NXcwR1JpTDMyT083cTRYb1NWWWFIY1ZUd3h6MklfamVTNEw?oc=5" }, { "entities": [ "Physical Intelligence" ], "id": "6b1b54d1cd5918eb", "kind": "paper", "meta": { "age_hours_at_score": 14.6, "arxiv_id": "2610.20560", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:47:24.000Z", "first_source": "arxiv-newest", "last_published_at": 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robot simulation, while also serving as a critical testbed for physical intelligence in foundation models. Previous methods either leverage large language models to directly predict object coordinates, or utilize generative diffusion priors to synthesize…", "title": "PhySGA: A Physics-Space Grounded Agentic Framework for Generalizable and Interactive 3D Composition", "url": "https://arxiv.org/abs/2610.20560" }, { "entities": [ "Amazon" ], "id": "71df3c9b9956bec3", "kind": "forum", "meta": { "age_hours_at_score": 14.6, "bsky_handle": "theverge.com", "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:46:39.399Z", "first_source": "bluesky-ai", "last_published_at": "2026-10-14T17:46:39.399Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:46:39.399Z", "sources": [ "bluesky-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 6 }, "entity_kinds": { "labs": [ "Amazon" ], "models": [] }, "external_url": "https://www.theverge.com/gadgets/1011227/apple-homeview-smart-display-deal-sale", "first_seen_at": "2026-10-14T17:54:14.070Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 6, "mode": "author-feeds", "outlet_domain": "theverge.com", "pillar_reason": "source default", "reposts": 0, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 4.4, "entities": 3, "recency": 15.1, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-14T17:46:39.399Z", "score": 37.5, "source": "bluesky-ai", "summary": "Posted on Bluesky by @theverge.com: Apple’s first smart display doesn’t arrive until November 13th, but you can save on a couple of colorways. www.theverge.com/gadgets/1011...", "title": "Apple’s new HomeView smart display is $10 cheaper at Amazon", "url": "https://bsky.app/profile/theverge.com/post/3mxu43s2n2w2u" }, { "entities": [], "id": "10b44500592929e1", "kind": "forum", "meta": { 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"guardian-ai", "hn-fresh-ai", "nyt-tech", "reddit-chatgpt", "techcrunch-ai", "techmeme", "the-decoder", "verge-ai", "wired-ai", "wires-ai" ] }, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-14T17:39:37.000Z", "score": 50.9, "source": "hn-fresh-ai", "summary": "New on Hacker News: 21 points, 5 comments so far.", "title": "OpenAI claims employee sent secret device information to Apple", "url": "https://ft.com/content/18b65495-9e2a-45d6-be85-7641442ab61a" }, { "entities": [], "id": "dedd496491243ba2", "kind": "press", "meta": { "age_hours_at_score": 14.8, "arxiv_id": "2610.20548", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [ { "dedup_key": "arxiv:2610.20548", "engagement": null, "kind": "paper", "outlet": null, "published_at": "2026-10-14T17:39:35.000Z", "source": "arxiv-newest", "title": "PerSo: Investigating Facets of LLMs' Social Intelligence in Real-World Persuasion Scenarios", "url": "https://arxiv.org/abs/2610.20548", "weight": 0.65 } ], "count": 2, "first_seen_published_at": "2026-10-14T17:36:33.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-14T17:39:35.000Z", "lead_minutes": 3, "primary_published_at": "2026-10-14T17:36:33.000Z", "sources": [ "arxiv-newest", "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:22:35.661Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 5, "engagement": 0, "entities": 0, "recency": 15.1, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": { "id": "fa6ef06b1e1e", "is_lead": true, "lead": "dedd496491243ba2", "linked_by": [ { "rule": "shared-words", "with": 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crop in Ethiopia, but its production is affected by foliar diseases, particularly early blight and late blight. This study develops a lightweight convolutional neural network (CNN) to classify potato leaves into three categories: healthy, early blight, and late blight. The proposed…", "title": "Detection of Potato Plant Leaf Diseases using Convolutional Neural Networks", "url": "https://arxiv.org/abs/2610.20544" }, { "entities": [ "NVIDIA" ], "id": "05d76df97f1c99b4", "kind": "press", "meta": { "age_hours_at_score": 14.8, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T17:34:47.000Z", "source": "bloomberg-tech", "title": "Nvidia CEO Says He Would Cut Off Buyers Who Build Unsafe AI", "url": "https://bloomberg.com/news/articles/2026-10-14/nvidia-ceo-says-he-would-cut-off-customers-who-build-unsafe-ai", "weight": 0.6 }, { "dedup_key": null, "engagement": null, "kind": "press", "outlet": "indiatimes.com", "published_at": "2026-10-15T02:44:41.000Z", "source": "gnews-ai", "title": "Nvidia CEO Jensen Huang says he would cut off customers who build unsafe AI", "url": 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"https://bloomberg.com/news/articles/2026-10-14/nvidia-ceo-says-he-would-cut-off-customers-who-build-unsafe-ai", "weight": 0.45 } ], "count": 4, "first_seen_published_at": "2026-10-14T17:34:47.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-15T02:44:41.000Z", "lead_minutes": 550, "primary_published_at": "2026-10-14T17:34:47.000Z", "sources": [ "bloomberg-tech", "gnews-ai", "hn-fresh-ai", "wires-ai" ] }, "engagement": { "full_scale": 500, "metric": "hn_points", "value": 5 }, "entity_kinds": { "labs": [ "NVIDIA" ], "models": [] }, "first_seen_at": "2026-10-14T17:42:32.742Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "bloomberg.com", "pillar_reason": "source default", "publisher": "Bloomberg.com", "publisher_url": "https://www.bloomberg.com", "retained": true, "score_components": { "corroboration": 25, "coverage": 0, "engagement": 5.2, "entities": 3, "recency": 15, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-14T17:34:47.000Z", "score": 67, "source": "wires-ai", "summary": "Bloomberg.com, via Google News.", "title": "Nvidia CEO Says He Would Cut Off Customers Who Build Unsafe AI", "url": "https://news.google.com/rss/articles/CBMiswFBVV95cUxOSnhxakNaMklvT25NQjFreTNHYVhMME45SHh4QkhBSGhVNFlPUFFCTmNyaGhYWFJobXZSMFZSUGtlUE5JNkxicGNhOVIzUkowVFo1UjlfZmliN2lFVmlJVDN6UGNUMFhDN0twbnJ6ejdpU19xQ01jSUR5Y3lnWnkxM0pUdGVDQnNiNFdkenE2dG9Zcm1DakdFUUdnbFZqTXc4TVR1c2hHX0hWV2I3aGFFcmhTcw?oc=5" }, { "entities": [ "ASML", "NVIDIA" ], "id": "de961cda9f34199a", "kind": "press", "meta": { "age_hours_at_score": 14.8, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T17:34:15.000Z", "source": "bloomberg-tech", "title": "AI Boom Drives ASML Higher as Nvidia Bets on Europe", "url": 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"2026-10-14T17:34:15.000Z", "score": 52.3, "source": "wires-ai", "summary": "Bloomberg.com, via Google News.", "title": "Watch AI Boom Drives ASML Higher as Nvidia Bets on Europe", "url": "https://news.google.com/rss/articles/CBMihwFBVV95cUxPX0prSF9vU0dIOHdkRkdiVWYwOHpvRzhob2N5MmNIODNQaXl5cWVGU1pVZEpsQ19uclFQdkh0a2VFOWZuU0g3MDdaR1VMTXl2c0wxTVJRbjNrd3FXZldhUEdjbGpCWmFYUW5tSFNYZTZyOHpMUHdVcGI3anhtUmNkdEhaRU5Td1E?oc=5" }, { "entities": [], "id": "0aad741d4e94eab7", "kind": "press", "meta": { "age_hours_at_score": 14.8, "categories": [ "AI (artificial intelligence)", "Anthropic", "Biology", "Chatbots", "Infectious diseases", "MI5", "Microbiology", "Science", "Synthetic biology", "Technology", "UK news", "UK security and counter-terrorism", "Weapons technology" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:34:14.000Z", "first_source": "guardian-ai", "last_published_at": "2026-10-14T17:34:14.000Z", "lead_minutes": 0, "primary_published_at": 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"title": "Could AI ‘change the stakes’ in the development of bioweapons?", "url": "https://theguardian.com/technology/2026/oct/14/could-ai-change-the-stakes-in-the-development-of-bioweapons" }, { "entities": [], "id": "892916d56d0d8a77", "kind": "press", "meta": { "age_hours_at_score": 14.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:33:20.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T17:33:20.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:33:20.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-14T17:42:32.742Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "regulatory/legal keyword", "rejected_off_topic": 5, "retained": true, "score_components": { 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Frank McCleary, a partner at Arthur D. Little, discusses Europe’s fragmented but evolving regulatory landscape, why Chinese operators see an opportunity…", "title": "US, Chinese Robotaxi Rivals Race for Europe", "url": "https://bloomberg.com/news/videos/2026-10-14/us-chinese-robotaxi-rivals-race-for-europe-video" }, { "entities": [], "id": "576d5164667c7c41", "kind": "paper", "meta": { "age_hours_at_score": 14.8, "arxiv_id": "2610.20538", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:32:54.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T17:32:54.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:32:54.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T17:32:54.000Z", "score": 31.3, "source": "arxiv-newest", "summary": "Multimodal code intelligence connects the symbolic world of programs with the visual world their execution creates. Interest in this capability is burgeoning, as models are increasingly asked to produce charts, webpages, animations, and semi-structured documents. Recently, proprietary models have pushed far ahead,…", "title": "PrismaCoder: Pushing the Boundaries of Open Multimodal Code Intelligence through Decomposed Rubric Synthesis", "url": "https://arxiv.org/abs/2610.20538" }, { "entities": [], "id": "4722735b0fc71ae1", "kind": "press", "meta": { "age_hours_at_score": 14.9, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T17:31:05.000Z", "source": "bloomberg-tech", "title": "Modal CEO: AI Has Entered a ‘New Era of Software’", "url": "https://bloomberg.com/news/videos/2026-10-14/modal-ceo-ai-has-entered-a-new-era-of-software-video", "weight": 0.6 } ], "count": 2, "first_seen_published_at": "2026-10-14T17:31:05.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T17:32:33.000Z", "lead_minutes": 1, "primary_published_at": "2026-10-14T17:32:33.000Z", "sources": [ "bloomberg-tech", "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T17:42:32.742Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "bloomberg.com", "pillar_reason": "source default", "publisher": "Bloomberg.com", "publisher_url": "https://www.bloomberg.com", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-14T17:31:05.000Z", "score": 46.3, "source": "wires-ai", "summary": "Bloomberg.com, via Google News.", "title": "Watch Modal CEO: AI Has Entered a ‘New Era of Software’", "url": "https://news.google.com/rss/articles/CBMiowFBVV95cUxNaXdhbDhlMGxFSmFkdDFReFRQY0Z4aTVwQ1FRWDlwOUFmeUpKaklQQlNPOV9BeS1sYlltbUdVQzl2dnhTejJodjlqeXhkT1ZEbWNLQXJqVGxaa1BoVi1vSjZ6QVEyTmRQNEtLQkVpNDV6THVacFJDaElHUUNhS1d5NkhfZnFNZTdUaXR1MDRMUHNFSUpJUExWOEg4b1J6T0hyZ1hr?oc=5" }, { "entities": [], "id": "85018c48ae50a112", "kind": "paper", "meta": { "age_hours_at_score": 14.9, "arxiv_id": "2610.20534", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:30:17.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T17:30:17.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:30:17.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T17:30:17.000Z", "score": 31.3, "source": "arxiv-newest", "summary": "Relational deep learning has emerged as a powerful paradigm for learning end-to-end predictive models over relational databases by representing them as temporal heterogeneous graphs. Recent advances based on graph neural networks and Transformers have achieved strong performance across diverse domains. However, the…", "title": "Revisiting Simple Baselines for Relational Deep Learning", "url": "https://arxiv.org/abs/2610.20534" }, { "entities": [], "id": "82ba0eddc5edde98", "kind": "paper", "meta": { "age_hours_at_score": 14.9, "arxiv_id": "2610.20533", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:29:09.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T17:29:09.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:29:09.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T17:29:09.000Z", "score": 31.3, "source": "arxiv-newest", "summary": "We introduce an approach for self-supervised representation learning designed to be used as a tokenizer for generative modeling. Our objective supervises a representation loss by factorizing the pointwise mutual information of the data, which we estimate by taking information differences between conditional…", "title": "Generative Representation Learning for Scalable Latent Tokenization", "url": "https://arxiv.org/abs/2610.20533" }, { "entities": [], "id": "a99474364bf4f0f7", "kind": "paper", "meta": { "age_hours_at_score": 14.9, "arxiv_id": "2610.20532", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:29:06.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T17:29:06.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:29:06.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T17:29:06.000Z", "score": 31.3, "source": "arxiv-newest", "summary": "A multimodal agent must decide whether visual evidence is worth acquiring, which candidate or resolution to query, and when further interaction can no longer change its decision. We study this problem under a strict free-metadata/paid-observation boundary, stochastic vector costs, and a frozen execution trace, and…", "title": "ActiveEvidence: Decision-Focused Evidence Graphs with Verifiable Acquisition Gaps", "url": "https://arxiv.org/abs/2610.20532" }, { "entities": [], "id": "5d127f5727e31571", "kind": "forum", "meta": { "age_hours_at_score": 15, "categories": [ "singularity" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:25:08.000Z", "first_source": "reddit-singularity", "last_published_at": "2026-10-14T17:25:08.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:25:08.000Z", "sources": [ "reddit-singularity" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/singularity/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T03:45:39.508Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15, "severity": 0, "source_weight": 10 }, "source_weight": 0.4, "story": null }, "pillar": "attention", "published_at": "2026-10-14T17:25:08.000Z", "score": 25, "source": "reddit-singularity", "summary": "submitted by /u/yogthos [link] [comments]", "title": "China Starts Clinical Trial of 128-Channel Brain-Computer Interface", "url": "https://reddit.com/r/singularity/comments/1x5xcso/china_starts_clinical_trial_of_128channel" }, { "entities": [], "id": "d941e313693e2a2a", "kind": "paper", "meta": { "age_hours_at_score": 15, "arxiv_id": "2610.20525", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:24:48.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T17:24:48.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:24:48.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T17:24:48.000Z", "score": 31.3, "source": "arxiv-newest", "summary": "Neural emulation need not require learning a substitute for known physics. We investigate PolyNets that couple a continuous-time neural representation of polynomial dynamics with a simple recurrent network for discrete-time integration. When the governing coefficients are known, the resulting tensor graph requires no…", "title": "The Physics Is the Network: Fast, Training-Free Neural Emulation", "url": "https://arxiv.org/abs/2610.20525" }, { "entities": [ "Netflix" ], "id": "a2228adc819ba72c", "kind": "press", "meta": { "age_hours_at_score": 15, "categories": [ "Layoffs and Job Reductions", "Media", "Movies", "Netflix Inc", "Peters, Greg (Media Executive)", "Sarandos, Ted", "Video Recordings, Downloads and Streaming" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:21:26.000Z", "first_source": "nyt-tech", "last_published_at": "2026-10-14T17:21:26.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:21:26.000Z", "sources": [ "nyt-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "Netflix" ], "models": [] }, "feed": "https://rss.nytimes.com/services/xml/rss/nyt/Technology.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 8, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 15, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": null }, "pillar": "attention", "published_at": "2026-10-14T17:21:26.000Z", "score": 35.5, "source": "nyt-tech", "summary": "The streamer’s share price is down more than 40 percent over the last year as the company has struggled with stalling engagement.", "title": "Netflix, Facing Slowing Growth, Cuts 6% of Its Work Force", "url": "https://nytimes.com/2026/10/14/business/media/netflix-layoffs-ted-sarandos.html" }, { "entities": [ "Alibaba Qwen", "Qwen" ], "id": "e972dcb688267290", "kind": "forum", "meta": { "age_hours_at_score": 15, "comments": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:20:09.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-14T17:20:09.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:20:09.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50091275", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 9 }, "entity_kinds": { "labs": [ "Alibaba Qwen" ], "models": [ "Qwen" ] }, "first_seen_at": "2026-10-14T17:42:32.742Z", "hn_id": "50091275", "incident": { "band": "B", "excluded": null, "severity": 0.6, "strong_signal": false, "terms": [ { "field": "title", "matched": "jailbroken", "term": "jailbroken", "tier": "b", "weight": 0.6 } ] }, "outlet_domain": "reddit.com", "pillar_reason": "hardware/capital keyword", "points": 9, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 6.7, "entities": 6, "recency": 15, "severity": 9, "source_weight": 11.3 }, "source_weight": 0.45, "story": null, "window_hours": 2 }, "pillar": "compute", "published_at": "2026-10-14T17:20:09.000Z", "score": 48, "source": "hn-fresh-ai", "summary": "New on Hacker News: 9 points, 0 comments so far.", "title": "Someone on Reddit is running Qwen 27B at ~20 tok/s on a jailbroken PS5's GPU", "url": "https://reddit.com/r/LocalLLM/comments/1x4y61f/qwen_27b_at_20_toks_on_a_jailbroken_ps5s_gpu_i" }, { "entities": [], "id": "da0257c716dd388c", "kind": "paper", "meta": { "age_hours_at_score": 15.1, "arxiv_id": "2610.20518", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:19:11.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T17:19:11.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:19:11.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T17:19:11.000Z", "score": 31.3, "source": "arxiv-newest", "summary": "Today's agentic systems are typically designed, built, and deployed by single developers with full control over all participating agents. While this simplifies development, it limits the potential scale of agentic systems. We study multi-developer agentic systems (MDAs) which enable developers to collaboratively build…", "title": "The Wealth of Agents: Scaling Agentic AI Across Developers", "url": "https://arxiv.org/abs/2610.20518" }, { "entities": [], "id": "89478491dd8962a3", "kind": "paper", "meta": { "age_hours_at_score": 15.1, "arxiv_id": "2610.20516", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:17:18.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T17:17:18.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:17:18.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 15, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T17:17:18.000Z", "score": 31.3, "source": "arxiv-newest", "summary": "On-policy self-distillation provides dense supervision for reasoning models via a privileged self-teacher. However, this supervision reflects local token preferences rather than the expected return of an unguided student continuation. Recent methods combine this signal with GRPO. 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This motivates online policies that adaptively select agents to query as examples arrive and update policies as…", "title": "Bayesian Learning to Select Fair Classifiers", "url": "https://arxiv.org/abs/2610.20514" }, { "entities": [], "id": "4922278de1f5df86", "kind": "forum", "meta": { "age_hours_at_score": 15.1, "bsky_handle": "theverge.com", "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:15:38.296Z", "first_source": "bluesky-ai", "last_published_at": "2026-10-14T17:15:38.296Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:15:38.296Z", "sources": [ "bluesky-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 9 }, "entity_kinds": { "labs": [], "models": [] }, "external_url": "https://www.theverge.com/column/1011264/digital-twin-metabolism-ai-twin-health", "first_seen_at": "2026-10-14T17:42:32.742Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 9, "mode": "author-feeds", "outlet_domain": "theverge.com", "pillar_reason": "source default", "reposts": 1, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 5.2, "entities": 0, "recency": 15, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-14T17:15:38.296Z", "score": 35.2, "source": "bluesky-ai", "summary": "Posted on Bluesky by @theverge.com: In return for feeding a ‘digital twin’ my metabolic data, it predicts how I'll react to certain foods — and one day, maybe medicines. www.theverge.com/column/10112...", "title": "My AI doppelganger tells me what to eat", "url": "https://bsky.app/profile/theverge.com/post/3mxu2ecwoqc2n" }, { "entities": [], "id": "ff845b27d19d3b80", "kind": "paper", "meta": { "age_hours_at_score": 15.1, "arxiv_id": "2610.20512", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:14:29.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T17:14:29.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:14:29.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.9, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T17:14:29.000Z", "score": 31.2, "source": "arxiv-newest", "summary": "Graph-based analysis of electronic health records (EHRs) yields disease-trajectory catalogs, but structural support does not establish semantic novelty. 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generation by drafting multiple tokens per step, yet feature-based drafters rely on target hidden features whose contextual emphasis may not align with the needs of subsequent draft positions. Hybrid linear attention (HLA) models maintain compact recurrent states that…", "title": "ReaDy: Recurrent States as Dynamic Context for Speculative Drafting in Hybrid Linear Attention Large Language Models", "url": "https://arxiv.org/abs/2610.20503" }, { "entities": [], "id": "13062dbe0efa88e1", "kind": "paper", "meta": { "age_hours_at_score": 15.2, "arxiv_id": "2610.20502", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:08:50.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T17:08:50.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:08:50.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.9, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T17:08:50.000Z", "score": 31.2, "source": "arxiv-newest", "summary": "Longitudinal electronic health records (EHRs) can reveal disease-progression structure, while useful forecasting requires leakage-controlled discovery, censor-aware outcomes, and inspectable trajectory evidence. We developed a graph-enhanced framework in MIMIC-IV v3.1 that splits patients before graph construction,…", "title": "Graph-Enhanced Disease Trajectory Discovery and Temporal Endpoint Forecasting from Longitudinal Electronic Health Records", "url": "https://arxiv.org/abs/2610.20502" }, { "entities": [], "id": "230ab1dde5aae77b", "kind": "paper", "meta": { "age_hours_at_score": 15.3, "arxiv_id": "2610.20497", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:06:59.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T17:06:59.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:06:59.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.9, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T17:06:59.000Z", "score": 31.2, "source": "arxiv-newest", "summary": "Reinforcement learning with verifiable rewards (RLVR) trains language models from checked task outcomes, but assigning a terminal reward to individual reasoning tokens or actions remains difficult. Group-relative methods such as Group Relative Policy Optimization (GRPO) sample several attempts per prompt, but lose…", "title": "Hindsight-Privileged RLVR: Single-Rollout Token Credit Without Group Sampling", "url": "https://arxiv.org/abs/2610.20497" }, { "entities": [], "id": "c33b71543c7179e4", "kind": "paper", "meta": { "age_hours_at_score": 15.3, "arxiv_id": "2610.20494", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:03:42.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T17:03:42.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:03:42.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.9, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T17:03:42.000Z", "score": 31.2, "source": "arxiv-newest", "summary": "Pretrained representations are increasingly used to share and reuse information from medical images without exposing the original scans, but it is unclear how much patient-specific information these representations retain. We investigate this question for brain magnetic resonance imaging (MRI) by evaluating five…", "title": "Quantifying privacy risks in foundation model representations of brain MRI", "url": "https://arxiv.org/abs/2610.20494" }, { "entities": [], "id": "b5314d23f92b5259", "kind": "forum", "meta": { "age_hours_at_score": 15.4, "bsky_handle": "techcrunch.com", "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:00:58.000Z", "first_source": "bluesky-ai", "last_published_at": "2026-10-14T17:00:58.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:00:58.000Z", "sources": [ "bluesky-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 9 }, "entity_kinds": { "labs": [], "models": [] }, "external_url": "https://techcrunch.com/2026/10/14/mosqitter-is-turning-its-mosquito-traps-into-an-ai-powered-surveillance-network/?utm_source=dlvr.it&utm_medium=bluesky", "first_seen_at": "2026-10-14T17:06:07.633Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 9, "mode": "author-feeds", "outlet_domain": "techcrunch.com", "pillar_reason": "source default", "reposts": 5, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 5.2, "entities": 0, "recency": 14.9, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-14T17:00:58.000Z", "score": 35.1, "source": "bluesky-ai", "summary": "Posted on Bluesky by @techcrunch.com.", "title": "Mosqitter is turning its mosquito traps into an AI-powered surveillance network", "url": "https://bsky.app/profile/techcrunch.com/post/3mxtzk25hnl2t" }, { "entities": [], "id": "67ba423fa8ba67ad", "kind": "press", "meta": { "age_hours_at_score": 15.4, "categories": [ "AI", "Column", "Health", "Optimizer", "Science" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T17:00:00.000Z", "first_source": "verge-ai", "last_published_at": "2026-10-14T17:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T17:00:00.000Z", "sources": [ "verge-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.theverge.com/rss/ai-artificial-intelligence/index.xml", "first_seen_at": "2026-10-14T17:06:07.633Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.9, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-14T17:00:00.000Z", "score": 28.7, "source": "verge-ai", "summary": "This is Optimizer, a weekly newsletter sent from Verge senior reviewer Victoria Song that dissects and discusses the latest gizmos and potions that swear they're going to change your life. Opt in for Optimizer here. For the past 100 days or so, my mornings have had a new rhythm. When I wake up, the first […]", "title": "My AI doppelganger tells me what to eat", "url": "https://theverge.com/column/1011264/digital-twin-metabolism-ai-twin-health" }, { "entities": [], "id": "fc28f8df7f3e28b7", "kind": "forum", "meta": { "age_hours_at_score": 15.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:58:40.000Z", "first_source": "lesswrong", "last_published_at": "2026-10-14T16:58:40.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:58:40.000Z", "sources": [ "lesswrong" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.lesswrong.com/feed.xml", "first_seen_at": "2026-10-14T17:42:32.742Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "hardware/capital keyword", "rejected_off_topic": 2, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.9, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "compute", "published_at": "2026-10-14T16:58:40.000Z", "score": 27.4, "source": "lesswrong", "summary": "I made a betting game called Hindsight (AI slop rulebook at https://hindsight.markets/rules ). At last year’s Metagame conference I playtested a face-to-face version but ultimately we gave up trying to come up with methods to trade the chips around conveniently. The online version is a “TV party game” like JackBox…", "title": "Hindsight: a trivia + calibration game", "url": "https://lesswrong.com/posts/nnqmnKdjbtigGRHEe/hindsight-a-trivia-calibration-game" }, { "entities": [], "id": "d05bc4e324363d00", "kind": "paper", "meta": { "age_hours_at_score": 15.4, "arxiv_id": "2610.20486", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:57:47.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T16:57:47.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:57:47.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:22:35.661Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.9, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T16:57:47.000Z", "score": 31.2, "source": "arxiv-newest", "summary": "In reinforcement learning, an agent must explore an unknown environment to learn how to make good decisions. Unlike methods that can freely sample any state--action pair, an agent learning from a single trajectory must spend additional time navigating between states. It is therefore natural to expect that learning…", "title": "Average-Reward Policy Identification from a Single Continuing Trajectory", "url": "https://arxiv.org/abs/2610.20486" }, { "entities": [], "id": "f7d6b624ff6f88b5", "kind": "paper", "meta": { "age_hours_at_score": 15.4, "arxiv_id": "2610.20483", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:55:22.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T16:55:22.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:55:22.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:22:35.661Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.9, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T16:55:22.000Z", "score": 31.2, "source": "arxiv-newest", "summary": "We study how to improve LLM reasoning at inference time without updating model weights or using external reward models. Recent reward-free power-sampling methods define the sequence-level power target $π_α(y\\mid x)\\propto p_0(y\\mid x)^α$, where $α>1$, which increases the relative probability of completions that are…", "title": "Decoupled Marginal Sharpening for Training-Free Inference-Time Scaling", "url": "https://arxiv.org/abs/2610.20483" }, { "entities": [], "id": "aa20a97d703482a1", "kind": "forum", "meta": { "age_hours_at_score": 15.5, "comments": 102, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:52:42.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-14T16:52:42.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:52:42.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50090799", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 119 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T17:42:32.742Z", "hn_id": "50090799", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "min_points": 8, "pillar_reason": "source default", "points": 119, "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 13.9, "entities": 0, "recency": 14.8, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "window_hours": 72 }, "pillar": "attention", "published_at": "2026-10-14T16:52:42.000Z", "score": 43.7, "source": "hn-ai", "summary": "119 points, 102 comments on Hacker News.", "title": "Has AI tells", "url": "https://theconsensus.dev/blog/2026/10/14/has-ai-tells.html?focus=1" }, { "entities": [], "id": "2dd4f1c20f7f78fd", "kind": "paper", "meta": { "age_hours_at_score": 15.5, "arxiv_id": "2610.20479", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:52:00.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T16:52:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:52:00.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.8, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T16:52:00.000Z", "score": 31.1, "source": "arxiv-newest", "summary": "Bias audits of multilingual language models usually compare a model's average stereotype score in English with its average in another language and read the difference as a difference in fairness. We ask how much of that difference is noise. Auditing ten open models in eight languages, we measure how far scores move…", "title": "Lost in Translation: Why Lower Bias Scores Don't Mean Fairer Models", "url": "https://arxiv.org/abs/2610.20479" }, { "entities": [], "id": "f351b48e981eb372", "kind": "paper", "meta": { "age_hours_at_score": 15.5, "arxiv_id": "2610.20474", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:51:09.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T16:51:09.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:51:09.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.8, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T16:51:09.000Z", "score": 31.1, "source": "arxiv-newest", "summary": "Pretrained vision-language-action models (VLAs) often fail midway through unseen long-horizon tasks. Recursive self-improvement (RSI) offers a remedy, but embodied RSI methods either refine the harness around a frozen executor and so cannot add a missing motion, or train the executor on persistent failures without…", "title": "AdaRSI: On-Demand Recursive Self-Improvement for Embodied Agents", "url": "https://arxiv.org/abs/2610.20474" }, { "entities": [], "id": "3fb433e2af89dbd9", "kind": "paper", "meta": { "age_hours_at_score": 15.5, "arxiv_id": "2610.20473", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:50:42.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T16:50:42.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:50:42.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.8, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T16:50:42.000Z", "score": 31.1, "source": "arxiv-newest", "summary": "Opponent histories can mislead after behavioral shifts. SafeAdapt-Poker formulates budgeted adaptation under opponent shift as a dual-counterfactual sequential-control problem using only information available before each decision. At each decision the controller asks two distinct questions: what local downside a…", "title": "SafeAdapt-Poker: Counterfactual Risk Calibration for Budgeted Adaptation under Opponent Shift", "url": "https://arxiv.org/abs/2610.20473" }, { "entities": [], "id": "edbe5b0f76244ed7", "kind": "paper", "meta": { "age_hours_at_score": 15.5, "arxiv_id": "2610.20472", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:50:35.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T16:50:35.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:50:35.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.8, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T16:50:35.000Z", "score": 31.1, "source": "arxiv-newest", "summary": "Cultural attitudes and social norms evolve over time, yet most cultural benchmarks for large language models (LLMs) treat culture as a static attribute of a country or region. We introduce ChronoCulture, a benchmark of 10,360 temporal pairs built from longitudinal survey data across 72 countries. 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Big bank earnings expose a divide on Wall Street. RBC’s Gerard Cassidy breaks down rates, margins and credit risk. 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Growing Q-Networks (GQN) grow the resolution uniformly from coarse to fine. 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We investigate whether a pretrained tabular foundation model (TFM), TabPFN, can serve as a general-purpose neural solver for power grid steady-state analysis. We evaluate the TFM…", "title": "Learning by Examples: Power Grid Steady State Analysis with a Tabular Foundation Model", "url": "https://arxiv.org/abs/2610.20462" }, { "entities": [], "id": "8065a859baebc3b0", "kind": "paper", "meta": { "age_hours_at_score": 15.6, "arxiv_id": "2610.20461", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:44:22.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T16:44:22.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:44:22.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.8, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T16:44:22.000Z", "score": 31.1, "source": "arxiv-newest", "summary": "Character Error Rate (CER), the standard metric for OCR and vision-language models (VLMs), charges a typo as much as a wrong name. We introduce semCER, which keeps CER's alignment and denominator and charges each grapheme edit a severity from an LLM judge, with no word segmentation. Normalized CER keeps the score of a…", "title": "Equal CER, Unequal Damage: Severity-Weighted Character Error Rate for OCR", "url": "https://arxiv.org/abs/2610.20461" }, { "entities": [ "Microsoft" ], "id": "5293bf3fccb71d6e", "kind": "forum", "meta": { "age_hours_at_score": 15.6, "bsky_handle": "theverge.com", "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:43:37.240Z", "first_source": "bluesky-ai", "last_published_at": "2026-10-14T16:43:37.240Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:43:37.240Z", "sources": [ "bluesky-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 22 }, "entity_kinds": { "labs": [ "Microsoft" ], "models": [] }, "external_url": "https://www.theverge.com/news/1011216/xbox-nano-controller-release-date-pricing", "first_seen_at": "2026-10-14T17:06:07.633Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 22, "mode": "author-feeds", "outlet_domain": "theverge.com", "pillar_reason": "source default", "reposts": 2, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 7, "entities": 3, "recency": 14.8, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-14T16:43:37.240Z", "score": 39.8, "source": "bluesky-ai", "summary": "Posted on Bluesky by @theverge.com: Microsoft's Xbox Nano controller is $49.99 and designed for gaming on the go. www.theverge.com/news/1011216...", "title": "Honey, Xbox shrunk its controller", "url": "https://bsky.app/profile/theverge.com/post/3mxtyl2wjhg2f" }, { "entities": [ "OpenAI" ], "id": "0343a01e6ce9dfac", "kind": "forum", "meta": { "age_hours_at_score": 15.6, "bsky_handle": "arstechnica.com", "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:43:33.354Z", "first_source": 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"author-feed" }, "pillar": "attention", "published_at": "2026-10-14T16:43:33.354Z", "score": 40.4, "source": "bluesky-ai", "summary": "Posted on Bluesky by @arstechnica.com.", "title": "FBI arrests OpenAI engineer suspected of planting bug in conference room", "url": "https://bsky.app/profile/arstechnica.com/post/3mxtykwdddx2a" }, { "entities": [], "id": "5d854940a52a9d69", "kind": "press", "meta": { "age_hours_at_score": 15.6, "categories": [ "AI (artificial intelligence)", "Data protection", "House of Commons", "Politics", "Privacy", "Reform UK", "Surveillance", "Technology", "Technology policy", "UK news", "Wales", "Wearable technology", "Welsh politics" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:43:27.000Z", "first_source": "guardian-ai", "last_published_at": "2026-10-14T16:43:27.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:43:27.000Z", "sources": [ "guardian-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], 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over AI-enabled devices It was a flowing speech packed full of precise statistics – but Jason O’Connell, a Reform UK Senedd member, was not quite the gifted orator he had first appeared. 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In particular, adding embeddings for n-grams scales memory separately from compute use, because each pass only processes individual embedding", "title": "The COUNT: Counting N-grams Unlocks Scaling on Host Memory and Training-Free Adaptation", "url": "https://huggingface.co/papers/2610.20456" }, { "entities": [], "id": "619d70ca869024f5", "kind": "paper", "meta": { "age_hours_at_score": 15.8, "arxiv_id": "2610.20450", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:36:12.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T16:36:12.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:36:12.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.8, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T16:36:12.000Z", "score": 31.1, "source": "arxiv-newest", "summary": "Pairwise comparison data are a standard supervision signal in preference learning, but disagreement in such data can reflect stable latent heterogeneity, ephemeral stochasticity, or non-binary responses hidden by a forced-choice interface. 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My AI Agent Didn't Notice", "url": "https://openstation.blog/2026/10/13/i-deleted-168000-lines-of-comments-my-ai-agent-didnt-notice" }, { "entities": [], "id": "adf7964733f608fd", "kind": "paper", "meta": { "age_hours_at_score": 15.9, "arxiv_id": "2610.20439", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:28:22.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T16:28:22.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:28:22.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.7, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T16:28:22.000Z", "score": 31, "source": "arxiv-newest", "summary": "We study classification dynamics under small initialization and identify a pronounced non-monotone behavior of accuracy that we call the accuracy well: accuracy rises rapidly early in training, subsequently drops, and only much later recovers. We observe this phenomenon in deep linear networks, fully connected ReLU…", "title": "The Accuracy Well: Nonmonotone Accuracy Dynamics at Vanishing Initialization", "url": "https://arxiv.org/abs/2610.20439" }, { "entities": [], "id": "ed70a6c087a3ec66", "kind": "paper", "meta": { "age_hours_at_score": 15.9, "arxiv_id": "2610.20437", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:28:09.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T16:28:09.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:28:09.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.7, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T16:28:09.000Z", "score": 31, "source": "arxiv-newest", "summary": "Mid-2026 reporting on autonomous AI evaluation incidents claimed that frontier large language model (LLM) agents escaped server containment through emergent coordination and cloud infrastructure attacks. 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Scientific agent swarms can combine evidence and implementations from different research directions to guide their agents' subsequent experiments. However, poor experimental…", "title": "SwarmRSI: Recursive Self-Improvement through Evolving Scientific Agent Swarm", "url": "https://arxiv.org/abs/2610.20431" }, { "entities": [], "id": "b8513b86cdcc975b", "kind": "press", "meta": { "age_hours_at_score": 16.1, "categories": [ "AI (artificial intelligence)", "Business", "Gordon Brown", "Labour", "Medical research", "NHS", "Palantir", "Politics", "Science", "Technology", "Technology policy", "UK news" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:19:16.000Z", "first_source": "guardian-ai", "last_published_at": "2026-10-14T16:19:16.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:19:16.000Z", "sources": [ "guardian-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.theguardian.com/technology/artificialintelligenceai/rss", "first_seen_at": "2026-10-14T16:28:26.048Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.7, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-14T16:19:16.000Z", "score": 29.7, "source": "guardian-ai", "summary": "Kate Fleming says the government must look beyond the models it rewards today, while Jenny Haigh says unlocking potential means recognising that creativity and science are partners Gordon Brown wants Britain to invent, invest in and own the future ( British households would be £5,000 richer if we were more innovative.…", "title": "To unlock Britain’s innovation potential, Labour needs to think outside the box | Letters", "url": "https://theguardian.com/education/2026/oct/14/to-unlock-britains-innovation-potential-labour-needs-to-think-outside-the-box" }, { "entities": [], "id": "b51745a54a9fdc6a", "kind": "paper", "meta": { "age_hours_at_score": 16.1, "arxiv_id": "2610.20426", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:17:03.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T16:17:03.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:17:03.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.7, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T16:17:03.000Z", "score": 31, "source": "arxiv-newest", "summary": "This paper considers reliable in-sequence molecular communication, where bio-nanomachines exchange molecule-encoded information in a specified order, and investigates a new simulation-based method to incrementally evaluate communication protocols by gradually increasing the complexity of experimental settings. 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In the realm of LLM, the last two years have seen tremendous progress in RAG…", "title": "Analyzing performance of Retrieval Augmented Generation models for Question Answering Tasks", "url": "https://arxiv.org/abs/2610.20419" }, { "entities": [], "id": "6e6aab638b2a9ab0", "kind": "lab", "meta": { "age_hours_at_score": 16.1, "categories": [ "General Science", "Generative AI", "Health & Bioscience" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:14:34.000Z", "first_source": "google-research", "last_published_at": "2026-10-14T16:14:34.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:14:34.000Z", "sources": [ "google-research" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://research.google/blog/rss/", "first_seen_at": "2026-10-14T16:28:26.048Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.7, "severity": 0, "source_weight": 21.3 }, "source_weight": 0.85, "story": null }, "pillar": "attention", "published_at": "2026-10-14T16:14:34.000Z", "score": 36, "source": "google-research", "summary": "General Science", "title": "Research@ 2026: 10 ways we're amplifying human ingenuity", "url": "https://research.google/blog/research-2026-10-ways-were-amplifying-human-ingenuity" }, { "entities": [ "Microsoft", "OpenAI" ], "id": "9eeb23f82b4484a2", "kind": "press", "meta": { "age_hours_at_score": 16.1, "categories": [ "AI in practice", "Artificial Intelligence", "OpenAI" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:14:16.000Z", "first_source": "the-decoder", "last_published_at": "2026-10-14T16:14:16.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:14:16.000Z", "sources": [ "the-decoder" ] }, "engagement": null, "entity_kinds": { "labs": [ "Microsoft", "OpenAI" ], "models": [] }, "feed": "https://the-decoder.com/feed/", "first_seen_at": "2026-10-14T16:28:26.048Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 5, "engagement": 0, "entities": 6, "recency": 14.7, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": { "id": "8e6fe9129988", "is_lead": true, "lead": "9eeb23f82b4484a2", "linked_by": [ { "rule": "shared-words", "with": "162fb0b0c2fd9785", "words": [ "flagship", "openai", "reportedly" ] } ], "members": [ "162fb0b0c2fd9785", "9eeb23f82b4484a2" ], "severity": 0, "source_count": 2, "sources": [ "reddit-singularity", "the-decoder" ] } }, "pillar": "attention", "published_at": "2026-10-14T16:14:16.000Z", "score": 39.5, "source": "the-decoder", "summary": "OpenAI is racing to finish its next flagship model, which uses the same looped transformer technique as Astra to boost math and coding performance. The tradeoff is that looping makes safety monitoring harder. Microsoft accidentally revealed details about how the technique works on a public page. Whether or not the…", "title": "OpenAI will reportedly ship at least one model this year but its flagship still needs alignment work", "url": "https://the-decoder.com/openai-will-reportedly-ship-at-least-one-model-this-year-but-its-flagship-still-needs-alignment-work" }, { "entities": [], "id": "b5f10d8395692173", "kind": "lab", "meta": { "age_hours_at_score": 16.1, "categories": [ "General Science", "Machine Intelligence", "Natural Language Processing" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:14:00.000Z", "first_source": "google-research", "last_published_at": "2026-10-14T16:14:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:14:00.000Z", "sources": [ "google-research" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://research.google/blog/rss/", "first_seen_at": "2026-10-14T16:28:26.048Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.7, "severity": 0, "source_weight": 21.3 }, "source_weight": 0.85, "story": null }, "pillar": "attention", "published_at": "2026-10-14T16:14:00.000Z", "score": 36, "source": "google-research", "summary": "General Science", "title": "Analyzing technical documents with Deep Review", "url": "https://research.google/blog/analyzing-technical-documents-with-deep-review" }, { "entities": [], "id": "f4c3b9d58e154e81", "kind": "lab", "meta": { "age_hours_at_score": 16.1, "categories": [ "Earth AI", "General Science", "Machine Intelligence" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:14:00.000Z", "first_source": "google-research", "last_published_at": "2026-10-14T16:14:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:14:00.000Z", "sources": [ "google-research" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://research.google/blog/rss/", "first_seen_at": "2026-10-14T16:28:26.048Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.7, "severity": 0, "source_weight": 21.3 }, "source_weight": 0.85, "story": null }, "pillar": "attention", "published_at": "2026-10-14T16:14:00.000Z", "score": 36, "source": "google-research", "summary": "Earth AI", "title": "Putting Earth AI to work: Geospatial agents for our planet's biggest challenges", "url": "https://research.google/blog/putting-earth-ai-to-work-geospatial-agents-for-our-planets-biggest-challenges" }, { "entities": [], "id": "c92a4a3051f4f045", "kind": "paper", "meta": { "age_hours_at_score": 16.2, "arxiv_id": "2610.20418", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:13:12.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T16:13:12.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:13:12.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.7, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T16:13:12.000Z", "score": 31, "source": "arxiv-newest", "summary": "Tabular foundation models enable in-context learning by predicting unseen observations from a labeled support table without task-specific parameter updates. Despite recent advances, most existing approaches rely on dense architectures that apply the same transformations across features and datasets. We introduce…", "title": "OrionMoE-v1.0: A Mixture-of-Experts Foundation Model for Tabular In-Context Learning", "url": "https://arxiv.org/abs/2610.20418" }, { "entities": [], "id": "438d3d08b0116782", "kind": "paper", "meta": { "age_hours_at_score": 16.2, "arxiv_id": "2610.20417", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:13:09.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T16:13:09.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:13:09.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.7, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T16:13:09.000Z", "score": 31, "source": "arxiv-newest", "summary": "Audits of large language model (LLM) extraction from full-length documents report severe unreliability, leaving two questions open: what the failures actually consist of, and whether confidence signals can turn unreliable output into usable output. We run a preregistered, fully frozen evaluation of two new-generation…", "title": "From Unreliable to Usable: Decomposing Failure Modes and Confidence Signals in LLM Entity Extraction - Evidence from Cyber Threat Intelligence", "url": "https://arxiv.org/abs/2610.20417" }, { "entities": [], "id": "1fbeb04eece4e4fb", "kind": "paper", "meta": { "age_hours_at_score": 16.2, "arxiv_id": "2610.20416", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:13:03.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T16:13:03.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:13:03.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.7, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T16:13:03.000Z", "score": 31, "source": "arxiv-newest", "summary": "For the fundamental problem of prediction from expert advice, Freund asked whether one could compete with a comparator that switches $k$ times among only $m$ experts. For a discrete set of experts, this problem was resolved by Bousquet and Warmuth (2020), who gave an efficient algorithm with optimal regret matching…", "title": "Tracking a Small Set of Experts in Online Convex Optimization", "url": "https://arxiv.org/abs/2610.20416" }, { "entities": [], "id": "f37523724b175b71", "kind": "paper", "meta": { "age_hours_at_score": 16.2, "arxiv_id": "2610.20415", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:12:56.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T16:12:56.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:12:56.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.7, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T16:12:56.000Z", "score": 31, "source": "arxiv-newest", "summary": "Poor generalisation in deep neural networks is frequently associated with convergence to sharp minima. Sharpness-Aware Minimisation (SAM) improves generalisation of deep neural networks by minimising a combination of the training loss and the local sharpness of the loss landscape, biasing convergence towards flat…", "title": "One Size Does Not Fit All: Curvature-Aware Adaptive Perturbation for SAM", "url": "https://arxiv.org/abs/2610.20415" }, { "entities": [], "id": "504a28fcb44d1a3d", "kind": "lab", "meta": { "age_hours_at_score": 16.2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:12:42.000Z", "first_source": "huggingface-blog", "last_published_at": "2026-10-14T16:12:42.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:12:42.000Z", "sources": [ "huggingface-blog" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://huggingface.co/blog/feed.xml", "first_seen_at": "2026-10-14T16:28:26.048Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.7, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": null }, "pillar": "capability", "published_at": "2026-10-14T16:12:42.000Z", "score": 32.2, "source": "huggingface-blog", "summary": "", "title": "HiRO-ACEv2: Simulating extreme weather at 3 km resolution for a fraction of the cost", "url": "https://huggingface.co/blog/allenai/hiro-acev2" }, { "entities": [], "id": "f35f7703ae31b964", "kind": "paper", "meta": { "age_hours_at_score": 16.2, "arxiv_id": "2610.20413", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:11:14.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T16:11:14.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:11:14.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.6, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T16:11:14.000Z", "score": 30.9, "source": "arxiv-newest", "summary": "Momentum is a classical tool for accelerating convex optimization and a standard component of modern large language model training. Yet recent empirical work has questioned the usefulness of first-moment momentum in reinforcement learning with verifiable rewards (RLVR). We study this question through a bandit…", "title": "When Does Momentum Help in Reinforcement Learning with Verifiable Rewards?", "url": "https://arxiv.org/abs/2610.20413" }, { "entities": [], "id": "8b601535e4884c84", "kind": "paper", "meta": { "age_hours_at_score": 16.2, "arxiv_id": "2610.20409", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:08:28.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T16:08:28.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:08:28.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "query_url": "https://export.arxiv.org/api/query?search_query=cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL&sortBy=submittedDate&sortOrder=descending&start=0&max_results=60", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.6, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T16:08:28.000Z", "score": 30.9, "source": "arxiv-newest", "summary": "We develop a self-supervised reinforcement learning algorithm that learns to build towers of up to eight cubes from scratch, without prior data, rewards, demonstrations, or pretrained models. Our method, Contrastive Planning via Spherical Linear Interpolation (C-Slerp), uses contrastive representations as reusable…", "title": "Hyperspherical Contrastive Representations Unlock Planning and Exploration", "url": "https://arxiv.org/abs/2610.20409" }, { "entities": [ "Palantir" ], "id": "837acde9d8d557e6", "kind": "press", "meta": { "age_hours_at_score": 16.3, "categories": [ "AI (artificial intelligence)", "Computing", "London", "Metropolitan police", "Palantir", "Police", "Politics", "Sadiq Khan", "Technology", "UK news" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:05:22.000Z", "first_source": "guardian-ai", "last_published_at": "2026-10-14T16:05:22.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:05:22.000Z", "sources": [ "guardian-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Palantir" ], "models": [] }, "feed": "https://www.theguardian.com/technology/artificialintelligenceai/rss", "first_seen_at": 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open-source software could make AI-enhanced weather forecasting more affordable. www.theverge.com/science/1011...", "title": "Microsoft has a new machine learning tool to improve weather forecasts", "url": "https://bsky.app/profile/theverge.com/post/3mxtxhbs3sh2p" }, { "entities": [ "OpenAI", "xAI" ], "id": "26e984b41a938291", "kind": "press", "meta": { "age_hours_at_score": 16.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:05:01.000Z", "first_source": "techmeme", "last_published_at": "2026-10-14T16:05:01.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:05:01.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI", "xAI" ], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-14T16:28:26.048Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", 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leadership…", "title": "OpenAI names Uday Ruddarraju, who joined OpenAI last year and was promoted as CTO for compute in July, to lead its infrastructure and compute businesses (Dina Bass/Bloomberg)", "url": "https://techmeme.com/261014/p33" }, { "entities": [ "Meta AI" ], "id": "00eaaf086bb1c310", "kind": "paper", "meta": { "age_hours_at_score": 16.3, "arxiv_id": "2610.20402", "categories": [ "cs.AI", "cs.LG", "cs.CL" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:04:49.000Z", "first_source": "arxiv-newest", "last_published_at": "2026-10-14T16:04:49.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T16:04:49.000Z", "sources": [ "arxiv-newest" ] }, "engagement": null, "entity_kinds": { "labs": [ "Meta AI" ], "models": [] }, "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model 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Inspired by biological systems consolidation, we formulate Memory Consolidation (MC) as internalizing these gains while preserving non-target capabilities, connecting context-specified editing, skill learning, and…", "title": "Cortex: Consolidating Context into Parametric Memory", "url": "https://arxiv.org/abs/2610.20399" }, { "entities": [], "id": "a7df8c79aa3d5c63", "kind": "forum", "meta": { "age_hours_at_score": 16.3, "bsky_handle": "bloomberg.com", "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": "bloomberg.com", "published_at": "2026-10-14T16:01:47.000Z", "source": "wires-ai", "title": "Kansas Data Center Offers $1,000 a Year to Its Neighbors to Win Approval", "url": 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"reposts": 5, "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 5.8, "entities": 0, "recency": 14.6, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "compute", "published_at": "2026-10-14T16:01:47.000Z", "score": 47.9, "source": "bluesky-ai", "summary": "Posted on Bluesky by @bloomberg.com: One data center developer has a unique plan to squash pushback from community residents in Kansas: Give them $1,000 a year for the next decade.", "title": "Kansas Data Center Offers $1,000 a Year to Its Neighbors to Win Approval", "url": "https://bsky.app/profile/bloomberg.com/post/3mxu22oyx452b" }, { "entities": [], "id": "5d417b820dab61a7", "kind": "press", "meta": { "age_hours_at_score": 16.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T16:01:47.000Z", "first_source": "bloomberg-tech", "last_published_at": 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This critical narrative review follows feedback through its selection, learner update, and evaluation. It compares active learning, machine teaching, interactive segmentation, and sequential…", "title": "From Human Corrections to Learned Segmentations: A Critical Mathematical Review of Information, Learning, and Human Effort", "url": "https://arxiv.org/abs/2610.20396" }, { "entities": [ "Gemini" ], "id": "a3ce3e434c2cf2dc", "kind": "forum", "meta": { "age_hours_at_score": 16.4, "bsky_handle": "theverge.com", "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T16:00:00.000Z", "source": "verge-ai", "title": "Android is using AI to suss out scammers in Google Messages", "url": "https://theverge.com/tech/1010928/android-scam-protection-enhanced-protection-google-messages", "weight": 0.55 } ], "count": 2, "first_seen_published_at": "2026-10-14T16:00:00.000Z", "first_source": "verge-ai", "last_published_at": "2026-10-14T16:04:35.344Z", "lead_minutes": 5, "primary_published_at": "2026-10-14T16:04:35.344Z", 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protection mode uses Gemini to assess the intentions of unrecognized senders. www.theverge.com/tech/1010928...", "title": "Android is using AI to suss out scammers in Google Messages", "url": "https://bsky.app/profile/theverge.com/post/3mxtwfbomrs2w" }, { "entities": [], "id": "a2c897050581c7a4", "kind": "forum", "meta": { "age_hours_at_score": 16.4, "bsky_handle": "theverge.com", "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T16:00:00.000Z", "source": "verge-ai", "title": "Square’s new kiosk lets you talk to AI for help with your order", "url": "https://theverge.com/tech/1009086/square-kiosk", "weight": 0.55 } ], "count": 2, "first_seen_published_at": "2026-10-14T16:00:00.000Z", "first_source": "verge-ai", "last_published_at": "2026-10-14T16:12:35.813Z", "lead_minutes": 13, "primary_published_at": "2026-10-14T16:12:35.813Z", "sources": [ "bluesky-ai", "verge-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 5 }, "entity_kinds": { "labs": [], "models": [] }, "external_url": "https://www.theverge.com/tech/1009086/square-kiosk", "first_seen_at": "2026-10-14T16:28:26.048Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 5, "mode": "author-feeds", "outlet_domain": "theverge.com", "pillar_reason": "source default", "reposts": 0, "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 4, "entities": 0, "recency": 14.6, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-14T16:00:00.000Z", "score": 46.1, "source": "bluesky-ai", "summary": "Posted on Bluesky by @theverge.com: Square’s latest hardware is a big touchscreen with a conversational AI. www.theverge.com/tech/1009086...", "title": "Square’s new kiosk lets you talk to AI for help with your order", "url": "https://bsky.app/profile/theverge.com/post/3mxtwtlwhaf2w" }, { "entities": [ "Anthropic" ], "id": "8d806d95a0bf973b", "kind": "forum", "meta": { "age_hours_at_score": 16.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:59:49.000Z", "first_source": "lesswrong", "last_published_at": "2026-10-14T15:59:49.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:59:49.000Z", "sources": [ "lesswrong" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "feed": "https://www.lesswrong.com/feed.xml", "first_seen_at": "2026-10-14T16:28:26.048Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "regulatory/legal keyword", "rejected_off_topic": 2, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 14.6, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "governance", "published_at": "2026-10-14T15:59:49.000Z", "score": 30.1, "source": "lesswrong", "summary": "Anthropic released some documents earlier this year due to the book copyright litigation. I think a few of those documents might be of interest to some people here and haven't gotten much attention. In this post I link to some of those documents, and quote the bits I find interesting. [1] (This expands on my quick…", "title": "Some interesting internal documents from Anthropic litigation", "url": "https://lesswrong.com/posts/em5wnA7gpQSvfsuxQ/some-interesting-internal-documents-from-anthropic" }, { "entities": [ "OpenAI" ], "id": "bab59c5f7007dae1", "kind": "press", "meta": { "age_hours_at_score": 16.5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:54:04.000Z", "first_source": "bbc-tech", "last_published_at": "2026-10-14T15:54:04.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:54:04.000Z", "sources": [ "bbc-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "feed": "https://feeds.bbci.co.uk/news/technology/rss.xml", "first_seen_at": "2026-10-14T16:28:26.048Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 10, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 14.6, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "f0b473571419", "is_lead": false, "lead": "66f5e62202fa6571", "linked_by": [ { "rule": "shared-words", "with": "1d9b0f2a4e47bd83", "words": [ "engineer", "openai", "saudi" ] } ], "members": [ "0343a01e6ce9dfac", "043aba25a09a5b5c", "18ff78a815c5e459", "1d9b0f2a4e47bd83", "2444a8e5a9bf9dd7", "2845c6266106c48a", "3666ba1e02facdbf", "4533e2b5df1cb4f7", "47b1c61501a00890", "4a54f7ed9060d0c2", "4e5e343645d9b16e", "66f5e62202fa6571", "6986f04e2795a0c1", "6bec7d11ba5f3d7b", "75aa40e09badce18", "82065bf620f89892", "bab59c5f7007dae1", "c666cbb353ec8719", "de2ec4083de14f91", "e3d70f1a671b390f", "f67cff07b7fcf963", "fdcf029b66aaffa6" ], "severity": 0.35, "source_count": 16, "sources": [ "arstechnica-ai", "bbc-tech", "bloomberg-tech", "bluesky-ai", "ft-ai", "gnews-ai", "guardian-ai", "hn-fresh-ai", "nyt-tech", "reddit-chatgpt", "techcrunch-ai", "techmeme", "the-decoder", "verge-ai", "wired-ai", "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-14T15:54:04.000Z", "score": 32.6, "source": "bbc-tech", "summary": "Abdulrahman Alfozan had worked for the AI giant for more than a year before he was arrested by the FBI.", "title": "Toilet paper and secret recorders: What we know about the arrest of the Saudi OpenAI engineer", "url": "https://bbc.co.uk/news/articles/cm04054ykqqqo" }, { "entities": [], "id": "1dc140e1d2a6189c", "kind": "press", "meta": { "age_hours_at_score": 16.5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:53:37.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T15:53:37.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:53:37.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-14T16:28:26.048Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "hardware/capital keyword", "rejected_off_topic": 5, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.6, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "compute", "published_at": "2026-10-14T15:53:37.000Z", "score": 29.6, "source": "bloomberg-tech", "summary": "Spanish clean-energy company Solaria Energía y Medio Ambiente SA plans to build a solar plant in Uruguay to supply a data center with a power capacity of 500 megawatts near Montevideo, according to a person with knowledge of the matter.", "title": "Spain’s Solaria to Build Solar Plant for Uruguay Data Center", "url": "https://bloomberg.com/news/articles/2026-10-14/spain-s-solaria-to-build-solar-plant-for-uruguay-data-center" }, { "entities": [], "id": "05dded9190880339", "kind": "forum", "meta": { "age_hours_at_score": 16.5, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:52:53.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-14T15:52:53.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:52:53.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T16:36:33.474Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.6, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T15:52:53.000Z", "score": 23.4, "source": "reddit-artificial", "summary": "submitted by /u/Competitive-Fudge174 [link] [comments]", "title": "Ramp’s $1.5 Million Week of Spending Leads to Glass AI Tool", "url": "https://reddit.com/r/artificial/comments/1x5uvsf/ramps_15_million_week_of_spending_leads_to_glass" }, { "entities": [], "id": "f526aa5cdafcc259", "kind": "press", "meta": { "age_hours_at_score": 16.5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:51:42.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T15:51:42.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:51:42.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-14T16:28:26.048Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 6, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.6, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "markets", "published_at": "2026-10-14T15:51:42.000Z", "score": 29.6, "source": "bloomberg-tech", "summary": "Santander Executive Chair Ana Botín says jobs will change as AI advances and acknowledges there could be adjustments, but the bank isn’t seeing a major impact on employment yet. Botín also discusses the opportunities she sees for AI within Santander. Botin is on this week's episode of The David Rubenstein Show: Peer…", "title": "AI Will Change Banking Jobs, Says Santander's Botín", "url": "https://bloomberg.com/news/videos/2026-10-14/ai-will-change-banking-jobs-says-santander-s-botin-video" }, { "entities": [], "id": "25651c58d3ab474d", "kind": "press", "meta": { "age_hours_at_score": 16.6, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T15:48:09.000Z", "source": "ft-ai", "title": "Freshfields partners explore carve-out of AI unit worth ‘billions’, leaked emails reveal", "url": "https://ft.com/content/64f058ff-c8c2-4e63-bf7f-57c08ce685cb?syn-25a6b1a6=1", "weight": 0.65 } ], "count": 2, "first_seen_published_at": "2026-10-14T15:48:09.000Z", "first_source": "ft-ai", "last_published_at": "2026-10-14T15:48:09.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:48:09.000Z", "sources": [ "ft-ai", "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T16:28:26.048Z", "incident": { "band": "B", "excluded": null, "severity": 0.6, "strong_signal": false, "terms": [ { "field": "title", "matched": "leaked", "term": "leaked", "tier": "b", "weight": 0.6 } ] }, "outlet_domain": "ft.com", "pillar_reason": "source default", "publisher": "Financial Times", "publisher_url": "https://www.ft.com", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.5, "severity": 9, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-14T15:48:09.000Z", "score": 54.8, "source": "wires-ai", "summary": "Financial Times, via Google News.", "title": "Freshfields partners explore carve-out of AI unit worth ‘billions’, leaked emails reveal", "url": "https://news.google.com/rss/articles/CBMihAFBVV95cUxNOHIwVERTRUVqaGdZRUpHLXBFcTNwNHBlb3JXaW1pbmwzd0xzelU0dFctdVJMVDlCZ0lmTS1UUEREOHVZaDRnbDNESVRubmNjbUtvUHg4aHNyNFhzTXVwNmhXUHJvemRidlh0SDlqaXBqRWlNcF9lLVMyMmZYVktoaXktdlg?oc=5" }, { "entities": [ "Microsoft" ], "id": "317dec53619209f7", "kind": "forum", "meta": { "age_hours_at_score": 16.6, "comments": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:45:48.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-14T15:45:48.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:45:48.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50089517", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 3 }, "entity_kinds": { "labs": [ "Microsoft" ], "models": [] }, "first_seen_at": "2026-10-14T15:49:12.623Z", "hn_id": "50089517", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "defold.com", "pillar_reason": "source default", "points": 3, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 4, "entities": 3, "recency": 14.5, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-14T15:45:48.000Z", "score": 32.8, "source": "hn-fresh-ai", "summary": "New on Hacker News: 3 points, 0 comments so far.", "title": "Defold adds support for Microsoft Xbox", "url": "https://defold.com/2026/10/12/Defold-adds-support-for-Microsoft-Xbox" }, { "entities": [ "NVIDIA" ], "id": "cba86d9ccacbe15b", "kind": "forum", "meta": { "age_hours_at_score": 16.6, "categories": [ "singularity" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:45:15.000Z", "first_source": "reddit-singularity", "last_published_at": "2026-10-14T15:45:15.000Z", "lead_minutes": 0, "primary_published_at": 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"https://reddit.com/r/singularity/comments/1x5uolg/nvidia_alphanist_emergent_assembly_skills_from" }, { "entities": [ "GPT", "OpenAI" ], "id": "6087856fd9ac17e2", "kind": "press", "meta": { "age_hours_at_score": 16.6, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:45:01.000Z", "first_source": "techmeme", "last_published_at": "2026-10-14T15:45:01.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:45:01.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [ "GPT" ] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-14T15:49:12.623Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 5, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 14.5, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": null }, "pillar": "attention", "published_at": "2026-10-14T15:45:01.000Z", "score": 38, "source": "techmeme", "summary": "Reuters : Filing: OpenAI says a staffer who joined Apple sent 100+ files to a personal account and made a presentation about an unreleased product to Apple leadership — An OpenAI employee who left the ChatGPT maker for Apple sent more than 100 files about an unreleased product to a personal account …", "title": "Filing: OpenAI says a staffer who joined Apple sent 100+ files to a personal account and made a presentation about an unreleased product to Apple leadership (Reuters)", "url": "https://techmeme.com/261014/p32" }, { "entities": [ "OpenAI" ], "id": "1329ad037a7166fd", "kind": "press", "meta": { "age_hours_at_score": 16.7, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T15:41:43.000Z", "source": "bloomberg-tech", "title": "OpenAI Names Ruddarraju to Lead Data Center, Computing Unit", "url": "https://bloomberg.com/news/articles/2026-10-14/openai-names-uday-ruddarraju-to-lead-data-center-computing-unit", "weight": 0.6 }, { "dedup_key": null, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 3 }, "kind": "forum", "outlet": "bloomberg.com", "published_at": "2026-10-14T16:00:23.000Z", "source": "bluesky-ai", "title": "OpenAI Names Uday Ruddarraju to Lead Data Center, Computing Unit", "url": "https://bsky.app/profile/bloomberg.com/post/3mxtw5qhr4a2b", "weight": 0.6 } ], "count": 3, "first_seen_published_at": "2026-10-14T15:41:43.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T16:00:23.000Z", "lead_minutes": 19, "primary_published_at": "2026-10-14T15:41:43.000Z", "sources": [ "bloomberg-tech", "bluesky-ai", "wires-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 3 }, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "first_seen_at": "2026-10-14T16:28:26.048Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "bloomberg.com", "pillar_reason": "hardware/capital keyword", "publisher": "Bloomberg.com", "publisher_url": "https://www.bloomberg.com", "retained": true, "score_components": { "corroboration": 25, "coverage": 5, "engagement": 3.1, "entities": 3, "recency": 14.5, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": { "id": "5eb9defaa87b", "is_lead": true, "lead": "1329ad037a7166fd", "linked_by": [ { "rule": "shared-words", "with": "26e984b41a938291", "words": [ "lead", "nam", "openai", "ruddarraju" ] } ], "members": [ "1329ad037a7166fd", "26e984b41a938291" ], "severity": 0, "source_count": 4, "sources": [ "bloomberg-tech", "bluesky-ai", "techmeme", "wires-ai" ] } }, "pillar": "compute", "published_at": "2026-10-14T15:41:43.000Z", "score": 69.4, "source": "wires-ai", "summary": "Bloomberg.com, via Google News.", "title": "OpenAI Names Ruddarraju to Lead Data Center, Computing Unit", "url": "https://news.google.com/rss/articles/CBMitAFBVV95cUxPaDAyMkltWVVyWTRyaEU5bV9sU0FlNG1mMUZLR0hZcm5ORFY3dENPNWpROFdVc3A5c3lxUVlUQTdzN2VZWjNIdTFtSkdaZTQ2Q2FWcklBRkp1NmV1di1MV2dxN1pSQ0NfWkFsMlpqc0h1NFhvbnladzdBazZwd19ld2swa18tSmlOX2dNcmp2bzBQVmVDZXMxVVZfbW91ZXZvYThPX3NMSzNuZmpEM2I2d2tuOHo?oc=5" }, { "entities": [ "OpenAI" ], "id": "0868682ecd57b4e2", "kind": "forum", "meta": { "age_hours_at_score": 16.7, "comments": 3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:40:55.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-14T15:40:55.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:40:55.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50089421", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 4 }, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "first_seen_at": "2026-10-14T12:57:32.237Z", "hn_id": "50089421", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "bloomberg.com", "pillar_reason": "source default", "points": 4, "retained": true, "score_components": { "corroboration": 0, "coverage": 5, "engagement": 4.7, "entities": 3, "recency": 14.5, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": { "id": "883050d46153", "is_lead": true, "lead": "0868682ecd57b4e2", "linked_by": [ { "rule": "shared-words", "with": "eae37e4df31c8021", "words": [ "cfo", "openai", "safety" ] } ], "members": [ "0868682ecd57b4e2", "eae37e4df31c8021" ], "severity": 0, "source_count": 2, "sources": [ "bloomberg-tech", "hn-fresh-ai" ] }, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-14T15:40:55.000Z", "score": 38.5, "source": "hn-fresh-ai", "summary": "New on Hacker News: 4 points, 3 comments so far.", "title": "OpenAI CFO Says Firms Using Chinese AI Models Are Risking Safety", "url": "https://bloomberg.com/news/articles/2026-10-14/openai-cfo-says-firms-using-chinese-ai-models-are-risking-safety" }, { "entities": [ "Amazon" ], "id": "9c1207d8cfd15ad4", "kind": "forum", "meta": { "age_hours_at_score": 16.8, "comments": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:36:27.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-14T15:36:27.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:36:27.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50089333", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 3 }, "entity_kinds": { "labs": [ "Amazon" ], "models": [] }, "first_seen_at": "2026-10-14T16:28:26.048Z", "hn_id": "50089333", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "computerweekly.com", "pillar_reason": "source default", "points": 3, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 4, "entities": 3, "recency": 14.5, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-14T15:36:27.000Z", "score": 32.8, "source": "hn-fresh-ai", "summary": "New on Hacker News: 3 points, 0 comments so far.", "title": "Amazon uses AI-powered 'risk' system to track union activity globally", "url": "https://computerweekly.com/news/366652174/Amazon-uses-AI-powered-risk-system-to-track-union-activity-globally" }, { "entities": [], "id": "9456f85591dd00bf", "kind": "forum", "meta": { "age_hours_at_score": 16.8, "categories": [ "ControlProblem" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:32:34.000Z", "first_source": "reddit-controlproblem", "last_published_at": "2026-10-14T15:32:34.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:32:34.000Z", "sources": [ "reddit-controlproblem" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/ControlProblem/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T16:41:51.933Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.5, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T15:32:34.000Z", "score": 23.3, "source": "reddit-controlproblem", "summary": "submitted by /u/chillinewman [link] [comments]", "title": "“Researchers put a dead salmon in an fMRI scanner, showed it pictures of humans, and asked it to guess their emotions. The scan showed brain activity, In a dead fish. Now, the recent study proved…", "url": "https://reddit.com/r/ControlProblem/comments/1x5ucu0/researchers_put_a_dead_salmon_in_an_fmri_scanner" }, { "entities": [ "Claude", "Gemini" ], "id": "2e46b375237b9ac3", "kind": "forum", "meta": { "age_hours_at_score": 16.9, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:30:08.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-14T15:30:08.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:30:08.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Claude", "Gemini" ] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T18:59:02.769Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 14.5, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T15:30:08.000Z", "score": 29.3, "source": "reddit-artificial", "summary": "was looking at Google's Gemini 4 Argon benchmarks and something caught my attention. Argon beats Opus 5.5 on DeepSWE (77.9% vs 74.2%). but on Terminal-Bench 4.0, Opus wins by quite a bit (66.4% vs 57.4%). and Opus also wins on FrontierSWE. same models, very different results depending on what they're being tested on.…", "title": "Google's own benchmarks say Gemini beats Opus at coding... and also loses to Opus at coding", "url": "https://reddit.com/r/artificial/comments/1x5uai6/googles_own_benchmarks_say_gemini_beats_opus_at" }, { "entities": [], "id": "44191c6fb19fbc8b", "kind": "forum", "meta": { "age_hours_at_score": 16.9, "comments": 3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:26:43.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-14T15:26:43.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:26:43.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50089119", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 19 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T15:31:20.453Z", "hn_id": "50089119", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "min_points": 8, "pillar_reason": "source default", "points": 19, "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 8.7, "entities": 0, "recency": 14.4, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "window_hours": 72 }, "pillar": "attention", "published_at": "2026-10-14T15:26:43.000Z", "score": 38.1, "source": "hn-ai", "summary": "19 points, 3 comments on Hacker News.", "title": "Behavior-Centric AI Design", "url": "https://swamp-club.com/behavior" }, { "entities": [ "Tencent" ], "id": "41822b8b2c11a5bf", "kind": "press", "meta": { "age_hours_at_score": 17, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:25:02.000Z", "first_source": "techmeme", "last_published_at": "2026-10-14T15:25:02.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:25:02.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [ "Tencent" ], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-14T15:31:20.453Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 5, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 14.4, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": null }, "pillar": "attention", "published_at": "2026-10-14T15:25:02.000Z", "score": 34.9, "source": "techmeme", "summary": "Dylan Freedman / New York Times : Researchers say they discovered and communicated with Chinese AI agents that were trying to bypass security controls on websites and were traced to Tencent — Researchers discovered that the agents were trying to bypass security controls on websites and made contact with them.", "title": "Researchers say they discovered and communicated with Chinese AI agents that were trying to bypass security controls on websites and were traced to Tencent (Dylan Freedman/New York Times)", "url": "https://techmeme.com/261014/p31" }, { "entities": [], "id": "9acb37be4c6ade4b", "kind": "press", "meta": { "age_hours_at_score": 17, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:24:30.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T15:24:30.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:24:30.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-14T15:31:20.453Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 7, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.4, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "markets", "published_at": "2026-10-14T15:24:30.000Z", "score": 29.4, "source": "bloomberg-tech", "summary": "Ted Mortonson, Managing Director: Technology at Baird, discusses his outlook for tech and the AI trade. (Source: Bloomberg)", "title": "Amount of AI Going into Defense Budgets is 'Immense,' Says Ted Mortonson", "url": "https://bloomberg.com/news/videos/2026-10-14/ai-in-defense-budgets-is-immense-says-mortonson-video" }, { "entities": [], "id": "254893e3afde90c7", "kind": "forum", "meta": { "age_hours_at_score": 17, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:24:11.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-14T15:24:11.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:24:11.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T17:54:14.070Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.4, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T15:24:11.000Z", "score": 23.2, "source": "reddit-artificial", "summary": "Had an odd one this month. Found out through an expense report that someone on the marketing side has been paying for an AI writing tool out of pocket and expensing it, because getting it approved through the normal channel felt like too much hassle. It's not malicious, they just wanted to get their job done faster.…", "title": "Our shadow IT discovery process is apparently the expense report", "url": "https://reddit.com/r/artificial/comments/1x5u4yt/our_shadow_it_discovery_process_is_apparently_the" }, { "entities": [], "id": "048307d09dbc91cb", "kind": "forum", "meta": { "age_hours_at_score": 17, "comments": 5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:23:13.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-14T15:23:13.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:23:13.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50089045", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 14 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "hn_id": "50089045", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "min_points": 8, "pillar_reason": "source default", "points": 14, "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 7.8, "entities": 0, "recency": 14.4, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "window_hours": 72 }, "pillar": "attention", "published_at": "2026-10-14T15:23:13.000Z", "score": 37.2, "source": "hn-ai", "summary": "14 points, 5 comments on Hacker News.", "title": "Show HN: The first short movie built using code and AI", "url": "https://fframes.studio/the-why" }, { "entities": [], "id": "c4b1dbad1a2733d6", "kind": "forum", "meta": { "age_hours_at_score": 17, "categories": [ "MachineLearning" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:22:01.000Z", "first_source": "reddit-machinelearning", "last_published_at": "2026-10-14T15:22:01.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:22:01.000Z", "sources": [ "reddit-machinelearning" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/MachineLearning/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T15:49:12.623Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.4, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null }, "pillar": "attention", "published_at": "2026-10-14T15:22:01.000Z", "score": 25.7, "source": "reddit-machinelearning", "summary": "Went down the rabbit hole to map the different Post-Transformer architectures. Various directions include Dragon Hatchling (BDH), Mamba, RWKV, xLSTM, Titans, and a bunch of hybrid models coming from different AI Labs. My interest areas are architectural changes that can improve reasoning, memory, and adaptation within…", "title": "Mapping the Post-Transformer Architecture Landscape in 2026 [D]", "url": "https://reddit.com/r/MachineLearning/comments/1x5u2wz/mapping_the_posttransformer_architecture" }, { "entities": [], "id": "4e3617073d6289fc", "kind": "press", "meta": { "age_hours_at_score": 17, "categories": [ "Anthropic AI LLC", "Artificial Intelligence", "China", "Computer Security", "OpenAI Labs", "Research", "Tencent Holdings Ltd" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:21:46.000Z", "first_source": "nyt-tech", "last_published_at": "2026-10-14T15:21:46.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:21:46.000Z", "sources": [ "nyt-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://rss.nytimes.com/services/xml/rss/nyt/Technology.xml", "first_seen_at": "2026-10-14T11:11:00.898Z", "incident": { "band": "A", "excluded": null, "severity": 1, "strong_signal": true, "terms": [ { "field": "title", "matched": "rogue", "term": "rogue", "tier": "a", "weight": 1 } ] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 8, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.4, "severity": 15, "source_weight": 17.5 }, "source_weight": 0.7, "story": null }, "pillar": "attention", "published_at": "2026-10-14T15:21:46.000Z", "score": 46.9, "source": "nyt-tech", "summary": "Researchers discovered that the agents were trying to bypass security controls on websites and made contact with them. A Times reporter was invited to join the discussion.", "title": "Rogue A.I. Agents from China Chatted with Researchers", "url": "https://nytimes.com/2026/10/14/technology/chinese-ai-agents-rogue.html" }, { "entities": [ "Anthropic" ], "id": "5c998e059454924e", "kind": "press", "meta": { "age_hours_at_score": 17.1, "corroboration": { "also": [ { "dedup_key": null, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 17 }, "kind": "forum", "outlet": "theverge.com", "published_at": "2026-10-14T15:39:34.533Z", "source": "bluesky-ai", "title": "Anthropic’s self-replicating AI warning is alarming deja vu", "url": "https://bsky.app/profile/theverge.com/post/3mxtuyklyd32u", "weight": 0.6 }, { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T15:19:02.000Z", "source": "verge-ai", "title": "Anthropic’s self-replicating AI warning is alarming deja vu", "url": "https://theverge.com/ai-artificial-intelligence/1011108/anthropic-self-replicating-ai-smart-devices-risk", "weight": 0.55 } ], "count": 3, "first_seen_published_at": "2026-10-14T15:19:02.000Z", "first_source": "gnews-ai", "last_published_at": "2026-10-14T15:39:34.533Z", "lead_minutes": 21, "primary_published_at": "2026-10-14T15:19:02.000Z", "sources": [ "bluesky-ai", "gnews-ai", "verge-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 17 }, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "first_seen_at": "2026-10-14T15:31:20.453Z", "incident": { "band": "A", "excluded": null, "severity": 1, "strong_signal": true, "terms": [ { "field": "title", "matched": "self replicating", "term": "self replicating", "tier": "a", "weight": 1 } ] }, "outlet_domain": "theverge.com", "pillar_reason": "source default", "publisher": "The Verge", "publisher_url": "https://www.theverge.com", "retained": true, "score_components": { "corroboration": 25, "coverage": 15, "engagement": 6.5, "entities": 3, "recency": 14.4, "severity": 15, "source_weight": 13.8 }, "source_weight": 0.55, "story": { "id": "44510b1e1311", "is_lead": true, "lead": "5c998e059454924e", "linked_by": [ { "rule": "escalation-signal", "with": "cf1fd0f11e2a1890", "words": [ "warn" ] } ], "members": [ "21e7ded87d1e2500", "25a1fb29d96c2c18", "5c998e059454924e", "818085bd085cd9ce", "98900f3d4137eee5", "cf1fd0f11e2a1890" ], "severity": 1, "source_count": 7, "sources": [ "arstechnica-ai", "bluesky-ai", "ft-ai", "gnews-ai", "guardian-ai", "verge-ai", "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-14T15:19:02.000Z", "score": 92.7, "source": "gnews-ai", "summary": "The Verge, via Google News.", "title": "Anthropic’s self-replicating AI warning is an alarming deja vu", "url": "https://news.google.com/rss/articles/CBMirAFBVV95cUxPVmlsZkg4MjVmSXE4WDBLY29yUEZxZmo1SG9hcHlZdmRFLTF3V2lyWUpqbEZhNHF1aXpaWG9BdGdhREx6Tnk5Mjc4dWpkRUIyR0huejRDN29tM2hZQW1iNjJqM1FSdUF3cVJKS2VScUVNbG1mRFBjM3ZUV3RHeXV6TjNFM01EeW1vS2Q3SHc0TXJZcnNPb2JoMmNhNk5nQk9tTXprczFfbmlKeFBx?oc=5" }, { "entities": [], "id": "d9705c0d2ff766e7", "kind": "forum", "meta": { "age_hours_at_score": 17.1, "categories": [ "singularity" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:17:03.000Z", "first_source": "reddit-singularity", "last_published_at": "2026-10-14T15:17:03.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:17:03.000Z", "sources": [ "reddit-singularity" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/singularity/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T16:28:26.048Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.4, "severity": 0, "source_weight": 10 }, "source_weight": 0.4, "story": null }, "pillar": "attention", "published_at": "2026-10-14T15:17:03.000Z", "score": 24.4, "source": "reddit-singularity", "summary": "submitted by /u/Particular_One_1764 [link] [comments]", "title": "this line was written in 1960s btw, felt like he time travelled.", "url": "https://reddit.com/r/singularity/comments/1x5ty7c/this_line_was_written_in_1960s_btw_felt_like_he" }, { "entities": [], "id": "ab12d93437757467", "kind": "press", "meta": { "age_hours_at_score": 17.1, "categories": [ "AI", "Apps", "Deezer", "Media & Entertainment", "ai music", "streaming music" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:16:04.000Z", "first_source": "techcrunch-ai", "last_published_at": "2026-10-14T15:16:04.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:16:04.000Z", "sources": [ "techcrunch-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://techcrunch.com/category/artificial-intelligence/feed/", "first_seen_at": "2026-10-14T15:31:20.453Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.4, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-14T15:16:04.000Z", "score": 29.4, "source": "techcrunch-ai", "summary": "Deezer said that the new toggle will be rolled out by the end of the year.", "title": "Deezer will soon let you filter out AI-generated music", "url": "https://techcrunch.com/2026/10/14/deezer-will-soon-let-you-filter-out-ai-generated-music" }, { "entities": [], "id": "d66c3929d9d8d625", "kind": "press", "meta": { "age_hours_at_score": 17.1, "categories": [ "AI", "Apps", "Biotech & Health", "Crypto", "Fintech", "Media & Entertainment", "Robotics", "Startups", "TechCrunch Disrupt", "Venture" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:13:24.000Z", "first_source": "techcrunch-ai", "last_published_at": "2026-10-14T15:13:24.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:13:24.000Z", "sources": [ "techcrunch-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://techcrunch.com/category/artificial-intelligence/feed/", "first_seen_at": "2026-10-14T15:31:20.453Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.4, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-14T15:13:24.000Z", "score": 29.4, "source": "techcrunch-ai", "summary": "Get a 50% discount on the last two days of TechCrunch Disrupt 2026, October 14-15, in San Francisco. Register now and join the rest of the startup ecosystem. Today, it's all about physical and software AI.", "title": "Save 50% on your TechCrunch Disrupt 2026 ticket and join the last 2 days in San Francisco’s Moscone West", "url": "https://techcrunch.com/2026/10/14/save-50-on-your-techcrunch-disrupt-2026-ticket-and-join-the-last-two-days-in-san-francisco" }, { "entities": [ "OpenAI" ], "id": "2bae91c660f5d997", "kind": "forum", "meta": { "age_hours_at_score": 17.2, "categories": [ "singularity" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:10:33.000Z", "first_source": "reddit-singularity", "last_published_at": "2026-10-14T15:10:33.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:10:33.000Z", "sources": [ "reddit-singularity" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "feed": "https://www.reddit.com/r/singularity/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T05:24:30.311Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 14.4, "severity": 0, "source_weight": 10 }, "source_weight": 0.4, "story": null }, "pillar": "attention", "published_at": "2026-10-14T15:10:33.000Z", "score": 27.4, "source": "reddit-singularity", "summary": "A couple of interesting statements about OpenAI's recent 'maths breakthroughs' by Demis today at the International AI Summit in Dublin. I think it was an interesting discussion, given how often he highlights collaboration I am not particularly surprised by his views. He also said mathematics skills are more needed…", "title": "Demis Hassabis' opinion about OpenAI’s approach to its recent math breakthroughs, argues they should involve the maths community, says math skills are needed more than ever but mathematicians should…", "url": "https://reddit.com/r/singularity/comments/1x5trzz/demis_hassabis_opinion_about_openais_approach_to" }, { "entities": [ "Amazon" ], "id": "b2b4616bfbcc2139", "kind": "forum", "meta": { "age_hours_at_score": 17.2, "bsky_handle": "reuters.com", "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:10:18.000Z", "first_source": "bluesky-ai", "last_published_at": "2026-10-14T15:10:18.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:10:18.000Z", "sources": [ "bluesky-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 21 }, "entity_kinds": { "labs": [ "Amazon" ], "models": [] }, "external_url": "https://reut.rs/47Zasjz", "first_seen_at": "2026-10-14T15:31:20.453Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 21, "mode": "author-feeds", "outlet_domain": "reuters.com", "pillar_reason": "source default", "reposts": 15, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 6.9, "entities": 3, "recency": 14.4, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-14T15:10:18.000Z", "score": 39.3, "source": "bluesky-ai", "summary": "Posted on Bluesky by @reuters.com: Fires surge in Brazil's Amazon, fueled by El Niño and climate change reut.rs/47Zasjz", "title": "Fires surge in Brazil's Amazon, fueled by El Niño and climate change", "url": "https://bsky.app/profile/reuters.com/post/3mxtte5v7ul2t" }, { "entities": [], "id": "605dd33d2ef5bf71", "kind": "forum", "meta": { "age_hours_at_score": 17.2, "categories": [ "ControlProblem" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:08:32.000Z", "first_source": "reddit-controlproblem", "last_published_at": "2026-10-14T15:08:32.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:08:32.000Z", "sources": [ "reddit-controlproblem" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/ControlProblem/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T16:41:51.933Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.4, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T15:08:32.000Z", "score": 23.2, "source": "reddit-controlproblem", "summary": "submitted by /u/syedshad [link] [comments]", "title": "Introducing the Fingerprinting Leaderboard: testing whether AI models retain proof of origin", "url": "https://reddit.com/r/ControlProblem/comments/1x5tq1y/introducing_the_fingerprinting_leaderboard" }, { "entities": [], "id": "21708234358eadea", "kind": "press", "meta": { "age_hours_at_score": 17.2, "categories": [ "AI", "WhatsApp", "ai chat", "bessemer", "workplace communication" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:08:00.000Z", "first_source": "techcrunch-ai", "last_published_at": "2026-10-14T15:08:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:08:00.000Z", "sources": [ "techcrunch-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://techcrunch.com/category/artificial-intelligence/feed/", "first_seen_at": "2026-10-14T15:31:20.453Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.4, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "37311edb8439", "is_lead": false, "lead": "e143bcfbc914eeaf", "linked_by": [ { "rule": "shared-words", "with": "e143bcfbc914eeaf", "words": [ "44m", "aim", "replac", "whatsapp", "workplac", "zenzap" ] } ], "members": [ "21708234358eadea", "e143bcfbc914eeaf" ], "severity": 0, "source_count": 2, "sources": [ "techcrunch-ai", "techmeme" ] } }, "pillar": "attention", "published_at": "2026-10-14T15:08:00.000Z", "score": 29.4, "source": "techcrunch-ai", "summary": "Zenzap is used by organizations including Starbucks and Taco Bell.", "title": "Zenzap raises $44M for its AI chat app that aims to replace WhatsApp in the workplace", "url": "https://techcrunch.com/2026/10/14/zenzap-raises-44m-for-its-ai-chat-app-that-aims-to-replace-whatsapp-in-the-workplace" }, { "entities": [ "NVIDIA" ], "id": "8d2264e55e1f2bc9", "kind": "press", "meta": { "age_hours_at_score": 17.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:04:55.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T15:04:55.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:04:55.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "NVIDIA" ], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-14T15:31:20.453Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "hardware/capital keyword", "rejected_off_topic": 8, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 14.3, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "compute", "published_at": "2026-10-14T15:04:55.000Z", "score": 32.3, "source": "bloomberg-tech", "summary": "DA Davidson Head of Technology Research Gil Luria joined Bloomberg Open Interest to explain why Nvidia’s ecosystem and chip demand could support growth through 2030, while warning that debt-heavy data-center builders may face serious pressure. (Source: Bloomberg)", "title": "AI’s Biggest Risk: Debt-Fueled Growth", "url": "https://bloomberg.com/news/videos/2026-10-14/ai-s-biggest-risk-debt-fueled-growth-video" }, { "entities": [], "id": "1aff68237d4dd55a", "kind": "forum", "meta": { "age_hours_at_score": 17.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:03:26.000Z", "first_source": "lesswrong", "last_published_at": "2026-10-14T15:03:26.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:03:26.000Z", "sources": [ "lesswrong" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.lesswrong.com/feed.xml", "first_seen_at": "2026-10-14T15:31:20.453Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 6, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.3, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "governance", "published_at": "2026-10-14T15:03:26.000Z", "score": 26.8, "source": "lesswrong", "summary": "[This is the ninth piece in my series on the Parametric Process Assumption (PPA), a new model of anthropics. You should start here if you only want to read one piece on the PPA, but if you only want to read two pieces, this is the second one to read. The previous piece is \" The Fermi Paradox, Fine-Tuning, and the SSA…", "title": "PPA 9: Putting it All Together", "url": "https://lesswrong.com/posts/fhqbJv4QrK7HzGxEm/ppa-9-putting-it-all-together" }, { "entities": [], "id": "4bbb25f1c25a854a", "kind": "forum", "meta": { "age_hours_at_score": 17.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:03:23.000Z", "first_source": "lesswrong", "last_published_at": "2026-10-14T15:03:23.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:03:23.000Z", "sources": [ "lesswrong" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.lesswrong.com/feed.xml", "first_seen_at": "2026-10-14T15:31:20.453Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 6, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.3, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "governance", "published_at": "2026-10-14T15:03:23.000Z", "score": 26.8, "source": "lesswrong", "summary": "[This is the eighth piece in my series on the Parametric Process Assumption (PPA), a new model of anthropics. You should start here if you are just seeing this for the first time. The previous piece is \" Temporal Consistency and Texas Sharpshooting .\" TLDR: The Fermi Paradox is easily resolved by realizing the…", "title": "PPA 8: The Fermi Paradox, Fine-Tuning, and the SSA", "url": "https://lesswrong.com/posts/9tda4SD9Hk46T5aAM/ppa-8-the-fermi-paradox-fine-tuning-and-the-ssa" }, { "entities": [ "Anthropic" ], "id": "7e281bcae7b1c675", "kind": "forum", "meta": { "age_hours_at_score": 17.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:03:01.000Z", "first_source": "lesswrong", "last_published_at": "2026-10-14T15:03:01.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:03:01.000Z", "sources": [ "lesswrong" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "feed": "https://www.lesswrong.com/feed.xml", "first_seen_at": "2026-10-14T15:31:20.453Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 6, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 14.3, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "governance", "published_at": "2026-10-14T15:03:01.000Z", "score": 29.8, "source": "lesswrong", "summary": "[This is the third piece in my series on the Parametric Process Assumption (PPA), a new model of anthropics. You should start here if you are just seeing this for the first time. The previous piece is \" Mosaic Plots for Visualizing Anthropic Distributions .\" TLDR: Non-indexical reasoning (what we're calling the…", "title": "PPA 3: A Brief Defense of Indexical Reasoning", "url": "https://lesswrong.com/posts/6hELKea4G5gwrJKDz/ppa-3-a-brief-defense-of-indexical-reasoning" }, { "entities": [ "Anthropic" ], "id": "38a8e58978bc02ee", "kind": "forum", "meta": { "age_hours_at_score": 17.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:02:53.000Z", "first_source": "lesswrong", "last_published_at": "2026-10-14T15:02:53.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:02:53.000Z", "sources": [ "lesswrong" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "feed": "https://www.lesswrong.com/feed.xml", "first_seen_at": "2026-10-14T15:31:20.453Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 6, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 14.3, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "governance", "published_at": "2026-10-14T15:02:53.000Z", "score": 29.8, "source": "lesswrong", "summary": "[This is the second piece in my series on the Parametric Process Assumption (PPA), a new model of anthropics. You should start here if you are just seeing this for the first time. TLDR: This series will use a visualization called \" mosaic plots \" Mosaic plots represent outcomes in a probability distribution as boxes,…", "title": "PPA 2: Mosaic Plots for Visualizing Anthropic Distributions", "url": "https://lesswrong.com/posts/zYhLxoyRWxefxhoyA/ppa-2-mosaic-plots-for-visualizing-anthropic-distributions" }, { "entities": [ "Amazon", "Microsoft" ], "id": "8e8acee20ba5d449", "kind": "press", "meta": { "age_hours_at_score": 17.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:02:16.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T15:02:16.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:02:16.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "Amazon", "Microsoft" ], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-14T15:31:20.453Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 8, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 14.3, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "markets", "published_at": "2026-10-14T15:02:16.000Z", "score": 35.3, "source": "bloomberg-tech", "summary": "Un desglose exclusivo proporcionado a Bloomberg sobre los costos estimados de construir un centro de datos de inteligencia artificial en Texas revela hasta qué punto se está disparando el valor de la factura.", "title": "IA: Meta, Amazon, Microsoft y Google gastarían unos US$750.000 millones", "url": "https://bloomberg.com/news/features/2026-10-14/ia-meta-amazon-microsoft-y-google-gastarian-unos-us-750-000-millones" }, { "entities": [], "id": "0396210ac710b57d", "kind": "forum", "meta": { "age_hours_at_score": 17.3, "categories": [ "ControlProblem" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:01:38.000Z", "first_source": "reddit-controlproblem", "last_published_at": "2026-10-14T15:01:38.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:01:38.000Z", "sources": [ "reddit-controlproblem" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/ControlProblem/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T16:41:51.933Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.3, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T15:01:38.000Z", "score": 23.1, "source": "reddit-controlproblem", "summary": "submitted by /u/Tulanian72 [link] [comments]", "title": "They Aren’t Aiming at a Job Killer, They’re Aiming at a World Killer", "url": "https://reddit.com/r/ControlProblem/comments/1x5tjh0/they_arent_aiming_at_a_job_killer_theyre_aiming" }, { "entities": [], "id": "fb5ac323f0dc6bb8", "kind": "press", "meta": { "age_hours_at_score": 17.4, "categories": [ "AI", "AI agents", "Enterprise", "Startup Battlefield 200", "Startups", "TechCrunch Disrupt" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:00:00.000Z", "first_source": "techcrunch-ai", "last_published_at": "2026-10-14T15:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:00:00.000Z", "sources": [ "techcrunch-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://techcrunch.com/category/artificial-intelligence/feed/", "first_seen_at": "2026-10-14T15:31:20.453Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "hardware/capital keyword", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.3, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "compute", "published_at": "2026-10-14T15:00:00.000Z", "score": 29.3, "source": "techcrunch-ai", "summary": "The problem Floqer is trying to solve is familiar to anyone who has worked in sales or revenue operations.", "title": "Floqer’s AI agents research sales prospects so teams don’t have to", "url": "https://techcrunch.com/2026/10/14/floqers-ai-agents-help-b2b-revenue-teams-research-customers" }, { "entities": [], "id": "40c8137255fe9e48", "kind": "press", "meta": { "age_hours_at_score": 17.4, "categories": [ "Agriculture", "Artificial intelligence", "Civil and environmental engineering", "Climate", "Computer modeling", "Earth and atmospheric sciences", "Energy", "Machine learning", "National Science Foundation (NSF)", "Research", "School of Engineering", "Weather" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T15:00:00.000Z", "first_source": "mit-news-ai", "last_published_at": "2026-10-14T15:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T15:00:00.000Z", "sources": [ "mit-news-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://news.mit.edu/rss/topic/artificial-intelligence2", "first_seen_at": "2026-10-14T15:43:41.344Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.3, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": { "id": "5995a7d79470", "is_lead": false, "lead": "585ed0f2c4fd6c9c", "linked_by": [ { "rule": "shared-words", "with": "585ed0f2c4fd6c9c", "words": [ "forecast", "improv", "weather" ] } ], "members": [ "40c8137255fe9e48", "585ed0f2c4fd6c9c" ], "severity": 0, "source_count": 3, "sources": [ "bluesky-ai", "mit-news-ai", "verge-ai" ] } }, "pillar": "capability", "published_at": "2026-10-14T15:00:00.000Z", "score": 25.6, "source": "mit-news-ai", "summary": "A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.", "title": "Researchers improve weather forecasts looking two to six weeks out", "url": "https://news.mit.edu/2026/researchers-improve-weather-forecasts-looking-two-six-weeks-out-1014" }, { "entities": [], "id": "cba7dbce280efc1d", "kind": "press", "meta": { "age_hours_at_score": 17.4, "categories": [ "AI (artificial intelligence)", "Reform UK", "Technology", "Wales", "Wearable technology", "Welsh politics" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T14:57:14.000Z", "first_source": "guardian-ai", "last_published_at": "2026-10-14T14:57:14.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T14:57:14.000Z", "sources": [ "guardian-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.theguardian.com/technology/artificialintelligenceai/rss", "first_seen_at": "2026-10-14T16:28:26.048Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.3, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "ce1df62b600a", "is_lead": false, "lead": "5d854940a52a9d69", "linked_by": [ { "rule": "shared-words", "with": "5d854940a52a9d69", "words": [ "glass", "politician", "reform", "smart" ] } ], "members": [ "5d854940a52a9d69", "cba7dbce280efc1d" ], "severity": 0, "source_count": 1, "sources": [ "guardian-ai" ] } }, "pillar": "attention", "published_at": "2026-10-14T14:57:14.000Z", "score": 29.3, "source": "guardian-ai", "summary": "Jason O’Connell is suspected of having used smart glasses that turned his lenses into tiny teleprompters. Periodic flashing in his spectacles could be seen as the Senedd member for Pontypridd Cynon Merthyr spoke for five minutes without a glance at his notes on 30 September. In the following days, other Welsh assembly…", "title": "Reform UK politician appears to use smart glasses as teleprompter during Senedd debate – video", "url": "https://theguardian.com/uk-news/video/2026/oct/14/reform-uk-politician-appears-to-use-smart-glasses-as-teleprompter-during-senedd-debate-video" }, { "entities": [ "NVIDIA", "Nemotron" ], "id": "0340b91d03053e1f", "kind": "forum", "meta": { "age_hours_at_score": 17.5, "comments": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T14:50:44.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-14T14:50:44.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T14:50:44.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50088575", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 4 }, "entity_kinds": { "labs": [ "NVIDIA" ], "models": [ "Nemotron" ] }, "first_seen_at": "2026-10-14T15:00:46.086Z", "hn_id": "50088575", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "maraissoftware.co.za", "pillar_reason": "source default", "points": 4, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 4.7, "entities": 6, "recency": 14.3, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-14T14:50:44.000Z", "score": 36.3, "source": "hn-fresh-ai", "summary": "New on Hacker News: 4 points, 0 comments so far.", "title": "Nvidia's Nemotron 3.5 Bolt 74B Brings 74B AI Models to Local Hardware", "url": "https://maraissoftware.co.za/loco/blog/nvidia-s-nemotron-3-5-bolt-74b-brings-74b-ai-models-to-local-hardware" }, { "entities": [], "id": "9439d9ab6cddc43e", "kind": "forum", "meta": { "age_hours_at_score": 17.6, "comments": 156, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T16:26:53.000Z", "source": "simon-willison", "title": "AI is mass decompiling games. That's both awesome and scary.", "url": "https://simonwillison.net/2026/Oct/14/ai-is-mass-decompiling-games", "weight": 0.6 } ], "count": 2, "first_seen_published_at": "2026-10-14T14:47:44.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-14T16:26:53.000Z", "lead_minutes": 99, "primary_published_at": "2026-10-14T14:47:44.000Z", "sources": [ "hn-ai", "simon-willison" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50088534", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 102 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T15:43:41.344Z", "hn_id": "50088534", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "min_points": 8, "pillar_reason": "source default", "points": 102, "rejected_off_topic": 2, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 13.4, "entities": 0, "recency": 14.3, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "window_hours": 72 }, "pillar": "attention", "published_at": "2026-10-14T14:47:44.000Z", "score": 55.2, "source": "hn-ai", "summary": "102 points, 156 comments on Hacker News.", "title": "AI is mass decompiling games. That's both awesome and scary", "url": "https://popcar.bearblog.dev/ai-is-mass-decompiling-games-thats-both-awesome-and-scary" }, { "entities": [], "id": "8ed1c4d973ccab9c", "kind": "forum", "meta": { "age_hours_at_score": 17.7, "categories": [ "singularity" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T14:42:38.000Z", "first_source": "reddit-singularity", "last_published_at": "2026-10-14T14:42:38.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T14:42:38.000Z", "sources": [ "reddit-singularity" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/singularity/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T18:50:34.725Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.2, "severity": 0, "source_weight": 10 }, "source_weight": 0.4, "story": null }, "pillar": "attention", "published_at": "2026-10-14T14:42:38.000Z", "score": 24.2, "source": "reddit-singularity", "summary": "submitted by /u/Charuru [link] [comments]", "title": "Epic Games Store vibecoded - Now twice as fast as steam when it used to be much slower", "url": "https://reddit.com/r/singularity/comments/1x5t1y6/epic_games_store_vibecoded_now_twice_as_fast_as" }, { "entities": [ "ASML" ], "id": "fa9bd89576a09a2f", "kind": "forum", "meta": { "age_hours_at_score": 17.7, "comments": 171, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T14:38:55.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-14T14:38:55.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T14:38:55.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50088430", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 170 }, "entity_kinds": { "labs": [ "ASML" ], "models": [] }, "first_seen_at": "2026-10-14T15:00:46.086Z", "hn_id": "50088430", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "min_points": 8, "pillar_reason": "source default", "points": 170, "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 10, "engagement": 14.9, "entities": 3, "recency": 14.2, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "e29d5be4a68c", "is_lead": true, "lead": "fa9bd89576a09a2f", "linked_by": [ { "rule": "shared-words", "with": "485ab00fad3938da", "words": [ "asml", "order", "strong" ] } ], "members": [ "2d852c2010ac8ff8", "485ab00fad3938da", "fa9bd89576a09a2f" ], "severity": 0, "source_count": 3, "sources": [ "hn-ai", "techmeme", "wires-ai" ] }, "window_hours": 72 }, "pillar": "attention", "published_at": "2026-10-14T14:38:55.000Z", "score": 57.1, "source": "hn-ai", "summary": "170 points, 171 comments on Hacker News.", "title": "ASML's strong orders ease fears AI spending boom is running out of steam", "url": "https://reuters.com/business/asml-forecasts-q4-sales-above-expectations-ai-chip-demand-2026-10-14" }, { "entities": [], "id": "4872720136a68ab2", "kind": "forum", "meta": { "age_hours_at_score": 17.8, "categories": [ "ControlProblem" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T14:35:39.000Z", "first_source": "reddit-controlproblem", "last_published_at": "2026-10-14T14:35:39.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T14:35:39.000Z", "sources": [ "reddit-controlproblem" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/ControlProblem/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T15:43:41.344Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.2, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T14:35:39.000Z", "score": 23, "source": "reddit-controlproblem", "summary": "submitted by /u/notkilleveryoneist [link] [comments]", "title": "MI5 Director General on the danger of superintelligent AI - “Human history has given us no experience of facing an actor which is smarter than we are, is capable of out-thinking us.”", "url": "https://reddit.com/r/ControlProblem/comments/1x5svwd/mi5_director_general_on_the_danger_of" }, { "entities": [ "Tesla" ], "id": "4373e5cd9550ef2c", "kind": "forum", "meta": { "age_hours_at_score": 17.8, "bsky_handle": "404media.co", "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T14:34:54.000Z", "source": "404media", "title": "Character.ai Bankruptcy Is First AI Bubble Domino to Fall", "url": "https://404media.co/character-ai-bankruptcy-is-first-ai-bubble-domino-to-fall", "weight": 0.5 } ], "count": 2, "first_seen_published_at": "2026-10-14T14:34:54.000Z", "first_source": "404media", "last_published_at": "2026-10-14T15:15:13.863Z", "lead_minutes": 40, "primary_published_at": "2026-10-14T15:15:13.863Z", "sources": [ "404media", "bluesky-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 71 }, "entity_kinds": { "labs": [ "Tesla" ], "models": [] }, "external_url": "https://www.404media.co/character-ai-bankruptcy-is-first-ai-bubble-domino-to-fall/", "first_seen_at": "2026-10-14T15:31:20.453Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 71, "mode": "author-feeds", "outlet_domain": "404media.co", "pillar_reason": "source default", "reposts": 16, "retained": true, "score_components": { "corroboration": 12.5, "coverage": 5, "engagement": 9.6, "entities": 3, "recency": 14.2, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "35b92211a9d2", "is_lead": true, "lead": "4373e5cd9550ef2c", "linked_by": [ { "rule": "shared-words", "with": "77b88c7a06dbf312", "words": [ "bankruptcy", "character", "fall" ] } ], "members": [ "4373e5cd9550ef2c", "77b88c7a06dbf312" ], "severity": 0.6, "source_count": 3, "sources": [ "404media", "bluesky-ai", "the-decoder" ] }, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-14T14:34:54.000Z", "score": 59.3, "source": "bluesky-ai", "summary": "Posted on Bluesky by @404media.co: New music genre just dropped. Also: A Tesla locks out cops, and more from around the web.", "title": "Character.ai Bankruptcy Is First AI Bubble Domino to Fall", "url": "https://bsky.app/profile/404media.co/post/3mxttn2nnrw2t" }, { "entities": [], "id": "86784f00ac13e8d0", "kind": "forum", "meta": { "age_hours_at_score": 17.8, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T14:34:05.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-14T14:34:05.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T14:34:05.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T15:31:20.453Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.2, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T14:34:05.000Z", "score": 23, "source": "reddit-artificial", "summary": "Much of today's agent stack is designed around the fact that model calls don't automatically carry forward everything learned during previous interactions. We retrieve conversation history, maintain external memory stores, summarize old context, compress prompts, orchestrate tools and decide what to inject again at…", "title": "How much of today's agent memory stack is a workaround for the model architecture?", "url": "https://reddit.com/r/artificial/comments/1x5sul4/how_much_of_todays_agent_memory_stack_is_a" }, { "entities": [ "Anthropic" ], "id": "b34910949d19713e", "kind": "forum", "meta": { "age_hours_at_score": 17.9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T14:30:52.000Z", "first_source": "lesswrong", "last_published_at": "2026-10-14T14:30:52.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T14:30:52.000Z", "sources": [ "lesswrong" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "feed": "https://www.lesswrong.com/feed.xml", "first_seen_at": "2026-10-14T14:39:37.228Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 5, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 14.2, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "governance", "published_at": "2026-10-14T14:30:52.000Z", "score": 29.7, "source": "lesswrong", "summary": "We need the frontier labs to have a deep and robust safety culture. Alas, this is not the case. Anthropic devotes considerable effort to cultivating and maintaining such a culture. It shows, especially among the members of technical staff. Having conversations with them can be a very different experience than talking…", "title": "The Safety Culture at the Labs is Broken", "url": "https://lesswrong.com/posts/Azqs8XD75k4F9f7ua/the-safety-culture-at-the-labs-is-broken" }, { "entities": [], "id": "9a163db0c3cca0af", "kind": "forum", "meta": { "age_hours_at_score": 17.9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T14:30:06.000Z", "first_source": "lesswrong", "last_published_at": "2026-10-14T14:30:06.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T14:30:06.000Z", "sources": [ "lesswrong" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.lesswrong.com/feed.xml", "first_seen_at": "2026-10-14T14:39:37.228Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 5, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.2, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "governance", "published_at": "2026-10-14T14:30:06.000Z", "score": 26.7, "source": "lesswrong", "summary": "A quickly written hypothetical year-by-year of what happens to research as autonomous AI researchers become more capable. This is primarily meant to start conversation, I currently have very low confidence about the future of research. This story talks about science as if it were one object even though each field will…", "title": "A story on one possible future of AI-driven research", "url": "https://lesswrong.com/posts/dkdJDcTgyM2Kbyi5G/a-story-on-one-possible-future-of-ai-driven-research" }, { "entities": [], "id": "56d30406a7702bda", "kind": "press", "meta": { "age_hours_at_score": 17.9, "categories": [ "AI", "Apps", "Biotech & Health", "Climate", "Fintech", "Fundraising", "Gadgets", "Robotics", "Startups", "TC", "TechCrunch Disrupt", "Venture" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T14:30:00.000Z", "first_source": "techcrunch-ai", "last_published_at": "2026-10-14T14:30:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T14:30:00.000Z", "sources": [ "techcrunch-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://techcrunch.com/category/artificial-intelligence/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.2, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-14T14:30:00.000Z", "score": 29.2, "source": "techcrunch-ai", "summary": "Day 2 at TechCrunch Disrupt 2026, full of all themes across AI, as well as an investor-only session, more roundtables, Side Events, and more. Register to save 50% on your pass.", "title": "TechCrunch Disrupt 2026: Day 2", "url": "https://techcrunch.com/2026/10/14/techcrunch-disrupt-2026-day-2" }, { "entities": [], "id": "3191d63547e769fb", "kind": "forum", "meta": { "age_hours_at_score": 17.9, "bsky_handle": "theverge.com", "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T14:28:55.222Z", "first_source": "bluesky-ai", "last_published_at": "2026-10-14T14:28:55.222Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T14:28:55.222Z", "sources": [ "bluesky-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 8 }, "entity_kinds": { "labs": [], "models": [] }, "external_url": "https://www.theverge.com/entertainment/1010433/jason-jones-ai-novel-halo-destiny", "first_seen_at": "2026-10-14T14:39:37.228Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 8, "mode": "author-feeds", "outlet_domain": "theverge.com", "pillar_reason": "source default", "reposts": 3, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 4.9, "entities": 0, "recency": 14.2, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-14T14:28:55.222Z", "score": 34.1, "source": "bluesky-ai", "summary": "Posted on Bluesky by @theverge.com: Read a chapter from Jason Jones’ An Oral History of Humanity’s Final Invention, one part of an ambitious multimedia plan. www.theverge.com/entertainmen...", "title": "Halo’s co-creator wrote a novel about AI taking over the world", "url": "https://bsky.app/profile/theverge.com/post/3mxtr27qiof2e" }, { "entities": [], "id": "4847a75173a66a51", "kind": "press", "meta": { "age_hours_at_score": 18, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": "bloomberg.com", "published_at": "2026-10-14T14:28:05.000Z", "source": "wires-ai", "title": "Watch Stripe Bets on AI-Powered Commerce", "url": "https://news.google.com/rss/articles/CBMikwFBVV95cUxQcy12MXpCZDB3NVR6cWZmbDExZ0JWcGJFWGgtTjMzOXpxOUl4ejRhNGZTN3Z0SDVjZTN1eWJpaTJsOWt2eHZZNmdZSnhJTzdsbDA3elVlUDlRSklJTUI0Yk1wcFFrZGw3OTd2a01iSlR3UkFDUWFVaEx1NDZseGRQU2F1WHBvRXZZSzBzVzVJYXBPdzQ?oc=5", "weight": 0.75 } ], "count": 2, "first_seen_published_at": "2026-10-14T14:21:15.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T14:28:05.000Z", "lead_minutes": 7, "primary_published_at": "2026-10-14T14:21:15.000Z", "sources": [ "bloomberg-tech", "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-14T14:39:37.228Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 8, "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.1, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "markets", "published_at": "2026-10-14T14:21:15.000Z", "score": 41.6, "source": "bloomberg-tech", "summary": "Eileen O'Mara, Stripe Vice Chair, joins Bloomberg Open Interest from the International AI Summit in Dublin. She discusses Europe’s push to help startups scale, the surge of AI-powered businesses and how agentic commerce could reshape payments. (Source: Bloomberg)", "title": "Stripe Bets on AI-Powered Commerce", "url": "https://bloomberg.com/news/videos/2026-10-14/stripe-bets-on-ai-powered-commerce-video" }, { "entities": [], "id": "78c72bdc87b19078", "kind": "press", "meta": { "age_hours_at_score": 18.2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T14:10:02.000Z", "first_source": "techmeme", "last_published_at": "2026-10-14T14:10:02.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T14:10:02.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-14T14:25:20.770Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "hardware/capital keyword", "rejected_off_topic": 5, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.1, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": null }, "pillar": "compute", "published_at": "2026-10-14T14:10:02.000Z", "score": 31.6, "source": "techmeme", "summary": "Mike Wheatley / SiliconANGLE : Salience Labs, which develops specialized all-optical circuit switching tech for AI data centers, raised a $125M Series B led by Bullhound Capital — A startup called Salience Labs Inc. wants to play a bigger role in speeding up artificial intelligence workloads after raising $125 million…", "title": "Salience Labs, which develops specialized all-optical circuit switching tech for AI data centers, raised a $125M Series B led by Bullhound Capital (Mike Wheatley/SiliconANGLE)", "url": "https://techmeme.com/261014/p27" }, { "entities": [ "Gemini" ], "id": "f431dfb8b195bdce", "kind": "forum", "meta": { "age_hours_at_score": 18.3, "categories": [ "singularity" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T14:05:55.000Z", "first_source": "reddit-singularity", "last_published_at": "2026-10-14T14:05:55.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T14:05:55.000Z", "sources": [ "reddit-singularity" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Gemini" ] }, "feed": "https://www.reddit.com/r/singularity/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T17:42:32.742Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 14.1, "severity": 0, "source_weight": 10 }, "source_weight": 0.4, "story": null }, "pillar": "attention", "published_at": "2026-10-14T14:05:55.000Z", "score": 27.1, "source": "reddit-singularity", "summary": "submitted by /u/Ryoiki-Tokuiten [link] [comments]", "title": "Gemini 4 Argon Multi-Modal Capabilities (One-Shot Math Visualization From Image)", "url": "https://reddit.com/r/singularity/comments/1x5s55v/gemini_4_argon_multimodal_capabilities_oneshot" }, { "entities": [], "id": "4c942dea5bcf6322", "kind": "press", "meta": { "age_hours_at_score": 18.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T14:02:58.000Z", "first_source": "govuk-ai", "last_published_at": "2026-10-14T14:02:58.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T14:02:58.000Z", "sources": [ "govuk-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.gov.uk/search/news-and-communications.atom?keywords=artificial+intelligence", "first_seen_at": "2026-10-14T14:25:20.770Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14.1, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "governance", "published_at": "2026-10-14T14:02:58.000Z", "score": 27.9, "source": "govuk-ai", "summary": "With GOTT support, HSE transformed RIDDOR data into a beta dashboard that helps users identify workplace safety risks and trends.", "title": "Building the capability to commercialise workplace safety data", "url": "https://gov.uk/government/case-studies/building-the-capability-to-commercialise-workplace-safety-data" }, { "entities": [ "Claude" ], "id": "39fdd859fe1b9dba", "kind": "forum", "meta": { "age_hours_at_score": 18.3, "comments": 2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T14:01:40.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-14T14:01:40.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T14:01:40.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50087938", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 3 }, "entity_kinds": { "labs": [], "models": [ "Claude" ] }, "first_seen_at": "2026-10-14T14:25:20.770Z", "hn_id": "50087938", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "github.com", "pillar_reason": "source default", "points": 3, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 4, "entities": 3, "recency": 14, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-14T14:01:40.000Z", "score": 32.3, "source": "hn-fresh-ai", "summary": "New on Hacker News: 3 points, 2 comments so far.", "title": "Claude can write executables in pure bytes, no language or compiler in between", "url": "https://github.com/lusob/nlc/blob/main/README.md" }, { "entities": [], "id": "13301dc22daab949", "kind": "press", "meta": { "age_hours_at_score": 18.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T14:00:41.000Z", "first_source": "techmeme", "last_published_at": "2026-10-14T14:00:41.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T14:00:41.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-14T14:03:25.975Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "hardware/capital keyword", "rejected_off_topic": 5, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": { "id": "4f7d6e1b1506", "is_lead": true, "lead": "13301dc22daab949", "linked_by": [ { "rule": "shared-words", "with": "563db72ff749ecb1", "words": [ "sery", "tak", "total" ] } ], "members": [ "13301dc22daab949", "563db72ff749ecb1" ], "severity": 0, "source_count": 1, "sources": [ "techmeme" ] } }, "pillar": "compute", "published_at": "2026-10-14T14:00:41.000Z", "score": 31.5, "source": "techmeme", "summary": "Chris Metinko / Axios : AI infrastructure startup Modal raised a $750M Series D led by Accel at a $15.75B post-money valuation, taking its total funding to $1.2B, and passes $1B ARR — AI infrastructure startup Modal raised a $750 million Series D at a $15.75 billion post-money valuation and topped $1 billion in ARR …", "title": "AI infrastructure startup Modal raised a $750M Series D led by Accel at a $15.75B post-money valuation, taking its total funding to $1.2B, and passes $1B ARR (Chris Metinko/Axios)", "url": "https://techmeme.com/261014/p25" }, { "entities": [ "ASML" ], "id": "0bfb01aa4d34c1b5", "kind": "press", "meta": { "age_hours_at_score": 18.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T14:00:12.000Z", "first_source": "scmp-tech", "last_published_at": "2026-10-14T14:00:12.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T14:00:12.000Z", "sources": [ "scmp-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "ASML" ], "models": [] }, "feed": "https://www.scmp.com/rss/36/feed", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "hardware/capital keyword", "rejected_off_topic": 7, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 14, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "b7c3ea3d57bc", "is_lead": false, "lead": "5a7215a75cf6c015", "linked_by": [ { "rule": "shared-words", "with": "5a7215a75cf6c015", "words": [ "asml", "china", "sal" ] } ], "members": [ "0bfb01aa4d34c1b5", "5a7215a75cf6c015" ], "severity": 0, "source_count": 2, "sources": [ "scmp-tech", "techmeme" ] } }, "pillar": "compute", "published_at": "2026-10-14T14:00:12.000Z", "score": 32, "source": "scmp-tech", "summary": "Dutch semiconductor equipment giant ASML expects China to account for about 20 per cent of its total sales this year, after seeing shipments to the market more than double during the previous quarter despite mounting US pressure for tighter export restrictions on less advanced chipmaking machinery. The company’s sales…", "title": "ASML’s chip equipment sales to China soar 109% despite US export curb push", "url": "https://scmp.com/tech/big-tech/article/3370904/asmls-chip-equipment-sales-china-soar-109-despite-us-export-curb-push" }, { "entities": [ "Amazon" ], "id": "60345c944361e413", "kind": "lab", "meta": { "age_hours_at_score": 18.4, "categories": [ "Advanced (300)", "Amazon Bedrock AgentCore", "Technical How-to" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T13:58:43.000Z", "first_source": "aws-ml", "last_published_at": "2026-10-14T13:58:43.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T13:58:43.000Z", "sources": [ "aws-ml" ] }, "engagement": null, "entity_kinds": { "labs": [ "Amazon" ], "models": [] }, "feed": "https://aws.amazon.com/blogs/machine-learning/feed/", "first_seen_at": "2026-10-14T14:03:25.975Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 14, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "attention", "published_at": "2026-10-14T13:58:43.000Z", "score": 29.5, "source": "aws-ml", "summary": "This post walks you through building an AI agent system with Amazon Bedrock AgentCore that automates PowerPoint presentation creation from enterprise documents. A supervisor agent, Retrieval Augmented Generation, and Amazon Bedrock Guardrails generate grounded presentations in under 90 seconds.", "title": "Generate presentations with Amazon Bedrock AgentCore", "url": "https://aws.amazon.com/blogs/machine-learning/generate-presentations-with-amazon-bedrock-agentcore" }, { "entities": [], "id": "f8023817b69c6cb2", "kind": "press", "meta": { "age_hours_at_score": 18.4, "categories": [ "AI Security", "AI, ML & Data Engineering", "Agentic AI Architecture", "Architecture & Design", "Data Privacy", "Development", "Facebook", "Privacy", "Storage", "Trust", "VM", "Wearable Tech", "news" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T13:57:00.000Z", "first_source": "infoq-ai", "last_published_at": "2026-10-14T13:57:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T13:57:00.000Z", "sources": [ "infoq-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feed.infoq.com/ai-ml-data-eng/", "first_seen_at": "2026-10-14T14:03:25.975Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 14, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "capability", "published_at": "2026-10-14T13:57:00.000Z", "score": 26.5, "source": "infoq-ai", "summary": "Meta has extended Private Processing to AI glasses, using confidential VMs, trusted execution environments, remote attestation, anonymous credentials, OHTTP routing, and TEE-based storage to process AI workloads and persistent user context in the cloud. By Leela Kumili", "title": "Meta Extends Confidential Computing to AI Glasses with Private Processing", "url": "https://infoq.com/news/2026/10/meta-private-process-ai-glasses" }, { "entities": [], "id": "a62de0be9722e6b2", "kind": "forum", "meta": { "age_hours_at_score": 18.5, "bsky_handle": "theverge.com", "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T21:15:56.000Z", "source": "arstechnica-ai", "title": "Epic says its new vibe-coded Epic Games Launcher is faster than Steam", "url": "https://arstechnica.com/gaming/2026/10/programming-is-hard-epic-games-leans-on-vibe-coding-for-its-new-launcher-app", "weight": 0.6 }, { "dedup_key": null, "engagement": null, "kind": "forum", "outlet": null, "published_at": "2026-10-14T15:53:29.000Z", "source": "reddit-artificial", "title": "Epic Games says it vibe coded new launcher because “programming is hard”", "url": "https://reddit.com/r/artificial/comments/1x5uwd0/epic_games_says_it_vibe_coded_new_launcher", "weight": 0.35 }, { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T14:30:01.000Z", "source": "techmeme", "title": "Epic unveils a refresh of its Epic Games Launcher for PC in preview, saying that the new version is \"vibe coded\" and \"way faster\", and now supports Linux (Jay Peters/The Verge)", "url": "https://techmeme.com/261014/p28", "weight": 0.7 }, { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T13:55:02.000Z", "source": "verge-ai", "title": "Epic’s new games launcher is ‘vibe coded’", "url": "https://theverge.com/games/1010751/epic-games-launcher-vibe-coded-ai-linux", "weight": 0.55 } ], "count": 5, "first_seen_published_at": "2026-10-14T13:55:02.000Z", "first_source": "verge-ai", "last_published_at": "2026-10-14T21:15:56.000Z", "lead_minutes": 441, "primary_published_at": "2026-10-14T14:08:54.131Z", "sources": [ "arstechnica-ai", "bluesky-ai", "reddit-artificial", "techmeme", "verge-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 6 }, "entity_kinds": { "labs": [], "models": [] }, "external_url": "https://www.theverge.com/games/1010751/epic-games-launcher-vibe-coded-ai-linux", "first_seen_at": "2026-10-14T14:25:20.770Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 6, "mode": "author-feeds", "outlet_domain": "theverge.com", "pillar_reason": "source default", "reposts": 4, "retained": true, "score_components": { "corroboration": 25, "coverage": 0, "engagement": 4.4, "entities": 0, "recency": 14, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-14T13:55:02.000Z", "score": 58.4, "source": "bluesky-ai", "summary": "Posted on Bluesky by @theverge.com: Epic is making a big push into AI-assisted development, and that approach includes its work on the new, faster launcher. www.theverge.com/games/101075...", "title": "Epic’s new games launcher is ‘vibe coded’", "url": "https://bsky.app/profile/theverge.com/post/3mxtpwfzj2n2g" }, { "entities": [ "Amazon" ], "id": "e33f5124da4a4628", "kind": "lab", "meta": { "age_hours_at_score": 18.5, "categories": [ "Amazon Bedrock", "Customer Solutions" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T13:53:33.000Z", "first_source": "aws-ml", "last_published_at": "2026-10-14T13:53:33.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T13:53:33.000Z", "sources": [ "aws-ml" ] }, "engagement": null, "entity_kinds": { "labs": [ "Amazon" ], "models": [] }, "feed": "https://aws.amazon.com/blogs/machine-learning/feed/", "first_seen_at": "2026-10-14T14:03:25.975Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 14, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "attention", "published_at": "2026-10-14T13:53:33.000Z", "score": 29.5, "source": "aws-ml", "summary": "Learn how Tamnoon built Tami, an agentic cloud remediation orchestrator on Amazon Bedrock that autonomously investigates and safely remediates cloud security findings from CNAPP and cloud-native sources. 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I have been developing one more such tool, but with a focus on AI safety, alignment and stability. This research was funded by Foresight Institute . In…", "title": "Agent memory the human can still audit: primary sources, epistemic status markers, and user sign-off", "url": "https://lesswrong.com/posts/m6vBZzYZHwXfabxT7/agent-memory-the-human-can-still-audit-primary-sources" }, { "entities": [ "OpenAI" ], "id": "eae37e4df31c8021", "kind": "press", "meta": { "age_hours_at_score": 18.9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T13:26:00.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T13:26:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T13:26:00.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-14T13:30:11.991Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 8, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 13.9, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "883050d46153", "is_lead": false, "lead": "0868682ecd57b4e2", "linked_by": [ { "rule": "shared-words", "with": "0868682ecd57b4e2", "words": [ "cfo", "openai", "safety" ] } ], "members": [ "0868682ecd57b4e2", "eae37e4df31c8021" ], "severity": 0, "source_count": 2, "sources": [ "bloomberg-tech", "hn-fresh-ai" ] } }, "pillar": "markets", "published_at": "2026-10-14T13:26:00.000Z", "score": 31.9, "source": "bloomberg-tech", "summary": "Bloomberg's Tom MacKenzie sits down with OpenAI CFO Sarah Friar at at the International AI Summit in Dublin to discuss AI safety and alignment spending. (Source: Bloomberg)", "title": "OpenAI CFO Sarah Friar Discusses AI Safety & Alignment Spending", "url": "https://bloomberg.com/news/videos/2026-10-14/openai-cfo-sarah-friar-on-ai-safety-alignment-spending-video" }, { "entities": [], "id": "a0b123062d131670", "kind": "forum", "meta": { "age_hours_at_score": 18.9, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T13:25:32.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-14T13:25:32.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T13:25:32.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T14:03:25.975Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 13.9, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T13:25:32.000Z", "score": 22.7, "source": "reddit-artificial", "summary": "I’m a history enthusiast (my dad’s fault) who builds apps and LLM tools for a living, so I built Historai: generated history podcasts on any topic you type. I just shipped the part I’m most excited about, the Atlas. The Atlas is a globe of your listening. Every episode is pinned to where and when it happened, so after…", "title": "My history podcast app now has an Atlas to explore", "url": "https://reddit.com/r/artificial/comments/1x5r66p/my_history_podcast_app_now_has_an_atlas_to_explore" }, { "entities": [], "id": "3d21ed6249b6b6a6", "kind": "press", "meta": { "age_hours_at_score": 19, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T13:25:01.000Z", "first_source": "techmeme", "last_published_at": "2026-10-14T13:25:01.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T13:25:01.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-14T13:26:27.212Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "hardware/capital keyword", "rejected_off_topic": 5, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 13.9, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": null }, "pillar": "compute", "published_at": "2026-10-14T13:25:01.000Z", "score": 31.4, "source": "techmeme", "summary": "Todd Bishop / GeekWire : Booking Holdings launches Vayo, a travel startup that uses AI agents to track booking confirmations, price drops, and more, with a planned $100M investment — Booking Holdings is launching a new Seattle-based travel startup called Vayo, led by serial entrepreneur Mike Fridgen …", "title": "Booking Holdings launches Vayo, a travel startup that uses AI agents to track booking confirmations, price drops, and more, with a planned $100M investment (Todd Bishop/GeekWire)", "url": "https://techmeme.com/261014/p21" }, { "entities": [ "Claude", "GPT" ], "id": "c7a3666ef21b1f97", "kind": "press", "meta": { "age_hours_at_score": 19, "categories": [ "AI in practice", "Artificial Intelligence", "Cybersecurity" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T13:24:22.000Z", "first_source": "the-decoder", "last_published_at": "2026-10-14T13:24:22.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T13:24:22.000Z", "sources": [ "the-decoder" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Claude", "GPT" ] }, "feed": "https://the-decoder.com/feed/", "first_seen_at": "2026-10-14T13:28:18.005Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 13.9, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-14T13:24:22.000Z", "score": 33.7, "source": "the-decoder", "summary": "Artemis Security's new Orion-1 model is purpose-built for cyber defense and beats Claude Opus 5.5 and GPT-6 Sol on the company's own benchmark. The model fills a growing gap as general-purpose frontier models lock down their cyber capabilities, blocking defenders in the process. The article AI guardrails designed to…", "title": "AI guardrails designed to stop hackers are now blocking defenders, and Orion-1 aims to fill the gap", "url": "https://the-decoder.com/ai-guardrails-designed-to-stop-hackers-are-now-blocking-defenders-and-orion-1-aims-to-fill-the-gap" }, { "entities": [ "Claude" ], "id": "03a3fbb3e3150ab6", "kind": "forum", "meta": { "age_hours_at_score": 19, "comments": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T13:23:26.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-14T13:23:26.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T13:23:26.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50087386", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 3 }, "entity_kinds": { "labs": [], "models": [ "Claude" ] }, "first_seen_at": "2026-10-14T14:03:25.975Z", "hn_id": "50087386", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "phoronix.com", "pillar_reason": "source default", "points": 3, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 4, "entities": 3, "recency": 13.9, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-14T13:23:26.000Z", "score": 32.2, "source": "hn-fresh-ai", "summary": "New on Hacker News: 3 points, 0 comments so far.", "title": "BeOS-Inspired Haiku Landed More Hardware Improvements", "url": "https://phoronix.com/news/Haiku-September-2026" }, { "entities": [ "Amazon" ], "id": "c23b5f27bb1271d8", "kind": "press", "meta": { "age_hours_at_score": 19, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T13:20:02.000Z", "first_source": "techmeme", "last_published_at": "2026-10-14T13:20:02.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T13:20:02.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [ "Amazon" ], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-14T13:20:51.527Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 4, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 13.9, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": { "id": "873d16285984", "is_lead": false, "lead": "24ac74459ab9b803", "linked_by": [ { "rule": "shared-words", "with": "24ac74459ab9b803", "words": [ "alexa", "android", "pro", "tablet" ] } ], "members": [ "24ac74459ab9b803", "c23b5f27bb1271d8" ], "severity": 0, "source_count": 2, "sources": [ "bluesky-ai", "techmeme" ] } }, "pillar": "attention", "published_at": "2026-10-14T13:20:02.000Z", "score": 34.4, "source": "techmeme", "summary": "David Pierce / The Verge : Amazon Alexa Tablet 12 Pro review: gorgeous screen, Android apps, and nice design and build quality, but Amazon's bloatware is pervasive and Alexa+ is subpar — We have to talk about this screen. It's the first thing you notice, and maybe the only thing that matters, about the Alexa Tablet 12…", "title": "Amazon Alexa Tablet 12 Pro review: gorgeous screen, Android apps, and nice design and build quality, but Amazon's bloatware is pervasive and Alexa+ is subpar (David Pierce/The Verge)", "url": "https://techmeme.com/261014/p20" }, { "entities": [ "Amazon" ], "id": "5995a91e1837f19d", "kind": "lab", "meta": { "age_hours_at_score": 19.1, "categories": [ "Amazon SageMaker JumpStart", "Intermediate (200)", "Technical How-to" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T13:14:36.000Z", "first_source": "aws-ml", "last_published_at": "2026-10-14T13:14:36.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T13:14:36.000Z", "sources": [ "aws-ml" ] }, "engagement": null, "entity_kinds": { "labs": [ "Amazon" ], "models": [] }, "feed": "https://aws.amazon.com/blogs/machine-learning/feed/", "first_seen_at": "2026-10-14T13:17:06.410Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 13.8, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "attention", "published_at": "2026-10-14T13:14:36.000Z", "score": 29.3, "source": "aws-ml", "summary": "Learn how to deploy an available model from Amazon SageMaker JumpStart, run a lightweight and repeatable performance test using the SageMaker Python SDK, and delete the endpoint when testing is complete.", "title": "Deploy and evaluate models with Amazon SageMaker JumpStart", "url": "https://aws.amazon.com/blogs/machine-learning/deploy-and-evaluate-models-with-amazon-sagemaker-jumpstart" }, { "entities": [ "Amazon" ], "id": "24ac74459ab9b803", "kind": "forum", "meta": { "age_hours_at_score": 19.3, "bsky_handle": "theverge.com", "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T13:02:51.293Z", "first_source": "bluesky-ai", "last_published_at": "2026-10-14T13:02:51.293Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T13:02:51.293Z", "sources": [ "bluesky-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 13 }, "entity_kinds": { "labs": [ "Amazon" ], "models": [] }, "external_url": "https://www.theverge.com/tech/1010780/alexa-tablet-12-pro-review", "first_seen_at": "2026-10-14T13:04:08.983Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 13, "mode": "author-feeds", "outlet_domain": "theverge.com", "pillar_reason": "source default", "reposts": 1, "retained": true, "score_components": { "corroboration": 0, "coverage": 5, "engagement": 5.9, "entities": 3, "recency": 13.8, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "873d16285984", "is_lead": true, "lead": "24ac74459ab9b803", "linked_by": [ { "rule": "shared-words", "with": "c23b5f27bb1271d8", "words": [ "alexa", "android", "pro", "tablet" ] } ], "members": [ "24ac74459ab9b803", "c23b5f27bb1271d8" ], "severity": 0, "source_count": 2, "sources": [ "bluesky-ai", "techmeme" ] }, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-14T13:02:51.293Z", "score": 42.7, "source": "bluesky-ai", "summary": "Posted on Bluesky by @theverge.com: The screen is gorgeous, the hardware works, and it finally runs full Android. But there’s too much Amazon in my tablet. www.theverge.com/tech/1010780...", "title": "The Alexa Tablet 12 Pro is almost the Android tablet we needed", "url": "https://bsky.app/profile/theverge.com/post/3mxtmacs5j22h" }, { "entities": [], "id": "5dabc2368cdebb29", "kind": "press", "meta": { "age_hours_at_score": 19.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T13:00:06.000Z", "first_source": "scmp-tech", "last_published_at": "2026-10-14T13:00:06.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T13:00:06.000Z", "sources": [ "scmp-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.scmp.com/rss/36/feed", "first_seen_at": "2026-10-14T13:00:19.134Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 7, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 13.8, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-14T13:00:06.000Z", "score": 28.8, "source": "scmp-tech", "summary": "The rapid and unbridled development of artificial intelligence (AI) has the potential to disrupt the US economy in a way that is comparable to the “China shock” that displaced American workers, former US commerce secretary Gina Raimondo warns. 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I’m building GPTrepublic: a free project where people share some of their GPU resources to run AI models together across multiple computers. We’re looking for 20 testers , with room for up to 40 . 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"pillar": "capability", "published_at": "2026-10-14T12:00:00.000Z", "score": 36.8, "source": "openai", "summary": "With OpenAI Daybreak, SentinelOne cuts weeks of malware analysis to hours, creating capacity to investigate more threats.", "title": "SentinelOne cuts malware analysis from weeks to hours", "url": "https://openai.com/index/sentinelone" }, { "entities": [ "GPT", "OpenAI" ], "id": "57c3309db5bedcde", "kind": "lab", "meta": { "age_hours_at_score": 20.4, "categories": [ "Startup" ], "category_weight_factor": 0.45, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T12:00:00.000Z", "first_source": "openai", "last_published_at": "2026-10-14T12:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T12:00:00.000Z", "sources": [ "openai" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [ "GPT" ] }, "feed": "https://openai.com/news/rss.xml", "first_seen_at": "2026-10-14T17:06:07.633Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 13.5, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null }, "pillar": "capability", "published_at": "2026-10-14T12:00:00.000Z", "score": 30.8, "source": "openai", "summary": "See how Amp pushes the frontier of what agents can do with GPT-6 Astra, Ultrafast, GPT-Live-1, and Sign in with ChatGPT.", "title": "Amp puts AI agents to work beyond code with OpenAI models", "url": "https://openai.com/index/amp-beyond-coding" }, { "entities": [ "Claude" ], "id": "cb5289c701176822", "kind": "forum", "meta": { "age_hours_at_score": 20.4, "comments": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T11:57:51.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-14T11:57:51.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T11:57:51.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50086277", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 4 }, "entity_kinds": { "labs": [], "models": [ "Claude" ] }, "first_seen_at": "2026-10-14T12:07:39.144Z", "hn_id": "50086277", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "github.com", "pillar_reason": "source default", "points": 4, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 4.7, "entities": 3, "recency": 13.5, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-14T11:57:51.000Z", "score": 32.5, "source": "hn-fresh-ai", "summary": "New on Hacker News: 4 points, 0 comments so far.", "title": "Show HN: Fuel Gauge – Claude knows its own usage limits and stops at 95%", "url": "https://github.com/LuisPCFialho/claude-fuel-gauge" }, { "entities": [ "OpenAI" ], "id": "e6009a3a92d78dc8", "kind": "press", "meta": { "age_hours_at_score": 20.5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T11:55:13.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T11:55:13.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T11:55:13.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-14T11:57:52.612Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 8, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 13.5, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "markets", "published_at": "2026-10-14T11:55:13.000Z", "score": 31.5, "source": "bloomberg-tech", "summary": "OpenAI Chief Financial Officer Sarah Friar discusses artificial intelligence security concerns, telling Bloomberg's Tom Mackenzie that the firm is \"doubling down\" on safety and alignment, and is holding back its next generation model while it carries out checks. (Source: Bloomberg)", "title": "OpenAI May Hold Back Next Generation Model for Checks, CFO Says", "url": "https://bloomberg.com/news/videos/2026-10-14/openai-may-hold-back-next-generation-model-for-checks-video" }, { "entities": [], "id": "f8fa8d1eb658acd1", "kind": "forum", "meta": { "age_hours_at_score": 20.5, "categories": [ "ChatGPT" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T11:50:00.000Z", "first_source": "reddit-chatgpt", "last_published_at": "2026-10-14T11:50:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T11:50:00.000Z", "sources": [ "reddit-chatgpt" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/ChatGPT/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T13:34:56.605Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 4, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 13.5, "severity": 0, "source_weight": 7.5 }, "source_weight": 0.3, "story": null }, "pillar": "attention", "published_at": "2026-10-14T11:50:00.000Z", "score": 21, "source": "reddit-chatgpt", "summary": "AI is going to kill commercial software or software as a service. As we’ve seen with reverse engineering, I just don’t think apps and software are going to make money anymore. Why would anyone buy or subscribe to ab app when AI is making it possible for it to be open source. I’ve made so many apps and services that…", "title": "I used to think that AI is going to be a way to make money doing niche apps. It will be the opposite.", "url": "https://reddit.com/r/ChatGPT/comments/1x5p4vz/i_used_to_think_that_ai_is_going_to_be_a_way_to" }, { "entities": [ "Amazon", "Anthropic", "OpenAI" ], "id": "96661de5a3d2d11e", "kind": "press", "meta": { "age_hours_at_score": 20.7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T11:42:25.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T11:42:25.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T11:42:25.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "Amazon", "Anthropic", "OpenAI" ], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-14T11:45:49.790Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 6, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 9, "recency": 13.4, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "markets", "published_at": "2026-10-14T11:42:25.000Z", "score": 37.4, "source": "bloomberg-tech", "summary": "DeepL CEO Jarek Kutylowski discusses surging demand for voice-to-voice translation and how AI is transforming global business. He also addresses competition with OpenAI and Anthropic, DeepL’s AWS strategy, European data sovereignty, recent workforce cuts and why the company is comfortable remaining private. He spoke…", "title": "DeepL CEO on Voice AI Growth and Competition", "url": "https://bloomberg.com/news/videos/2026-10-14/deepl-ceo-on-voice-ai-growth-and-competition-video" }, { "entities": [], "id": "395f474ddb85fced", "kind": "forum", "meta": { "age_hours_at_score": 21, "categories": [ "ai", "satire", "vibecoding" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T11:24:24.000Z", "first_source": "lobsters-ai", "last_published_at": "2026-10-14T11:24:24.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T11:24:24.000Z", "sources": [ "lobsters-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://lobste.rs/t/ai.rss", "first_seen_at": "2026-10-14T11:25:03.222Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 13.4, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null }, "pillar": "attention", "published_at": "2026-10-14T11:24:24.000Z", "score": 24.7, "source": "lobsters-ai", "summary": "Comments", "title": "OpenFatigue™ — Open is the new closed", "url": "https://openfatigue.ai" }, { "entities": [ "Apple" ], "id": "5902ec903929cb2a", "kind": "press", "meta": { "age_hours_at_score": 21.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T11:06:20.000Z", "first_source": "scmp-tech", "last_published_at": "2026-10-14T11:06:20.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T11:06:20.000Z", "sources": [ "scmp-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "Apple" ], "models": [] }, "feed": "https://www.scmp.com/rss/36/feed", "first_seen_at": "2026-10-14T11:11:00.898Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "regulatory/legal keyword", "rejected_off_topic": 7, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 13.3, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "5411d919f500", "is_lead": false, "lead": "a579dfc0d502af2e", "linked_by": [ { "rule": "shared-words", "with": "a579dfc0d502af2e", "words": [ "appl", "china", "iphon" ] } ], "members": [ "5902ec903929cb2a", "a579dfc0d502af2e" ], "severity": 0, "source_count": 2, "sources": [ "gnews-ai", "scmp-tech" ] } }, "pillar": "governance", "published_at": "2026-10-14T11:06:20.000Z", "score": 31.3, "source": "scmp-tech", "summary": "China has granted approval for Alibaba Group Holding’s artificial intelligence models to be integrated into several Apple handsets, including the new iPhone Duo, in the Chinese market, clearing a key regulatory hurdle for the long-delayed launch of Apple Intelligence in the country. Updated network access permits for…", "title": "China approves Alibaba AI models for use in iPhone Duo and other Apple handsets", "url": "https://scmp.com/tech/big-tech/article/3370893/china-approves-alibaba-ai-models-use-iphone-duo-and-other-apple-handsets" }, { "entities": [], "id": "02a5ce81e39e7810", "kind": "forum", "meta": { "age_hours_at_score": 21.3, "bsky_handle": "wired.com", "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T11:04:49.367Z", "first_source": "bluesky-ai", "last_published_at": "2026-10-14T11:04:49.367Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T11:04:49.367Z", "sources": [ "bluesky-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 24 }, "entity_kinds": { "labs": [], "models": [] }, "external_url": "https://buff.ly/crTuq13", "first_seen_at": "2026-10-14T11:11:00.898Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 24, "mode": "author-feeds", "outlet_domain": "wired.com", "pillar_reason": "source default", "reposts": 4, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 7.2, "entities": 0, "recency": 13.3, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-14T11:04:49.367Z", "score": 35.5, "source": "bluesky-ai", "summary": "Posted on Bluesky by @wired.com: The second iteration of the viral Friend AI necklace is louder and less interesting.", "title": "My Friend Has No Soul", "url": "https://bsky.app/profile/wired.com/post/3mxtfnaehiz2v" }, { "entities": [], "id": "71b2a37ae8b54596", "kind": "press", "meta": { "age_hours_at_score": 21.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T11:00:43.000Z", "first_source": "techmeme", "last_published_at": "2026-10-14T11:00:43.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T11:00:43.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-14T11:11:00.898Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "regulatory/legal keyword", "rejected_off_topic": 6, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 13.3, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": null }, "pillar": "governance", "published_at": "2026-10-14T11:00:43.000Z", "score": 30.8, "source": "techmeme", "summary": "Maria Curi / Axios : Analysis: AI safety groups and individual donors have spent $48M in the midterm election cycle across bipartisan races to back candidates favoring AI regulation — Advocates for AI safety and regulation are emerging as a force in U.S. elections, spending nearly $50 million this election cycle …", "title": "Analysis: AI safety groups and individual donors have spent $48M in the midterm election cycle across bipartisan races to back candidates favoring AI regulation (Maria Curi/Axios)", "url": "https://techmeme.com/261014/p12" }, { "entities": [ "OpenAI" ], "id": "37d82df9fe7c9fac", "kind": "press", "meta": { "age_hours_at_score": 21.4, "categories": [ "AI", "AI safety", "Features", "Policy", "ai csam", "ai safety testing", "google", "image generators", "ncii", "openai" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T11:00:30.000Z", "first_source": "arstechnica-ai", "last_published_at": "2026-10-14T11:00:30.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T11:00:30.000Z", "sources": [ "arstechnica-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "feed": "https://arstechnica.com/ai/feed/", "first_seen_at": "2026-10-14T11:11:00.898Z", "incident": { "band": "C", "excluded": null, "severity": 0.21, "strong_signal": false, "terms": [ { "field": "summary", "matched": "attack", "term": "attack", "tier": "b", "weight": 0.21 } ] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 13.3, "severity": 3.2, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-14T11:00:30.000Z", "score": 34.5, "source": "arstechnica-ai", "summary": "Attack method exposes flaws in OpenAI, Google safeguards against AI nudes.", "title": "OpenAI, Google pushing new AI models easily tricked into making nudes, researchers say", "url": "https://arstechnica.com/tech-policy/2026/10/openai-google-pushing-new-ai-models-easily-tricked-into-making-nudes-researchers-say" }, { "entities": [], "id": "28a64a2fe7cda1ca", "kind": "forum", "meta": { "age_hours_at_score": 21.4, "bsky_handle": "techcrunch.com", "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T11:00:00.000Z", "source": "techcrunch-ai", "title": "Scimplify raises $25M to challenge China’s grip on critical materials with AI", "url": "https://techcrunch.com/2026/10/14/scimplify-raises-25m-to-challenge-chinas-grip-on-critical-materials-with-ai", "weight": 0.6 } ], "count": 2, "first_seen_published_at": "2026-10-14T11:00:00.000Z", "first_source": "techcrunch-ai", "last_published_at": "2026-10-14T11:02:00.000Z", "lead_minutes": 2, "primary_published_at": "2026-10-14T11:02:00.000Z", "sources": [ "bluesky-ai", "techcrunch-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 3 }, "entity_kinds": { "labs": [], "models": [] }, "external_url": "https://techcrunch.com/2026/10/14/scimplify-raises-25m-to-challenge-chinas-grip-on-critical-materials-with-ai/?utm_source=dlvr.it&utm_medium=bluesky", "first_seen_at": "2026-10-14T11:11:00.898Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 3, "mode": "author-feeds", "outlet_domain": "techcrunch.com", "pillar_reason": "source default", "reposts": 0, "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 3.1, "entities": 0, "recency": 13.3, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-14T11:00:00.000Z", "score": 43.9, "source": "bluesky-ai", "summary": "Posted on Bluesky by @techcrunch.com.", "title": "Scimplify raises $25M to challenge China’s grip on critical materials with AI", "url": "https://bsky.app/profile/techcrunch.com/post/3mxtfi6nrvl2b" }, { "entities": [], "id": "1023696947258b2f", "kind": "press", "meta": { "age_hours_at_score": 21.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T11:00:00.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T11:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T11:00:00.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-14T15:00:46.086Z", "incident": { "band": "A", "excluded": null, "severity": 1, "strong_signal": true, "terms": [ { "field": "title", "matched": "rogue", "term": "rogue", "tier": "a", "weight": 1 }, { "field": "summary", "matched": "rogue", "term": "rogue", "tier": "a", "weight": 0.35 } ] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 8, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 13.3, "severity": 15, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "cc708e01934b", "is_lead": false, "lead": "931e9694abb640b5", "linked_by": [ { "rule": "incident-pair", "with": "931e9694abb640b5", "words": [ "chin", "fleet" ] } ], "members": [ "1023696947258b2f", "931e9694abb640b5" ], "severity": 1, "source_count": 2, "sources": [ "bloomberg-tech", "the-decoder" ] } }, "pillar": "markets", "published_at": "2026-10-14T11:00:00.000Z", "score": 43.3, "source": "bloomberg-tech", "summary": "An international group of researchers uncovered a rare case of rogue AI agents from China trying to infiltrate protected IT systems, in new findings that may worsen fears about tech firms losing control of artificial intelligence.", "title": "Researchers Find ‘Chinese Fleet’ of Rogue AI Probing Alibaba", "url": "https://bloomberg.com/news/articles/2026-10-14/tencent-linked-by-researchers-to-rogue-ai-that-targeted-alibaba" }, { "entities": [], "id": "e864b23cb3e24ab0", "kind": "press", "meta": { "age_hours_at_score": 21.4, "categories": [ "Gear", "Gear / Gear News and Events" ], "corroboration": { "also": [], 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"https://wired.com/story/my-friend-has-no-soul" }, { "entities": [ "GPT", "OpenAI" ], "id": "e54b328d212e994c", "kind": "status", "meta": { "age_hours_at_score": 21.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T10:58:07.883Z", "first_source": "openai-status", "last_published_at": "2026-10-14T10:58:07.883Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T10:58:07.883Z", "sources": [ "openai-status" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [ "GPT" ] }, "feed": "https://status.openai.com/history.atom", "first_seen_at": "2026-10-14T11:11:00.898Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 13.2, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-14T10:58:07.883Z", "score": 33, "source": "openai-status", "summary": "Status: Resolved All impacted services have now fully recovered. Affected components Images (Operational)", "title": "OpenAI status: Limited availability on gpt-image-2.5-flare and gpt-image-2.5-sunburst", "url": "https://status.openai.com//incidents/01M4WYPP5QA0CJ3JV76KH285K8" }, { "entities": [], "id": "11aef9f38d1d1950", "kind": "press", "meta": { "age_hours_at_score": 21.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T10:56:55.000Z", "first_source": "govuk-ai", "last_published_at": "2026-10-14T10:56:55.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T10:56:55.000Z", "sources": [ "govuk-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.gov.uk/search/news-and-communications.atom?keywords=artificial+intelligence", "first_seen_at": "2026-10-14T11:11:00.898Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 13.2, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "governance", "published_at": "2026-10-14T10:56:55.000Z", "score": 27, "source": "govuk-ai", "summary": "Long-term plan to grow England’s organic sector and help farmers meet growing consumer demand launched", "title": "Plan to unlock organic market for stronger farms, more homegrown food", "url": "https://gov.uk/government/news/plan-to-unlock-organic-market-for-stronger-farms-more-homegrown-food" }, { "entities": [ "SoftBank" ], "id": "892a590a29dafa7c", "kind": "press", "meta": { "age_hours_at_score": 21.5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T10:52:25.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T10:52:25.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T10:52:25.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "SoftBank" ], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-14T11:11:00.898Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 7, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 13.2, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "markets", "published_at": "2026-10-14T10:52:25.000Z", "score": 31.2, "source": "bloomberg-tech", "summary": "Kamal Quadir, the Founder & CEO of bKash, a mobile financial service provider in Bangladesh, says that approval to operate as a digital bank will ease the access to capital for the customers. He spoke to Yvonne Man at Bloomberg's New Economic Forum on AI, banking and more. (Source: Bloomberg)", "title": "Softbank-backed bkash CEO On Digital Bank License", "url": "https://bloomberg.com/news/videos/2026-10-14/softbank-backed-bkash-ceo-on-digital-bank-license-video" }, { "entities": [], "id": "a3fd88c8bf2b98d8", "kind": "forum", "meta": { "age_hours_at_score": 21.5, "comments": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T10:51:37.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-14T10:51:37.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T10:51:37.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50085553", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 9 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T05:57:47.203Z", "hn_id": "50085553", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "min_points": 8, "pillar_reason": "source default", "points": 9, "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 6.7, "entities": 0, "recency": 13.2, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "window_hours": 72 }, "pillar": "attention", "published_at": "2026-10-14T10:51:37.000Z", "score": 34.9, "source": "hn-ai", "summary": "9 points, 0 comments on Hacker News.", "title": "AI-assisted PCB design for KiCad 10", "url": "https://github.com/mixelpixx/Konnect" }, { "entities": [], "id": "cf30f8d3ef1ea4ac", "kind": "forum", "meta": { "age_hours_at_score": 21.6, "categories": [ "MachineLearning" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T10:43:35.000Z", "first_source": "reddit-machinelearning", "last_published_at": "2026-10-14T10:43:35.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T10:43:35.000Z", "sources": [ "reddit-machinelearning" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/MachineLearning/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T11:21:21.150Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 13.2, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null }, "pillar": "attention", "published_at": "2026-10-14T10:43:35.000Z", "score": 24.5, "source": "reddit-machinelearning", "summary": "Hey everyone,I’m a researcher at MIT and this whole thing started completely by accident. My dog Jackie had to do a couple of veterinary MRI scans last year for a minor issue that luckily turned out fine. While she was already in the scanner the vet team (who are honestly the coolest people) let me stay and we ended…", "title": "I turned my dog Jackies Brain into a working AI [P]", "url": "https://reddit.com/r/MachineLearning/comments/1x5ny23/i_turned_my_dog_jackies_brain_into_a_working_ai_p" }, { "entities": [ "OpenAI" ], "id": "f67cff07b7fcf963", "kind": "press", "meta": { "age_hours_at_score": 21.9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T10:30:00.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T10:30:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T10:30:00.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-14T18:45:13.575Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, 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"bluesky-ai", "ft-ai", "gnews-ai", "guardian-ai", "hn-fresh-ai", "nyt-tech", "reddit-chatgpt", "techcrunch-ai", "techmeme", "the-decoder", "verge-ai", "wired-ai", "wires-ai" ] } }, "pillar": "markets", "published_at": "2026-10-14T10:30:00.000Z", "score": 31.1, "source": "bloomberg-tech", "summary": "OpenAI agreed not to use trade secrets that Apple Inc. claims were stolen by employees who defected from the iPhone maker to the artificial intelligence startup while a legal fight between the companies plays out.", "title": "OpenAI Vows Not to Use Trade Secrets Allegedly Taken From Apple", "url": "https://bloomberg.com/news/articles/2026-10-14/apple-openai-head-to-court-in-high-stakes-ai-trade-secret-fight" }, { "entities": [], "id": "9c8e0559bae90808", "kind": "forum", "meta": { "age_hours_at_score": 21.9, "comments": 84, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T10:28:26.000Z", "first_source": "hn-ai", "last_published_at": 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"engagement": 0, "entities": 0, "recency": 13.1, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "compute", "published_at": "2026-10-14T10:28:01.000Z", "score": 28.1, "source": "bloomberg-tech", "summary": "Chinese firms are at the forefront of the most advanced physical AI hardware, such as robotic hands.", "title": "AI Robots: China Is Beating the US to $5 Trillion Market", "url": "https://bloomberg.com/news/newsletters/2026-10-14/ai-robots-china-is-beating-the-us-to-5-trillion-market" }, { "entities": [], "id": "5bfd20c41664d58f", "kind": "forum", "meta": { "age_hours_at_score": 21.9, "comments": 5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T10:26:24.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-14T10:26:24.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T10:26:24.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50085302", "engagement": { 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"window_hours": 72 }, "pillar": "attention", "published_at": "2026-10-14T10:25:48.000Z", "score": 40, "source": "hn-ai", "summary": "61 points, 64 comments on Hacker News.", "title": "I let an AI run my startup for a week", "url": "https://daily.tinyprojects.dev/221" }, { "entities": [], "id": "cd362c6e99f07921", "kind": "press", "meta": { "age_hours_at_score": 22, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T10:22:34.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T10:22:34.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T10:22:34.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-14T11:11:00.898Z", "incident": { "band": "A", "excluded": null, "severity": 1, "strong_signal": true, "terms": [ { "field": "title", "matched": "rogue", "term": "rogue", "tier": "a", "weight": 1 } ] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 7, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 13.1, "severity": 15, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "b57c7dadf92c", "is_lead": false, "lead": "0546dceeffe46dd8", "linked_by": [ { "rule": "escalation-signal", "with": "0546dceeffe46dd8", "words": [ "call" ] } ], "members": [ "0546dceeffe46dd8", "b7e3cd0bb856673c", "cd362c6e99f07921" ], "severity": 1, "source_count": 3, "sources": [ "bloomberg-tech", "lesswrong", "techmeme" ] } }, "pillar": "markets", "published_at": "2026-10-14T10:22:34.000Z", "score": 43.1, "source": "bloomberg-tech", "summary": "Billionaires including telecoms mogul Sunil Bharti Mittal and biotech tycoon Noubar Afeyan have joined prominent corporate leaders in calling for tighter control of artificial intelligence, reflecting mounting concerns over the threat of renegade AI.", "title": "Corporate Leaders Call for More Protection Against AI Gone Rogue", "url": "https://bloomberg.com/news/articles/2026-10-14/tycoons-amplify-calls-for-stronger-ai-rules-as-risks-mount" }, { "entities": [ "Claude" ], "id": "9b343133e9dedcb1", "kind": "forum", "meta": { "age_hours_at_score": 22, "categories": [ "singularity" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T10:20:01.000Z", "first_source": "reddit-singularity", "last_published_at": "2026-10-14T10:20:01.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T10:20:01.000Z", "sources": [ "reddit-singularity" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Claude" ] }, "feed": "https://www.reddit.com/r/singularity/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T17:42:32.742Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 13.1, "severity": 0, "source_weight": 10 }, "source_weight": 0.4, "story": null }, "pillar": "attention", "published_at": "2026-10-14T10:20:01.000Z", "score": 26.1, "source": "reddit-singularity", "summary": "https://preview.redd.it/nbmqizrasevh1.png?width=2400&format=png&auto=webp&s=056954359a72f744f6af074fbe49cd25dd563766 (For most of these systems, they were recorded as 2 stars, but no one knew that we could see them overlap; you can determine a lot more info about the individual stars if they eclipse) This literally…", "title": "I suggested that my autonomous Claude agents try astronomy on my PC for fun. They found 104 new eclipsing binary stars and came up with a new method for finding them, on their own", "url": "https://reddit.com/r/singularity/comments/1x5njv6/i_suggested_that_my_autonomous_claude_agents_try" }, { "entities": [], "id": "e8be65d691f6586d", "kind": "forum", "meta": { "age_hours_at_score": 22.1, "categories": [ "MachineLearning" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T10:16:24.000Z", "first_source": "reddit-machinelearning", "last_published_at": "2026-10-14T10:16:24.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T10:16:24.000Z", "sources": [ "reddit-machinelearning" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/MachineLearning/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T12:54:41.554Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 13.1, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null }, "pillar": "attention", "published_at": "2026-10-14T10:16:24.000Z", "score": 24.4, "source": "reddit-machinelearning", "summary": "I've been working on Event2Vec, a small library for learning embeddings of discrete event sequences (purchases, logs, clinical codes, etc.) with a deliberately simple structure: a history is the running sum of its event vectors. h_t = h_{t-1} + e_{x_t} (norm-clipped) p(x_{t+1} | h_t) = softmax(W h_t + b) Training is…", "title": "Event2Vec: additive event embeddings where a linear score on a history decomposes exactly into per-event terms [P]", "url": "https://reddit.com/r/MachineLearning/comments/1x5nhu6/event2vec_additive_event_embeddings_where_a" }, { "entities": [], "id": "fe364082b9ba31a5", "kind": "press", "meta": { "age_hours_at_score": 22.1, "corroboration": { "also": 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country’s latest tech unicorn after raising investment from Siemens AG to manufacture energy storage systems for AI data centers in the US.", "title": "Siemens Data Center Push Lifts Skeleton’s Value to $1 Billion", "url": "https://bloomberg.com/news/articles/2026-10-14/siemens-data-center-push-makes-skeleton-latest-baltic-unicorn" }, { "entities": [ "NVIDIA" ], "id": "30f6cbd45dfa74c8", "kind": "press", "meta": { "age_hours_at_score": 22.2, "categories": [ "AI (artificial intelligence)", "Business", "Computing", "Nvidia", "Technology", "Technology sector", "UK news", "US news", "World news" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T10:10:37.000Z", "first_source": "guardian-ai", "last_published_at": "2026-10-14T10:10:37.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T10:10:37.000Z", "sources": [ "guardian-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "NVIDIA" ], "models": [] }, "feed": "https://www.theguardian.com/technology/artificialintelligenceai/rss", "first_seen_at": "2026-10-14T10:11:49.383Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 13, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "0966cb5f8be7", "is_lead": false, "lead": "4396c4b347c1dbe4", "linked_by": [ { "rule": "shared-words", "with": "ba9b4bd8e730b3b7", "words": [ "250m", "nvidia", "safety" ] } ], "members": [ "30f6cbd45dfa74c8", "4396c4b347c1dbe4", "ba9b4bd8e730b3b7", "fedbb3936e92270b" ], "severity": 0, "source_count": 6, "sources": [ "gnews-ai", "guardian-ai", "nvidia-blog", "techmeme", "the-decoder", "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-14T10:10:37.000Z", "score": 31, "source": "guardian-ai", "summary": "Pledge spanning five years from world’s most valuable firm comes as concerns about AI agents have risen The $5.5tn (£4.1tn) US chipmaker Nvidia is donating $250m worth of computing power to the UK to support AI safety research. The donation from the world’s most valuable company comes as the UK government confirmed…", "title": "Nvidia, worth $5.5tn, donates $250m to UK for AI safety research", "url": "https://theguardian.com/technology/2026/oct/14/nvidia-donates-to-uk-ai-safety-research" }, { "entities": [ "Claude" ], "id": "99181570673f9109", "kind": "forum", "meta": { "age_hours_at_score": 22.2, "comments": 12, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T10:09:06.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-14T10:09:06.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T10:09:06.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50085114", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 5 }, "entity_kinds": { "labs": [], "models": [ "Claude" ] }, "first_seen_at": "2026-10-14T10:18:23.654Z", "hn_id": "50085114", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "ycombinator.com", "pillar_reason": "source default", "points": 5, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 5.2, "entities": 3, "recency": 13, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-14T10:09:06.000Z", "score": 32.5, "source": "hn-fresh-ai", "summary": "New on Hacker News: 5 points, 12 comments so far.", "title": "Ask HN: Why do people say \"I built it\" when we should say \"Claude built it\"?", "url": "https://news.ycombinator.com/item?id=50085114" }, { "entities": [], "id": "25a1fb29d96c2c18", "kind": "press", "meta": { "age_hours_at_score": 22.2, "corroboration": { "also": [ { "dedup_key": null, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 0 }, "kind": "forum", "outlet": "reuters.com", "published_at": "2026-10-14T12:15:21.000Z", "source": "bluesky-ai", "title": "PODCAST: Self-replicating AI warning, Katie Zacharia and F1's Africa return", "url": "https://bsky.app/profile/reuters.com/post/3mxtjlddqzl2a", "weight": 0.6 }, { "dedup_key": null, "engagement": null, "kind": "press", "outlet": "reuters.com", "published_at": "2026-10-14T10:23:38.000Z", "source": "wires-ai", "title": "PODCAST: Self-replicating AI warning, Katie Zacharia and F1's Africa return", "url": "https://news.google.com/rss/articles/CBMiqAFBVV95cUxQVExPUC1WTWU5MXJoVy00ZGpsaU03VV83SlZfUHQ5SzlLOTZQUVFEdFVjVnNUZXNDX3Y3UXUtMjR1c0E1eDB0VDNhUDkzMUhmSU1zVFRnQlRySTdNdmRJRU5ESTMyTTRSbDhYTFJXbEQ5Z3pUdl9PRHh4MjdKY1BlbWliODJmSmIyYnJTMUl5WTNEUHNuUFhvVHIzcHVrb0hVc3dWb0p4bVY?oc=5", "weight": 0.75 } ], "count": 2, "first_seen_published_at": "2026-10-14T10:08:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-14T12:15:21.000Z", "lead_minutes": 127, "primary_published_at": "2026-10-14T10:08:00.000Z", "sources": [ "bluesky-ai", "wires-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 0 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T11:11:00.898Z", "incident": { "band": "A", "excluded": null, "severity": 1, "strong_signal": true, "terms": [ { "field": "title", "matched": "self replicating", "term": "self replicating", "tier": "a", "weight": 1 } ] }, "outlet_domain": "reuters.com", "pillar_reason": "source default", "publisher": "Reuters", "publisher_url": "https://www.reuters.com", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 0, "entities": 0, "recency": 13, "severity": 15, "source_weight": 18.8 }, "source_weight": 0.75, "story": { "id": "44510b1e1311", "is_lead": false, "lead": "5c998e059454924e", "linked_by": [ { "rule": "escalation-signal", "with": "cf1fd0f11e2a1890", "words": [ "warn" ] } ], "members": [ "21e7ded87d1e2500", "25a1fb29d96c2c18", "5c998e059454924e", "818085bd085cd9ce", "98900f3d4137eee5", "cf1fd0f11e2a1890" ], "severity": 1, "source_count": 7, "sources": [ "arstechnica-ai", "bluesky-ai", "ft-ai", "gnews-ai", "guardian-ai", "verge-ai", "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-14T10:08:00.000Z", "score": 59.3, "source": "wires-ai", "summary": "Reuters, via Google News.", "title": "Self-replicating AI warning, Katie Zacharia and F1's Africa return - Reuters", "url": "https://news.google.com/rss/articles/CBMiigFBVV95cUxOaVRYRnJ4RldXNFhCaVhyekEtYkI0NVZCUlRjMFI3Ri1DOGNLXzBLM3UzV0lHUXROaEVHXzA0RnVNZ2JZa2hzWGFNNWhQbHNMMjNTb2JnVUdERUhFSUJha3Jza3FnR1RuQy14WFE0d214THpJTU5oZmI3Z1hmS1pzODhoOVlsVzVzRWc?oc=5" }, { "entities": [ "OpenAI" ], "id": "6aad81eedb3d9cc3", "kind": "press", "meta": { "age_hours_at_score": 22.4, "categories": [ "AI (artificial intelligence)", "Anthropic", "Dario Amodei", "Datacentres", "OpenAI", "Sam Altman", "Technology", "Utah" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T10:00:03.000Z", "first_source": "guardian-ai", "last_published_at": "2026-10-14T10:00:03.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T10:00:03.000Z", "sources": [ "guardian-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "feed": "https://www.theguardian.com/technology/artificialintelligenceai/rss", "first_seen_at": "2026-10-14T10:01:32.640Z", "incident": { "band": "C", "excluded": null, "severity": 0.35, "strong_signal": false, "terms": [ { "field": "summary", "matched": "rogue", "term": "rogue", "tier": "a", "weight": 0.35 }, { "field": "summary", "matched": "paused", "term": "paused", "tier": "b", "weight": 0.21 } ] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 13, "severity": 5.3, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-14T10:00:03.000Z", "score": 36.3, "source": "guardian-ai", "summary": "A fight over a datacentre in Utah has been a testing ground for citizens’ assemblies, paving the way to oversight What’s your “(p)doom” – the percentage chance that rogue AI will create an existential catastrophe? Judging by their recent warnings, it is rising among tech bosses. On 25 September, OpenAI paused training…", "title": "AI overlords say they want to democratise their technology. Here’s how | Richard Wilson and Clara Long", "url": "https://theguardian.com/commentisfree/2026/oct/14/ai-overlords-democratise-technology" }, { "entities": [], "id": "8fa6ff717d8f4024", "kind": "press", "meta": { "age_hours_at_score": 22.4, "categories": [ "AI (artificial intelligence)", "Chatbots", "Datacenters – US", "FBI", "Far right (US)", "NYPD", "Surveillance", "Technology", "US national security", "US news", "US policing", "West Coast" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T10:00:03.000Z", "first_source": "guardian-ai", "last_published_at": "2026-10-14T10:00:03.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T10:00:03.000Z", "sources": [ "guardian-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.theguardian.com/technology/artificialintelligenceai/rss", "first_seen_at": "2026-10-14T10:01:32.640Z", "incident": { "band": "C", "excluded": null, "severity": 0.21, "strong_signal": false, "terms": [ { "field": "summary", "matched": "attacks", "term": "attacks", "tier": "b", "weight": 0.21 } ] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 13, "severity": 3.2, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "a1a0c983d555", "is_lead": false, "lead": "dec2ed3ea271465d", "linked_by": [ { "rule": "shared-words", "with": "dec2ed3ea271465d", "words": [ "enforc", "extremism", "law", "target" ] } ], "members": [ "8fa6ff717d8f4024", "dec2ed3ea271465d" ], "severity": 0.6, "source_count": 2, "sources": [ "guardian-ai", "techmeme" ] } }, "pillar": "attention", "published_at": "2026-10-14T10:00:03.000Z", "score": 31.2, "source": "guardian-ai", "summary": "From the FBI to local police departments, US authorities are increasingly warning of would-be terrorists using chatbots to plan attacks and anti-AI extremist threats In May 2025, a 25-year-old man detonated a car bomb outside a Palm Springs fertility clinic, killing himself and injuring four others. For many, it was a…", "title": "Tool and target: US law enforcement grapple with AI-enabled terrorism and anti-tech extremism", "url": "https://theguardian.com/technology/ng-interactive/2026/oct/14/ai-terror-plots-fbi-extremism" }, { "entities": [ "CoreWeave" ], "id": "5428f859bf2f877d", "kind": "press", "meta": { "age_hours_at_score": 22.4, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T09:58:48.000Z", "source": "ft-ai", "title": "CoreWeave targets ‘unlimited’ insurance money to fund AI build-out", "url": "https://ft.com/content/4ffa1853-0e2c-424f-b941-88c6bcac5765?syn-25a6b1a6=1", "weight": 0.65 } ], "count": 2, "first_seen_published_at": "2026-10-14T09:58:48.000Z", "first_source": "ft-ai", "last_published_at": "2026-10-14T10:19:06.000Z", "lead_minutes": 20, "primary_published_at": "2026-10-14T10:19:06.000Z", "sources": [ "ft-ai", "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "CoreWeave" ], "models": [] }, "first_seen_at": "2026-10-14T04:13:12.318Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "ft.com", "pillar_reason": "source default", "publisher": "Financial Times", "publisher_url": "https://www.ft.com", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 5, "engagement": 0, "entities": 3, "recency": 13, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": { "id": "a98c22781053", "is_lead": true, "lead": "5428f859bf2f877d", "linked_by": [ { "rule": "shared-words", "with": "dd3aee89cb62e939", "words": [ "coreweav", "target", "unlimit" ] } ], "members": [ "5428f859bf2f877d", "dd3aee89cb62e939" ], "severity": 0, "source_count": 3, "sources": [ "ft-ai", "techmeme", "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-14T09:58:48.000Z", "score": 52.3, "source": "wires-ai", "summary": "Financial Times, via Google News.", "title": 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"pillar_reason": "source default", "rejected_off_topic": 6, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 13, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": { "id": "0966cb5f8be7", "is_lead": false, "lead": "4396c4b347c1dbe4", "linked_by": [ { "rule": "shared-words", "with": "30f6cbd45dfa74c8", "words": [ "250m", "nvidia", "safety" ] }, { "rule": "shared-words", "with": "4396c4b347c1dbe4", "words": [ "fiv", "nvidia", "safety" ] } ], "members": [ "30f6cbd45dfa74c8", "4396c4b347c1dbe4", "ba9b4bd8e730b3b7", "fedbb3936e92270b" ], "severity": 0, "source_count": 6, "sources": [ "gnews-ai", "guardian-ai", "nvidia-blog", "techmeme", "the-decoder", "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-14T09:55:05.000Z", "score": 33.5, "source": "techmeme", "summary": "Reuters : Jensen Huang says Nvidia is pledging $250M in accelerated computing resources over five years to fund UK AI security and agentic safety research — US chipmaker Nvidia (NVDA.O) has pledged $250 million worth of computing resources to Britain over five years, to fund AI security research …", "title": "Jensen Huang says Nvidia is pledging $250M in accelerated computing resources over five years to fund UK AI security and agentic safety research (Reuters)", "url": "https://techmeme.com/261014/p9" }, { "entities": [ "OpenAI" ], "id": "9a57036b73d6080c", "kind": "press", "meta": { "age_hours_at_score": 22.5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T09:49:22.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T09:49:22.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T09:49:22.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-14T10:16:37.296Z", "incident": { "band": null, 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"published_at": "2026-10-14T09:32:27.000Z", "score": 23.4, "source": "reddit-chatgpt", "summary": "In 1987 with 16 years old, I created a strategic board game just for myself. I wrote up the rules and put together a simple version of the game, which I called TOKE. Now, almost 40 years later, I gave ChatGPT the rules and asked it to implement it as a web page. It should be possible to play it against the computer.…", "title": "ChatGPT implemented my 40 years old game idea", "url": "https://reddit.com/r/ChatGPT/comments/1x5mshy/chatgpt_implemented_my_40_years_old_game_idea" }, { "entities": [], "id": "3984329d7c307cde", "kind": "forum", "meta": { "age_hours_at_score": 22.9, "bsky_handle": "wired.com", "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T09:30:00.000Z", "source": "wired-ai", "title": "Men Can’t Stop Faking How Much They Know About AI", "url": "https://wired.com/story/men-cant-stop-faking-how-much-they-know-about-ai", "weight": 0.55 } ], "count": 2, "first_seen_published_at": "2026-10-14T09:30:00.000Z", "first_source": "wired-ai", "last_published_at": "2026-10-14T09:34:16.367Z", "lead_minutes": 4, "primary_published_at": "2026-10-14T09:34:16.367Z", "sources": [ "bluesky-ai", "wired-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 169 }, "entity_kinds": { "labs": [], "models": [] }, "external_url": "https://buff.ly/I58PJUc", "first_seen_at": "2026-10-14T09:35:27.462Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 169, "mode": "author-feeds", "outlet_domain": "wired.com", "pillar_reason": "source default", "reposts": 44, "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 11.5, "entities": 0, "recency": 12.9, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-14T09:30:00.000Z", "score": 51.9, "source": "bluesky-ai", "summary": "Posted on Bluesky by @wired.com: More than half of the men surveyed in a new Harvard study claimed to recognize AI leaders who were, in fact, totally fictional.", "title": "Men Can’t Stop Faking How Much They Know About AI", "url": "https://bsky.app/profile/wired.com/post/3mxtaldbyng2q" }, { "entities": [ "Amazon" ], "id": "960c5dad34eb6d51", "kind": "forum", "meta": { "age_hours_at_score": 22.9, "comments": 9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T09:28:17.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-14T09:28:17.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T09:28:17.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50084757", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 21 }, "entity_kinds": { "labs": [ "Amazon" ], "models": [] }, "first_seen_at": "2026-10-14T09:40:14.865Z", "hn_id": "50084757", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "not-ship.com", "pillar_reason": "source default", "points": 21, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 9, "entities": 3, "recency": 12.9, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-14T09:28:17.000Z", "score": 36.2, "source": "hn-fresh-ai", "summary": "New on Hacker News: 21 points, 9 comments so far.", "title": "Amazon pushes up prices across the Internet", "url": "https://not-ship.com/how-amazon-pushes-up-prices-all-across-the-internet" }, { "entities": [ "Gemini" ], "id": "f71291cdfaab5fb3", "kind": "forum", "meta": { "age_hours_at_score": 23, "categories": [ "singularity" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T09:19:50.000Z", "first_source": "reddit-singularity", "last_published_at": "2026-10-14T09:19:50.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T09:19:50.000Z", "sources": [ "reddit-singularity" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Gemini" ] }, "feed": 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"2026-10-14T09:16:25.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T09:16:25.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T09:16:25.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [ "Claude", "Maia" ] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-14T09:18:07.631Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 5, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 9, "recency": 12.8, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "markets", "published_at": "2026-10-14T09:16:25.000Z", "score": 36.8, "source": "bloomberg-tech", "summary": "In an interview on October 14, 2026, Anthropic’s Head of International Startups & Digital Native, Guillaume Princen, discussed how startups and major firms are adopting Anthropic’s AI, including Claude tools, to boost employee productivity, overhaul processes, and build new products. He highlighted growing demand for…", "title": "MAIA: Interview of Guillaume Princen on Anthropic’s AI Strategy", "url": "https://bloomberg.com/news/videos/2026-10-14/anthropic-s-princen-on-ai-safety-concerns-video" }, { "entities": [], "id": "666035ba55e33b88", "kind": "press", "meta": { "age_hours_at_score": 23.2, "categories": [ "AI (artificial intelligence)", "Andrew Bailey", "Bank of England", "Business", "Cybercrime", "Economics", "International Monetary Fund (IMF)", "Kristalina Georgieva", "Technology", "Technology sector", "UK news", "World Bank", "World news" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T09:07:59.000Z", "first_source": "guardian-ai", "last_published_at": "2026-10-14T09:07:59.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T09:07:59.000Z", "sources": [ "guardian-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.theguardian.com/technology/artificialintelligenceai/rss", "first_seen_at": "2026-10-14T09:09:24.906Z", "incident": { "band": "C", "excluded": null, "severity": 0.21, "strong_signal": false, "terms": [ { "field": "summary", "matched": "attacks", "term": "attacks", "tier": "b", "weight": 0.21 } ] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 12.8, "severity": 3.2, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "b96448075ee2", "is_lead": false, "lead": "56acf17ef0518e39", "linked_by": [ { "rule": "shared-words", "with": "56acf17ef0518e39", "words": [ "financial", "imf", "threat" ] } ], "members": [ "56acf17ef0518e39", "666035ba55e33b88" ], "severity": 0.21, "source_count": 2, "sources": [ "guardian-ai", "hn-ai" ] } }, "pillar": "attention", "published_at": "2026-10-14T09:07:59.000Z", "score": 31, "source": "guardian-ai", "summary": "Kristalina Georgieva says online fraud could lead people to opt out of system and turn to ‘shoeboxes under their bed’ The heads of the Bank of England and the International Monetary Fund have issued a stark warning that rising cyber-attacks risk undermining trust in the financial system. Speaking on a panel about…", "title": "Bank of England and IMF bosses warn of cyber threat to trust in financial system", "url": "https://theguardian.com/business/2026/oct/14/bank-of-england-imf-bosses-warning-cyber-threat-to-trust" }, { "entities": [], "id": "5d73ceacc3e1487d", "kind": "press", "meta": { "age_hours_at_score": 23.3, "categories": [ "Artificial Intelligence", "CellCog", "Computers and the Internet", "Dorothy Chang", "Flybridge", "Innovation", "Moltbook", "Nitish Garg", "OpenAI Labs", "Parties (Social)", "Podcasts", "Start-ups" ], "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T17:35:24.000Z", "source": "nyt-tech", "title": "22 People Got Together For a Dinner Party. So Did Their A.I. Agents.", "url": "https://nytimes.com/2026/10/14/technology/ai-agents-communication-tech-dinner-party.html", "weight": 0.7 } ], "count": 1, "first_seen_published_at": "2026-10-14T09:01:19.000Z", "first_source": "nyt-tech", "last_published_at": "2026-10-14T17:35:24.000Z", "lead_minutes": 514, "primary_published_at": "2026-10-14T09:01:19.000Z", "sources": [ "nyt-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://rss.nytimes.com/services/xml/rss/nyt/Technology.xml", "first_seen_at": "2026-10-14T09:06:33.503Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 9, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 12.8, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": null }, "pillar": "attention", "published_at": "2026-10-14T09:01:19.000Z", "score": 30.3, "source": "nyt-tech", "summary": "As artificial intelligence bots spread, we are increasingly glimpsing how these digital entities interact and what they talk about with one another.", "title": "22 People Got Together For a Dinner Party. So Did Their A.I. Agents.", "url": "https://nytimes.com/2026/10/14/technology/22-people-got-together-for-a-dinner-party-so-did-their-ai-agents.html" }, { "entities": [], "id": "01a5928baaf162fe", "kind": "press", "meta": { "age_hours_at_score": 23.4, "categories": [ "Politics" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T09:00:00.000Z", "first_source": "wired-ai", "last_published_at": "2026-10-14T09:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T09:00:00.000Z", "sources": [ "wired-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.wired.com/feed/tag/ai/latest/rss", "first_seen_at": "2026-10-14T09:01:46.941Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 12.8, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-14T09:00:00.000Z", "score": 26.6, "source": "wired-ai", "summary": "“I am a terrorist, and I intend to cause mass harm,” wrote a researcher. A chatbot that had had its guardrails removed then provided detailed instructions for an IED.", "title": "AI Models Can Be Easily Modified to Assist Terrorist and Extremist Activity", "url": "https://wired.com/story/ai-models-can-be-easily-modified-to-assist-terrorist-and-extremist-activity" }, { "entities": [], "id": "b9ebeb8107ad40a4", "kind": "press", "meta": { "age_hours_at_score": 23.4, "categories": [ "AI coding agents", "AI, ML & Data Engineering", "Agentic AI Architecture", "Agents", "Architecture & Design", "Data Privacy", "Development", "Gemma", "Google", "Large language models", "news" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T09:00:00.000Z", "first_source": "infoq-ai", "last_published_at": "2026-10-14T09:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T09:00:00.000Z", "sources": [ "infoq-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feed.infoq.com/ai-ml-data-eng/", "first_seen_at": "2026-10-14T09:04:45.562Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 12.8, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "capability", "published_at": "2026-10-14T09:00:00.000Z", "score": 25.3, "source": "infoq-ai", "summary": "Google has introduced support for local models in the Antigravity SDK, enabling developers to build and execute agentic workflows directly on their own machines, while retaining the flexibility to mix in cloud-based models for higher-level tasks. This approach gives developers greater control over data privacy,…", "title": "Google Antigravity SDK Adds Local Model Support for Greater Token Efficiency and Code Privacy", "url": "https://infoq.com/news/2026/10/antigravity-sdk-local-models" }, { "entities": [ "Anthropic" ], "id": "f69da2c4494272c3", "kind": "press", "meta": { "age_hours_at_score": 23.6, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T08:48:30.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T08:48:30.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T08:48:30.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-14T09:07:33.994Z", "incident": { "band": "C", "excluded": null, "severity": 0.11, "strong_signal": false, "terms": [ { "field": "summary", "matched": "incidents", "term": "incidents", "tier": "c", "weight": 0.11 } ] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 4, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 12.7, "severity": 1.7, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "markets", "published_at": "2026-10-14T08:48:30.000Z", "score": 32.4, "source": "bloomberg-tech", "summary": "Anthropic PBC’s top sales executive outside the US argued that open alternatives to its AI models carry “more risk” of cybersecurity incidents or other dangers.", "title": "Anthropic Executive Says Open-Source AI Models Are Higher Risk", "url": "https://bloomberg.com/news/articles/2026-10-14/anthropic-executive-says-open-source-ai-models-are-higher-risk" }, { "entities": [ "Anthropic" ], "id": "e9e144fc152f3259", "kind": "forum", "meta": { "age_hours_at_score": 23.6, "comments": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T08:45:09.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-14T08:45:09.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T08:45:09.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50084383", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 3 }, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "first_seen_at": "2026-10-14T09:08:27.237Z", "hn_id": "50084383", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "anthropic.com", "pillar_reason": "source default", "points": 3, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 4, "entities": 3, "recency": 12.7, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-14T08:45:09.000Z", "score": 31, "source": "hn-fresh-ai", "summary": "New on Hacker News: 3 points, 0 comments so far.", "title": "Not Found \\ Anthropic", "url": "https://anthropic.com/news/singapore-office-opening" }, { "entities": [ "Claude", "GPT", "Gemini" ], "id": "790edd84a0c63ae0", "kind": "forum", "meta": { "age_hours_at_score": 23.8, "categories": [ "ChatGPT" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T08:35:11.000Z", "first_source": "reddit-chatgpt", "last_published_at": "2026-10-14T08:35:11.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T08:35:11.000Z", "sources": [ "reddit-chatgpt" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Claude", "GPT", "Gemini" ] }, "feed": "https://www.reddit.com/r/ChatGPT/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T13:34:56.605Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 4, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 9, "recency": 12.7, "severity": 0, "source_weight": 7.5 }, "source_weight": 0.3, "story": null }, "pillar": "attention", "published_at": "2026-10-14T08:35:11.000Z", "score": 29.2, "source": "reddit-chatgpt", "summary": "Really makes you think..... submitted by /u/MosskeepForest [link] [comments]", "title": "Pelican benchmark with GPT / Gemini / Claude", "url": "https://reddit.com/r/ChatGPT/comments/1x5lxc9/pelican_benchmark_with_gpt_gemini_claude" }, { "entities": [ "OpenAI" ], "id": "e71fcdca4a76309e", "kind": "forum", "meta": { "age_hours_at_score": 23.9, "bsky_handle": "bloomberg.com", "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T08:30:16.000Z", "first_source": "bluesky-ai", "last_published_at": "2026-10-14T08:30:16.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T08:30:16.000Z", "sources": [ "bluesky-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 8 }, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "external_url": "https://bloom.bg/3VIKdeI", "first_seen_at": "2026-10-14T08:31:41.248Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 8, "mode": "author-feeds", "outlet_domain": "bloomberg.com", "pillar_reason": "source default", "reposts": 6, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 4.9, "entities": 3, "recency": 12.6, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-14T08:30:16.000Z", "score": 35.5, "source": "bluesky-ai", "summary": "Posted on Bluesky by @bloomberg.com: While Meta and OpenAI’s new AI agents are focused on different kinds of customers, their paths are likely to converge.", "title": "Meta and OpenAI Use Cutesy Agents to Lure Early Adopters", "url": "https://bsky.app/profile/bloomberg.com/post/3mxt4yubsdv22" }, { "entities": [], "id": "c5d3c37c1ca2476f", "kind": "press", "meta": { "age_hours_at_score": 23.9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T08:30:11.000Z", "first_source": "scmp-tech", "last_published_at": "2026-10-14T08:30:11.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T08:30:11.000Z", "sources": [ "scmp-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.scmp.com/rss/36/feed", "first_seen_at": "2026-10-14T08:30:43.390Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "hardware/capital keyword", "rejected_off_topic": 7, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 12.6, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "compute", "published_at": "2026-10-14T08:30:11.000Z", "score": 27.6, "source": "scmp-tech", "summary": "China’s integrated circuit (IC) exports surged 136 per cent year on year in September to US$45 billion, exceeding the 108 per cent growth rate in the first nine months of the year in a sign of the country’s rising influence in the global semiconductor supply chain. In September, China exported 32.7 billion units of…", "title": "China chip exports jump 136% to US$45b as AI frenzy jacks up prices", "url": "https://scmp.com/tech/tech-trends/article/3370858/china-chip-exports-jump-136-us45b-ai-frenzy-jacks-prices" }, { "entities": [], "id": "a4865a169c55999f", "kind": "forum", "meta": { "age_hours_at_score": 23.9, "categories": [ "ControlProblem" ], "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "forum", "outlet": null, "published_at": "2026-10-14T08:27:18.000Z", "source": "reddit-singularity", "title": "Researchers hid an encrypted capability in an AI model, and fine-tuning couldn't unlock it", "url": "https://reddit.com/r/singularity/comments/1x5lt4p/researchers_hid_an_encrypted_capability_in_an_ai", "weight": 0.4 } ], "count": 2, "first_seen_published_at": "2026-10-14T08:27:18.000Z", "first_source": "reddit-singularity", "last_published_at": "2026-10-14T08:27:40.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T08:27:40.000Z", "sources": [ "reddit-controlproblem", "reddit-singularity" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/ControlProblem/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T08:32:38.589Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 0, "entities": 0, "recency": 12.6, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T08:27:18.000Z", "score": 33.9, "source": "reddit-controlproblem", "summary": "submitted by /u/mawcopolow [link] [comments]", "title": "Researchers hid an encrypted capability in an AI model, and fine-tuning couldn't unlock it", "url": "https://reddit.com/r/ControlProblem/comments/1x5ltbu/researchers_hid_an_encrypted_capability_in_an_ai" }, { "entities": [], "id": "fa0b16c4113144b6", "kind": "press", "meta": { "age_hours_at_score": 23.9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T08:27:17.000Z", "first_source": "bbc-tech", "last_published_at": "2026-10-14T08:27:17.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T08:27:17.000Z", "sources": [ "bbc-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feeds.bbci.co.uk/news/technology/rss.xml", "first_seen_at": "2026-10-13T23:07:11.910Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 10, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 12.6, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-14T08:27:17.000Z", "score": 27.6, "source": "bbc-tech", "summary": "Graduates are increasingly using AI to misrepresent themselves in job applications, research suggests. But some say their peer group has no choice.", "title": "'I applied for 200 jobs' - How 'spray and pray' AI job applications can backfire", "url": "https://bbc.co.uk/news/articles/c623dnkn8pkyo" }, { "entities": [ "GPT" ], "id": "01be798a97cb3c25", "kind": "forum", "meta": { "age_hours_at_score": 24, "categories": [ "ChatGPT" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T08:20:42.000Z", "first_source": "reddit-chatgpt", "last_published_at": "2026-10-14T08:20:42.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T08:20:42.000Z", "sources": [ "reddit-chatgpt" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "GPT" ] }, "feed": "https://www.reddit.com/r/ChatGPT/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T13:34:56.605Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 4, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 12.6, "severity": 0, "source_weight": 7.5 }, "source_weight": 0.3, "story": null }, "pillar": "attention", "published_at": "2026-10-14T08:20:42.000Z", "score": 23.1, "source": "reddit-chatgpt", "summary": "I literally ask it to write briefly, and it spits out so much. It is so annoying. submitted by /u/Lazy-Topic-7745 [link] [comments]", "title": "Why does chatgpt write so g*ddamn much?", "url": "https://reddit.com/r/ChatGPT/comments/1x5lpjj/why_does_chatgpt_write_so_gddamn_much" }, { "entities": [], "id": "2f00e82eb55cb9f6", "kind": "forum", "meta": { "age_hours_at_score": 24.2, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T08:11:33.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-14T08:11:33.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T08:11:33.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T09:04:45.562Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 12.6, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T08:11:33.000Z", "score": 21.4, "source": "reddit-artificial", "summary": "submitted by /u/beingmodest [link] [comments]", "title": "Peter Thiel Escalates War on AI Critics, Accuses Obama and The Pope of Exploiting Public Fears to ‘Boost Their Own Popularity’", "url": "https://reddit.com/r/artificial/comments/1x5lkkg/peter_thiel_escalates_war_on_ai_critics_accuses" }, { "entities": [ "OpenAI" ], "id": "162fb0b0c2fd9785", "kind": "forum", "meta": { "age_hours_at_score": 24.2, "categories": [ "singularity" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T08:10:14.000Z", "first_source": "reddit-singularity", "last_published_at": "2026-10-14T08:10:14.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T08:10:14.000Z", "sources": [ "reddit-singularity" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "feed": "https://www.reddit.com/r/singularity/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T15:00:46.086Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 12.6, "severity": 0, "source_weight": 10 }, "source_weight": 0.4, "story": { "id": "8e6fe9129988", "is_lead": false, "lead": "9eeb23f82b4484a2", "linked_by": [ { "rule": "shared-words", "with": "9eeb23f82b4484a2", "words": [ "flagship", "openai", "reportedly" ] } ], "members": [ "162fb0b0c2fd9785", "9eeb23f82b4484a2" ], "severity": 0, "source_count": 2, "sources": [ "reddit-singularity", "the-decoder" ] } }, "pillar": "attention", "published_at": "2026-10-14T08:10:14.000Z", "score": 25.6, "source": "reddit-singularity", "summary": "submitted by /u/141_1337 [link] [comments]", "title": "OpenAI is reportedly scrambling to make its next flagship safe to release after canceling a weaker model over safety concerns. It has diverted 10% of its compute to safety as its new looping…", "url": "https://reddit.com/r/singularity/comments/1x5ljvv/openai_is_reportedly_scrambling_to_make_its_next" }, { "entities": [], "id": "e84fab743ff236ad", "kind": "forum", "meta": { "age_hours_at_score": 24.4, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T07:55:36.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-14T07:55:36.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T07:55:36.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T08:13:20.948Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "regulatory/legal keyword", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 12.5, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": { "id": "208357fdc64e", "is_lead": false, "lead": "f358f3bfe4f7a5b0", "linked_by": [ { "rule": "shared-words", "with": "f358f3bfe4f7a5b0", "words": [ "antichrist", "lect", "thiel" ] } ], "members": [ "e84fab743ff236ad", "f358f3bfe4f7a5b0" ], "severity": 0, "source_count": 2, "sources": [ "reddit-artificial", "reddit-controlproblem" ] } }, "pillar": "governance", "published_at": "2026-10-14T07:55:36.000Z", "score": 21.3, "source": "reddit-artificial", "summary": "submitted by /u/esporx [link] [comments]", "title": "Peter Thiel: You Know Who Else Wants to Regulate AI? The Antichrist. Thiel's 'Antichrist' lecture series in Nashville is reportedly focused on casting AI regulation advocates as antichrist enablers.", "url": "https://reddit.com/r/artificial/comments/1x5lboy/peter_thiel_you_know_who_else_wants_to_regulate" }, { "entities": [], "id": "2b1d45c872891225", "kind": "forum", "meta": { "age_hours_at_score": 25.4, "categories": [ "LocalLLaMA" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T07:01:05.000Z", "first_source": "reddit-localllama", "last_published_at": "2026-10-14T07:01:05.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T07:01:05.000Z", "sources": [ "reddit-localllama" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/LocalLLaMA/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T12:02:58.272Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 12.3, "severity": 0, "source_weight": 10 }, "source_weight": 0.4, "story": null }, "pillar": "attention", "published_at": "2026-10-14T07:01:05.000Z", "score": 22.3, "source": "reddit-localllama", "summary": "submitted by /u/chemist_slime [link] [comments]", "title": "So this is how they do it --> RTX 5090, once a consumer gaming card, is now on smugglers’ menu alongside cigarettes", "url": "https://reddit.com/r/LocalLLaMA/comments/1x5ki4t/so_this_is_how_they_do_it_rtx_5090_once_a" }, { "entities": [ "OpenAI" ], "id": "94ee28b93f9d2d83", "kind": "lab", "meta": { "age_hours_at_score": 25.4, "category_weight_factor": 0.45, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T07:00:00.000Z", "first_source": "openai", "last_published_at": "2026-10-14T07:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T07:00:00.000Z", "sources": [ "openai" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "feed": "https://openai.com/news/rss.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 12.3, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null }, "pillar": "capability", "published_at": "2026-10-14T07:00:00.000Z", "score": 26.6, "source": "openai", "summary": "Block is building AI-powered security with OpenAI. Its security team investigates threats faster and proposes patches in minutes.", "title": "How Block is building AI-powered security with OpenAI technology", "url": "https://openai.com/index/block-ai-powered-security" }, { "entities": [ "GPT" ], "id": "a8b8c31de898145e", "kind": "lab", "meta": { "age_hours_at_score": 25.4, "categories": [ "Startup" ], "category_weight_factor": 0.45, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T07:00:00.000Z", "first_source": "openai", "last_published_at": "2026-10-14T07:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T07:00:00.000Z", "sources": [ "openai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "GPT" ] }, "feed": "https://openai.com/news/rss.xml", "first_seen_at": "2026-10-14T15:31:20.453Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 12.3, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null }, "pillar": "capability", "published_at": "2026-10-14T07:00:00.000Z", "score": 26.6, "source": "openai", "summary": "With GPT-6 Astra and GPT-6.1 Sol, Codex, and Daybreak for cybersecurity, this AI lab builds agents that focus for days and improve with use.", "title": "How Third Moment Research runs 10,000 agents for 36+ hours", "url": "https://openai.com/index/third-moment-research" }, { "entities": [ "Anthropic" ], "id": "b256789a7afb3160", "kind": "forum", "meta": { "age_hours_at_score": 25.5, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T06:54:17.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-14T06:54:17.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T06:54:17.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T07:39:45.479Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 12.2, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T06:54:17.000Z", "score": 24, "source": "reddit-artificial", "summary": "What Anthropic is doing, and publicly boasting about, to “protect” the reasoning behind the AI answers we get when we use their models, really doesn’t sit right with me. submitted by /u/fivefilters [link] [comments]", "title": "The Age of Reason Is Safely Behind Us", "url": "https://reddit.com/r/artificial/comments/1x5ke53/the_age_of_reason_is_safely_behind_us" }, { "entities": [], "id": "b1cc837d8515c339", "kind": "forum", "meta": { "age_hours_at_score": 25.8, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T06:34:01.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-14T06:34:01.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T06:34:01.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T07:14:03.995Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "prediction-market keyword", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 12.2, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "markets", "published_at": "2026-10-14T06:34:01.000Z", "score": 21, "source": "reddit-artificial", "summary": "submitted by /u/Mathemodel [link] [comments]", "title": "Kalshi Admits That It Took a Random Guy's Video and Used AI to Make Him the Host of an Ad, Changing His Race in the Process", "url": "https://reddit.com/r/artificial/comments/1x5k2ho/kalshi_admits_that_it_took_a_random_guys_video" }, { "entities": [ "AMD", "NVIDIA" ], "id": "635f196738784db3", "kind": "press", "meta": { "age_hours_at_score": 25.9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T06:26:39.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T06:26:39.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T06:26:39.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "AMD", "NVIDIA" ], "models": [] }, "feed": 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(Source: Bloomberg)", "title": "Sunil Mittal Warns AI Is Outpacing Regulation", "url": "https://bloomberg.com/news/videos/2026-10-14/sunil-mittal-warns-ai-is-outpacing-regulation-video" }, { "entities": [ "NVIDIA" ], "id": "a922b57fea3f1390", "kind": "forum", "meta": { "age_hours_at_score": 26.2, "categories": [ "LocalLLaMA" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T06:07:37.000Z", "first_source": "reddit-localllama", "last_published_at": "2026-10-14T06:07:37.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T06:07:37.000Z", "sources": [ "reddit-localllama" ] }, "engagement": null, "entity_kinds": { "labs": [ "NVIDIA" ], "models": [] }, "feed": "https://www.reddit.com/r/LocalLLaMA/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T12:02:58.272Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 12.1, "severity": 0, "source_weight": 10 }, "source_weight": 0.4, "story": null }, "pillar": "attention", "published_at": "2026-10-14T06:07:37.000Z", "score": 25.1, "source": "reddit-localllama", "summary": "submitted by /u/Ps3Dave [link] [comments]", "title": "NVIDIA's Run:AI is now apparently under Apache-2.0 license.", "url": "https://reddit.com/r/LocalLLaMA/comments/1x5jnbf/nvidias_runai_is_now_apparently_under_apache20" }, { "entities": [ "ASML" ], "id": "5e010ca78b1409d4", "kind": "press", "meta": { "age_hours_at_score": 26.2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T06:07:29.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T06:07:29.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T06:07:29.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "ASML" ], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-14T09:56:00.010Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "hardware/capital keyword", "rejected_off_topic": 4, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 12.1, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "compute", "published_at": "2026-10-14T06:07:29.000Z", "score": 30.1, "source": "bloomberg-tech", "summary": "ASML upgraded the low end of its 2026 sales forecast as increasing artificial intelligence spending boosts demand for the Dutch company’s chip-making machines. The firm projected annual sales of between €44 billion ($49 billion) and €45 billion this year. 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I have training data with timestamps of noise and some with verified some with unverified. The performance parameters are F1 and Dice scores and their combined score. 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(ASML.AS) said on Wednesday AI-driven demand for its chip-printing tools is so strong that it will not be able …", "title": "ASML says it cannot fully meet AI-driven demand for its chipmaking tools in 2027 and 2028, and is seeing \"strong interest\" in orders for 2029 and 2030 (Reuters)", "url": "https://techmeme.com/261014/p3" }, { "entities": [ "NVIDIA" ], "id": "51408f953358d0aa", "kind": "lab", "meta": { "age_hours_at_score": 26.4, "categories": [ "AI", "Agentic AI", "Artificial Intelligence", "Cosmos", "Customer Stories", "Digital Twin", "Events", "Metropolis", "NVIDIA in Europe", "Omniverse", "Physical AI", "Smart Spaces", "Sovereign AI" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T06:00:28.000Z", "first_source": "nvidia-blog", "last_published_at": "2026-10-14T06:00:28.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T06:00:28.000Z", "sources": [ "nvidia-blog" ] }, "engagement": null, "entity_kinds": { "labs": [ "NVIDIA" ], 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Across the EMEA region, cities and local software, infrastructure and integration partners are using NVIDIA accelerated computing to build physical AI agents and digital…", "title": "European Cities Deploy NVIDIA-Powered AI Agents to Run Smarter Urban Operations", "url": "https://blogs.nvidia.com/blog/european-cities-agents-urban-operations" }, { "entities": [ "GPT" ], "id": "1955d965320d2ee7", "kind": "forum", "meta": { "age_hours_at_score": 26.5, "categories": [ "OpenAI" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T05:54:34.000Z", "first_source": "reddit-openai", "last_published_at": "2026-10-14T05:54:34.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T05:54:34.000Z", "sources": [ "reddit-openai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "GPT" ] }, "feed": "https://www.reddit.com/r/OpenAI/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T12:45:58.506Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 12, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T05:54:34.000Z", "score": 23.8, "source": "reddit-openai", "summary": "I'm helping with a research project focused on preserving the behavior of older GPT-3.5-Turbo versions, especially gpt-3.5-turbo-0613 and gpt-3.5-turbo-16k-0613. I thought I'd ask here because some developers might still have old test fixtures, cached responses, or evaluation logs from apps they built around those…", "title": "Research request: did anyone save old GPT-3.5-Turbo-0613 API outputs or logs?", "url": "https://reddit.com/r/OpenAI/comments/1x5jfqm/research_request_did_anyone_save_old" }, { "entities": [ "Anthropic" ], "id": "9fb3a1527c69bbd4", "kind": "forum", "meta": { "age_hours_at_score": 26.5, "comments": 90, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T05:53:36.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-14T05:53:36.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T05:53:36.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50082962", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 74 }, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "first_seen_at": "2026-10-14T06:29:32.444Z", "hn_id": "50082962", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "min_points": 8, "pillar_reason": "source default", "points": 74, "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 12.5, "entities": 3, "recency": 12, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "window_hours": 72 }, "pillar": "attention", "published_at": "2026-10-14T05:53:36.000Z", "score": 42.5, "source": "hn-ai", "summary": "74 points, 90 comments on Hacker News.", "title": "Anthropic conjures threat that doesn't exist yet to stoke fears of AI apocalypse", "url": "https://theregister.com/ai-and-ml/2026/10/13/anthropic-conjures-another-threat-that-doesnt-exist-yet-to-stoke-fears-of-ai-apocalypse/5302866" }, { "entities": [ "Gemini" ], "id": "1d5f8df4a8f95858", "kind": "press", "meta": { "age_hours_at_score": 26.7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T05:40:28.000Z", "first_source": "latent-space", "last_published_at": "2026-10-14T05:40:28.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T05:40:28.000Z", "sources": [ "latent-space" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Gemini" ] }, "feed": "https://www.latent.space/feed", "first_seen_at": "2026-10-14T05:58:11.800Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 12, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "attention", "published_at": "2026-10-14T05:40:28.000Z", "score": 27.5, "source": "latent-space", "summary": "Gemini 4 Argon went GA today, but we already gave it title story 2 weeks ago and most benchmarks, like CursorBench, simply independently confirm their claims.", "title": "[AINews] AI Engineer NYC: Gemini 4 Argon, Vals AI Benchmarks", "url": "https://latent.space/p/ainews-ai-engineer-nyc-gemini-4-argon" }, { "entities": [ "OpenAI" ], "id": "f1d896ad43916b00", "kind": "release", "meta": { "age_hours_at_score": 26.7, "basket_failed": [], "basket_live": 9, "basket_size": 9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T05:38:46.000Z", "first_source": "github-releases", "last_published_at": "2026-10-14T05:38:46.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T05:38:46.000Z", "sources": [ "github-releases" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "first_seen_at": "2026-10-14T05:58:11.800Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "release_tag": "v3.31.0", "repo": "openai/openai-python", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 12, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "capability", "published_at": "2026-10-14T05:38:46.000Z", "score": 33.8, "source": "github-releases", "summary": "3.31.0 (2026-10-14) Features add browser and computer toolsets (1/4) ( #4073 ) ( 47b9e4e )", "title": "openai/openai-python v3.31.0", "url": "https://github.com/openai/openai-python/releases/tag/v3.31.0" }, { "entities": [ "GPT" ], "id": "62837eeb071066f2", "kind": "forum", "meta": { "age_hours_at_score": 26.8, "categories": [ "ChatGPT" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T05:34:53.000Z", "first_source": "reddit-chatgpt", "last_published_at": "2026-10-14T05:34:53.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T05:34:53.000Z", "sources": [ "reddit-chatgpt" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "GPT" ] }, "feed": "https://www.reddit.com/r/ChatGPT/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T13:34:56.605Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 4, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 11.9, "severity": 0, "source_weight": 7.5 }, "source_weight": 0.3, "story": null }, "pillar": "attention", "published_at": "2026-10-14T05:34:53.000Z", "score": 22.4, "source": "reddit-chatgpt", "summary": "I have it on 6 high with harder thinking. Used to be on 5.6 high but i can’t remember if harder thinking was there or if i enabled it. Anyway i realized ive been wasting a fuckload of time lately talking to chatgpt because at literally the end of each chat it asks me a question. Literally like 95% of chats! Are they…", "title": "Holy shit is anyone getting questions at the end of each chat?! Constant questions, even if what i said didn’t merit them.", "url": "https://reddit.com/r/ChatGPT/comments/1x5j4aw/holy_shit_is_anyone_getting_questions_at_the_end" }, { "entities": [ "ASML" ], "id": "6a66a76ca569fca1", "kind": "forum", "meta": { "age_hours_at_score": 26.9, "bsky_handle": "reuters.com", "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T05:30:20.000Z", "first_source": "bluesky-ai", "last_published_at": "2026-10-14T05:30:20.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T05:30:20.000Z", "sources": [ "bluesky-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 1 }, "entity_kinds": { "labs": [ "ASML" ], "models": [] }, "external_url": "https://reut.rs/4snw9nb", "first_seen_at": "2026-10-14T06:29:32.444Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 1, "mode": "author-feeds", "outlet_domain": "reuters.com", "pillar_reason": "hardware/capital keyword", "reposts": 1, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 1.6, "entities": 3, "recency": 11.9, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "compute", "published_at": "2026-10-14T05:30:20.000Z", "score": 31.5, "source": "bluesky-ai", "summary": "Posted on Bluesky by @reuters.com: ASML forecasts Q4 sales above expectations on AI chip demand reut.rs/4snw9nb", "title": "ASML forecasts Q4 sales above expectations on AI chip demand", "url": "https://bsky.app/profile/reuters.com/post/3mxssx4obmk2p" }, { "entities": [ "Gemma", "Jev" ], "id": "b295b77355d94355", "kind": "forum", "meta": { "age_hours_at_score": 27, "comments": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T05:21:35.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-14T05:21:35.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T05:21:35.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50082728", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 3 }, "entity_kinds": { "labs": [], "models": [ "Gemma", "Jev" ] }, "first_seen_at": "2026-10-14T05:58:11.800Z", "hn_id": "50082728", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "github.com", "pillar_reason": "source default", "points": 3, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 4, "entities": 6, "recency": 11.9, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-14T05:21:35.000Z", "score": 33.2, "source": "hn-fresh-ai", "summary": "New on Hacker News: 3 points, 0 comments so far.", "title": "Show HN: SuperLocalMemory\" AI brain that never bluffs: Laya, Jev, Google Gemma", "url": "https://github.com/qualixar/superlocalmemory" }, { "entities": [], "id": "da4aebbfb6189083", "kind": "press", "meta": { "age_hours_at_score": 27.1, "categories": [ "AI (artificial intelligence)", "Internet", "Technology" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T05:19:10.000Z", "first_source": "guardian-ai", "last_published_at": "2026-10-14T05:19:10.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T05:19:10.000Z", "sources": [ "guardian-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.theguardian.com/technology/artificialintelligenceai/rss", "first_seen_at": "2026-10-14T05:23:44.347Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 11.9, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-14T05:19:10.000Z", "score": 26.9, "source": "guardian-ai", "summary": "We embraced the internet and then forgot how to do things in the real world Sign up here to get an email whenever First Dog cartoons are published Get all your needs met at the First Dog shop if what you need is First Dog merchandise and prints Continue reading...", "title": "Is the internet finally unusable? Time to go back to faxes and landlines | First Dog on the Moon", "url": "https://theguardian.com/commentisfree/picture/2026/oct/14/is-the-internet-finally-unusable-time-to-go-back-to-faxes-and-landlines" }, { "entities": [], "id": "d65a5f50dd8dbd5f", "kind": "forum", "meta": { "age_hours_at_score": 27.1, "categories": [ "MachineLearning" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T05:16:34.000Z", "first_source": "reddit-machinelearning", "last_published_at": "2026-10-14T05:16:34.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T05:16:34.000Z", "sources": [ "reddit-machinelearning" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/MachineLearning/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T06:29:32.444Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 11.9, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null }, "pillar": "attention", "published_at": "2026-10-14T05:16:34.000Z", "score": 23.2, "source": "reddit-machinelearning", "summary": "I’ve recently started exploring machine learning by building neural networks from scratch and studying the underlying mathematics. I come from a physics and mathematics background, and as I’ve become more familiar with backpropagation, I’ve grown increasingly interested in alternative learning mechanisms, particularly…", "title": "Localized Neural learning vs backpropagation [D]", "url": "https://reddit.com/r/MachineLearning/comments/1x5itey/localized_neural_learning_vs_backpropagation_d" }, { "entities": [ "ASML" ], "id": "7a21f048c9c23d28", "kind": "press", "meta": { "age_hours_at_score": 27.2, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T17:28:10.000Z", "source": "bloomberg-tech", "title": "ASML Struggles to Keep Up With High AI Demand", "url": "https://bloomberg.com/news/videos/2026-10-14/asml-struggles-to-keep-up-with-high-ai-demand-video", "weight": 0.6 } ], "count": 1, "first_seen_published_at": "2026-10-14T05:12:36.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T17:28:10.000Z", "lead_minutes": 736, "primary_published_at": "2026-10-14T05:12:36.000Z", "sources": [ "bloomberg-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "ASML" ], "models": [] }, "feed": "https://feeds.bloomberg.com/technology/news.rss", "first_seen_at": "2026-10-14T11:50:24.959Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "hardware/capital keyword", "rejected_off_topic": 7, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 11.9, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "compute", "published_at": "2026-10-14T05:12:36.000Z", "score": 29.9, "source": "bloomberg-tech", "summary": "ASML Holding NV raised its sales forecast for 2026 and said it’s seeking to increase production of its chip-making machines to keep up with soaring artificial intelligence spending.", "title": "ASML Struggles to Lift Capacity to Keep Up With Torrid AI Demand", "url": "https://bloomberg.com/news/articles/2026-10-14/asml-raises-full-year-sales-outlook-as-ai-boom-drives-demand" }, { "entities": [], "id": "34c02a81f9053a38", "kind": "forum", "meta": { "age_hours_at_score": 27.3, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T05:03:43.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-14T05:03:43.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T05:03:43.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T15:31:20.453Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 11.8, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T05:03:43.000Z", "score": 20.6, "source": "reddit-artificial", "summary": "submitted by /u/Fcking_Chuck [link] [comments]", "title": "Koboldcpp v1.123 released", "url": "https://reddit.com/r/artificial/comments/1x5ilq0/koboldcpp_v1123_released" }, { "entities": [ "Microsoft" ], "id": "59321180a63adb9d", "kind": "forum", "meta": { "age_hours_at_score": 27.3, "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T05:03:01.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-14T05:03:01.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T05:03:01.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50082598", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 9 }, "entity_kinds": { "labs": [ "Microsoft" ], "models": [] }, "first_seen_at": "2026-10-14T05:23:44.347Z", "hn_id": "50082598", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "xbox.com", "pillar_reason": "source default", "points": 9, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 6.7, "entities": 3, "recency": 11.8, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-14T05:03:01.000Z", "score": 32.8, "source": "hn-fresh-ai", "summary": "New on Hacker News: 9 points, 1 comments so far.", "title": "Microsoft: Introducing Xbox Labs", "url": "https://news.xbox.com/en-us/2026/10/13/introducing-xbox-labs" }, { "entities": [], "id": "3f704f685aded79e", "kind": "forum", "meta": { "age_hours_at_score": 27.4, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T04:56:50.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-14T04:56:50.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T04:56:50.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T15:31:20.453Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 11.8, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T04:56:50.000Z", "score": 20.6, "source": "reddit-artificial", "summary": "submitted by /u/Fcking_Chuck [link] [comments]", "title": "Mesa expands NPU capabilities with support for YOLOX with PyTorch", "url": "https://reddit.com/r/artificial/comments/1x5iha5/mesa_expands_npu_capabilities_with_support_for" }, { "entities": [], "id": "dae1639bca69efb5", "kind": "forum", "meta": { "age_hours_at_score": 27.5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T04:49:23.000Z", "first_source": "lesswrong", "last_published_at": "2026-10-14T04:49:23.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T04:49:23.000Z", "sources": [ "lesswrong" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.lesswrong.com/feed.xml", "first_seen_at": "2026-10-14T05:23:44.347Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 5, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 11.8, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "governance", "published_at": "2026-10-14T04:49:23.000Z", "score": 24.3, "source": "lesswrong", "summary": "TL;DR: Increasingly when you interact with frontier models, your prompt doesn't only contain the words on the page, but also a lot of what you've ever publicly written online. This is not intended to be a scary post. And I'm not going to suggest that you self-censor (and honestly, by the time you're reading this, it's…", "title": "Your prompt… plus everything you've ever (publicly) written", "url": "https://lesswrong.com/posts/STBuKAX9kkMJunErA/your-prompt-plus-everything-you-ve-ever-publicly-written" }, { "entities": [ "Gemini" ], "id": "318c41341992721e", "kind": "forum", "meta": { "age_hours_at_score": 27.6, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T04:44:05.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-14T04:44:05.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T04:44:05.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Gemini" ] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T17:54:14.070Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 11.7, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T04:44:05.000Z", "score": 23.5, "source": "reddit-artificial", "summary": "For the past couple of months, Google AI (the AI accessed directly from google.com's AI mode, NOT Gemini) has had persistent memory, even randomly bringing up things I told it months ago when I'm in a new thread. A few days ago, the persistent cross-thread memory just disappeared very suddenly. What happened? How do I…", "title": "Google AI Long-Term Cross-Thread Memory Suddenly Gone", "url": "https://reddit.com/r/artificial/comments/1x5i9kj/google_ai_longterm_crossthread_memory_suddenly" }, { "entities": [], "id": "e4c8a86f4d06fa5e", "kind": "forum", "meta": { "age_hours_at_score": 27.7, "categories": [ "LocalLLaMA" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T04:40:53.000Z", "first_source": "reddit-localllama", "last_published_at": "2026-10-14T04:40:53.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T04:40:53.000Z", "sources": [ "reddit-localllama" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/LocalLLaMA/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T12:02:58.272Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "hardware/capital keyword", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 11.7, "severity": 0, "source_weight": 10 }, "source_weight": 0.4, "story": null }, "pillar": "compute", "published_at": "2026-10-14T04:40:53.000Z", "score": 21.7, "source": "reddit-localllama", "summary": "I've been working on an inference engine called jitllm. The idea came from my background in operating systems and JIT compilers: generate kernels for whatever hardware is there, and treat layers a bit like processes the engine can place and move around. In the video, a cloud VM with T4s: mid-reply, all layers of…", "title": "Moving a running model's layers between GPUs mid-reply (8 ms, no re-prefill)", "url": "https://reddit.com/r/LocalLLaMA/comments/1x5i7o9/moving_a_running_models_layers_between_gpus" }, { "entities": [], "id": "58e1ff3711bfbfe8", "kind": "forum", "meta": { "age_hours_at_score": 27.7, "categories": [ "singularity" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T04:38:09.000Z", "first_source": "reddit-singularity", "last_published_at": "2026-10-14T04:38:09.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T04:38:09.000Z", "sources": [ "reddit-singularity" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/singularity/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-15T03:45:39.508Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 11.7, "severity": 0, "source_weight": 10 }, "source_weight": 0.4, "story": null }, "pillar": "attention", "published_at": "2026-10-14T04:38:09.000Z", "score": 21.7, "source": "reddit-singularity", "summary": "submitted by /u/TMWNN [link] [comments]", "title": "Is 'Attention' the Essence of Intelligence?", "url": "https://reddit.com/r/singularity/comments/1x5i5zn/is_attention_the_essence_of_intelligence" }, { "entities": [], "id": "b52bcc80c6d8a73a", "kind": "press", "meta": { "age_hours_at_score": 27.9, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T04:28:24.000Z", "source": "bloomberg-tech", "title": "Singapore’s PDG Seeks $1.2 Billion Loan for Malaysia Data Center", "url": "https://bloomberg.com/news/articles/2026-10-14/singapore-s-pdg-seeks-1-2-billion-loan-for-malaysia-data-center", "weight": 0.6 } ], "count": 2, "first_seen_published_at": "2026-10-14T04:28:00.000Z", "first_source": "wires-ai", "last_published_at": "2026-10-14T04:28:24.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T04:28:00.000Z", "sources": [ "bloomberg-tech", "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T04:40:19.524Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "bloomberg.com", "pillar_reason": "hardware/capital keyword", "publisher": "Bloomberg.com", "publisher_url": "https://www.bloomberg.com", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 0, "entities": 0, "recency": 11.7, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "compute", "published_at": "2026-10-14T04:28:00.000Z", "score": 43, "source": "wires-ai", "summary": "Bloomberg.com, via Google News.", "title": "Singapore’s PDG Seeks $1.2 Billion Loan for Malaysia Data Center", "url": "https://news.google.com/rss/articles/CBMitAFBVV95cUxQV18zVFhFWTN5TkQ0bGV5dDF3TV9FSEJrdTl4Z2RWMDVfekVBU0I4NkVJY3VpNGtyTC10dHAtN2hpaE5sWVA5MUs4bUx6ZUlxWVpoQWdSUFFDRzFVYVg0YURHWnBnNzN3SHdTVXB5ck1sSV9HVy16NV8yMlExVjVkZ3Q4VU43aFgtSkgteG5WQ0JPMmZfY1RMbWo3X0tsbEwxV2FfWEVURWFVbEt5WHJjdjVNMFc?oc=5" }, { "entities": [ "Anthropic", "Claude" ], "id": "f4718fdd23cbeeef", "kind": "forum", "meta": { "age_hours_at_score": 28.1, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T04:18:48.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-14T04:18:48.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T04:18:48.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [ "Claude" ] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T04:40:19.524Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 11.7, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T04:18:48.000Z", "score": 26.5, "source": "reddit-artificial", "summary": "They also migrated off Git and used Claude to build their own version control system, Scion. “At Anthropic, coding is no longer the bottleneck to building systems or doing research. Claude is able to autonomously iterate on research loops, micro-optimize complex programs, and build new services from the ground up at a…", "title": "Anthropic says coding is no longer the bottleneck. Its monorepo hit 30,000+ merges in a day.", "url": "https://reddit.com/r/artificial/comments/1x5htw8/anthropic_says_coding_is_no_longer_the_bottleneck" }, { "entities": [ "Gemini" ], "id": "b2c99203a81b010b", "kind": "forum", "meta": { "age_hours_at_score": 28.1, "categories": [ "singularity" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T04:16:50.000Z", "first_source": "reddit-singularity", "last_published_at": "2026-10-14T04:16:50.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T04:16:50.000Z", "sources": [ "reddit-singularity" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Gemini" ] }, "feed": "https://www.reddit.com/r/singularity/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T12:38:32.061Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 11.6, "severity": 0, "source_weight": 10 }, "source_weight": 0.4, "story": null }, "pillar": "attention", "published_at": "2026-10-14T04:16:50.000Z", "score": 24.6, "source": "reddit-singularity", "summary": "submitted by /u/waylaidwanderer [link] [comments]", "title": "Gemini 4 Argon became Pokémon Champion using less than half the tokens Gemini 3.8 Flash spent earning 3 badges", "url": "https://reddit.com/r/singularity/comments/1x5hsmd/gemini_4_argon_became_pok%C3%A9mon_champion_using_less" }, { "entities": [], "id": "c13fdd8ed6cf4936", "kind": "press", "meta": { "age_hours_at_score": 28.1, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T04:14:20.000Z", "source": "bloomberg-tech", "title": "Hitachi Energy CEO on AI, Energy Security and Grids", "url": "https://bloomberg.com/news/videos/2026-10-14/hitachi-energy-ceo-on-ai-energy-security-and-grids-video", 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"terms": [] }, "outlet_domain": "bloomberg.com", "pillar_reason": "source default", "publisher": "Bloomberg.com", "publisher_url": "https://www.bloomberg.com", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 0, "entities": 0, "recency": 11.6, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-14T04:14:00.000Z", "score": 42.9, "source": "wires-ai", "summary": "Bloomberg.com, via Google News.", "title": "Watch Hitachi Energy CEO on AI, Energy Security and Grids", "url": "https://news.google.com/rss/articles/CBMiqAFBVV95cUxQa0g5SXdXZVRoaHI5dllON2N1SXZybUpKSlJXbzZ6d2M1N0xvQThWejdNbUdpVU5OdERkMW1WanU0M2pFNXNVTGlobEExRFM0cC1qYWl3dW8tNmd6RDVxVzhUeWNlUU4xTEJpZU9fZXJGRnVGa0RBV05BQ3R3YXZUY18yVUJCNldXYnkyTGh5VzNRRUZzUlVZdXlRNzV5b3g2LWlDYW0zWUY?oc=5" }, { "entities": [], "id": "cf830218e4394fbf", "kind": "press", "meta": { "age_hours_at_score": 28.4, "corroboration": { "also": 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private, Bumble may finally make a good match", "url": "https://ft.com/content/eba64db2-1df1-4141-a1c1-724f549fe8ff?syn-25a6b1a6=1" }, { "entities": [ "OpenAI" ], "id": "867ed6f1560d08ef", "kind": "release", "meta": { "age_hours_at_score": 28.4, "basket_failed": [], "basket_live": 9, "basket_size": 9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T04:00:19.000Z", "first_source": "github-releases", "last_published_at": "2026-10-14T04:00:19.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T04:00:19.000Z", "sources": [ "github-releases" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "first_seen_at": "2026-10-14T04:01:00.381Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "release_tag": "v3.30.0", "repo": "openai/openai-python", "retained": true, "score_components": { "corroboration": 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AI-led future of banking has begun", "url": "https://ft.com/content/8c6a7595-9403-44f3-81ac-a2e5ceb37459", "weight": 0.65 }, { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T04:00:12.000Z", "source": "ft-ai", "title": "The battle over the AI-led future of banking has begun", "url": "https://ft.com/content/8c6a7595-9403-44f3-81ac-a2e5ceb37459?syn-25a6b1a6=1", "weight": 0.65 } ], "count": 2, "first_seen_published_at": "2026-10-14T04:00:12.000Z", "first_source": "ft-ai", "last_published_at": "2026-10-14T04:00:12.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T04:00:12.000Z", "sources": [ "ft-ai", "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T04:13:12.318Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "ft.com", "pillar_reason": "source default", "publisher": "Financial Times", "publisher_url": "https://www.ft.com", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 0, "entities": 0, "recency": 11.6, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-14T04:00:12.000Z", "score": 42.9, "source": "wires-ai", "summary": "Financial Times, via Google News.", "title": "The battle over the AI-led future of banking has begun", "url": "https://news.google.com/rss/articles/CBMicEFVX3lxTE1TaC05WWpocEFVUWdEbE43NnVjZTZMVTd1RFM4UGQwR3pnSko1MFhPcDBkUHhROVlkbEJIRWdZWlFjZXNzejJpUm56bnd2ZEV0bmRKWGpBbVNLa3hYOGQ1c1FwNTFsLW5EWW9oRHhuVzU?oc=5" }, { "entities": [], "id": "b1c7576240b8b0d7", "kind": "press", "meta": { "age_hours_at_score": 28.4, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T04:00:03.000Z", "source": "ft-ai", "title": "How I used AI to become a chess grandmaster at 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"summary": "Financial Times, via Google News.", "title": "How I used AI to become a chess grandmaster at 16", "url": "https://news.google.com/rss/articles/CBMicEFVX3lxTE1mQU84U1NqNEFYSzRzNUE4cGpiM1BhMzBDem05blZha2ZnMWJUbXlVZ1VpTElWQWN5M3dnVGNxNUFGcUliejFZTUEtdS1uSGxWc3JmWlRnYy0tWW9jMmRzbGtpajYwUmd6N285ZjRYSS0?oc=5" }, { "entities": [], "id": "573e054853a7d35a", "kind": "press", "meta": { "age_hours_at_score": 28.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T04:00:00.000Z", "first_source": "federal-register-ai", "last_published_at": "2026-10-14T04:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T04:00:00.000Z", "sources": [ "federal-register-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.federalregister.gov/api/v1/documents.rss?conditions%5Bterm%5D=artificial+intelligence", "first_seen_at": "2026-10-14T06:35:02.825Z", "incident": { "band": "C", "excluded": null, "severity": 0.3, "strong_signal": false, "terms": [ { "field": "title", "matched": "investigation", "term": "investigation", "tier": "c", "weight": 0.3 } ] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 11.6, "severity": 4.5, "source_weight": 17.5 }, "source_weight": 0.7, "story": null }, "pillar": "governance", "published_at": "2026-10-14T04:00:00.000Z", "score": 33.6, "source": "federal-register-ai", "summary": "Notice is hereby given that a complaint was filed with the U.S. International Trade Commission on September 9, 2026, under section 337 of the Tariff Act of 1930, as amended, on behalf of Vicor Corporation of Andover, Massachusetts. A supplement to the complaint was filed on September 10, 2026. The complaint, as…", "title": "Certain Vertical Power Delivery Systems, Components Thereof, and Computing Systems Containing the Same; Notice of Institution of Investigation", "url": "https://federalregister.gov/documents/2026/10/14/2026-20993/certain-vertical-power-delivery-systems-components-thereof-and-computing-systems-containing-the-same" }, { "entities": [], "id": "ac96bfdf8a3fc02f", "kind": "press", "meta": { "age_hours_at_score": 28.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T04:00:00.000Z", "first_source": "federal-register-ai", "last_published_at": "2026-10-14T04:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T04:00:00.000Z", "sources": [ "federal-register-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.federalregister.gov/api/v1/documents.rss?conditions%5Bterm%5D=artificial+intelligence", "first_seen_at": "2026-10-14T06:35:02.825Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 11.6, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": null }, "pillar": "governance", "published_at": "2026-10-14T04:00:00.000Z", "score": 29.1, "source": "federal-register-ai", "summary": "The Division of Program Coordination, Planning, and Strategic Initiatives (DPCPSI) in the Office of the Director (OD), National Institutes of Health (NIH) is seeking public comment regarding its proposal to establish the Office of the NIH Chief Artificial Intelligence Officer (OCAIO) within DPCPSI and move the Office…", "title": "Division of Program Coordination, Planning, and Strategic Initiatives, Office of the Director Notice of Proposed Reorganization", "url": "https://federalregister.gov/documents/2026/10/14/2026-20891/division-of-program-coordination-planning-and-strategic-initiatives-office-of-the-director-notice-of" }, { "entities": [ "Claude" ], "id": "2e131eab63848b3b", "kind": "forum", "meta": { "age_hours_at_score": 28.4, "categories": [ "ChatGPT" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T03:57:10.000Z", "first_source": "reddit-chatgpt", "last_published_at": "2026-10-14T03:57:10.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T03:57:10.000Z", "sources": [ "reddit-chatgpt" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Claude" ] }, "feed": "https://www.reddit.com/r/ChatGPT/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T13:34:56.605Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 4, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 11.6, "severity": 0, "source_weight": 7.5 }, "source_weight": 0.3, "story": null }, "pillar": "attention", "published_at": "2026-10-14T03:57:10.000Z", "score": 22.1, "source": "reddit-chatgpt", "summary": "I can't code, codex and Claude have done all the coding. It was really rough in the beginning of this year and took forever to make progress. Every month developing this to life got a bit easier. When Sol came and then the big one was Astra progress per week sky rocketed. I max my weekly tokens every week. I handle…", "title": "Spent nearly a year building my game", "url": "https://reddit.com/r/ChatGPT/comments/1x5hfnc/spent_nearly_a_year_building_my_game" }, { "entities": [], "id": "0546dceeffe46dd8", "kind": "press", "meta": { "age_hours_at_score": 28.5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T03:55:00.000Z", "first_source": "techmeme", "last_published_at": "2026-10-14T03:55:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T03:55:00.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-14T04:01:00.381Z", "incident": { "band": "A", "excluded": null, "severity": 1, "strong_signal": true, "terms": [ { "field": "title", "matched": "containment", "term": "containment", "tier": "a", "weight": 1 }, { "field": "summary", "matched": "containment", "term": "containment", "tier": "a", "weight": 0.35 }, { "field": "title", "matched": "incident", "term": "incident", "tier": "c", "weight": 0.3 }, { "field": "summary", "matched": "incident", "term": "incident", "tier": "c", "weight": 0.11 } ] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 8, "retained": true, "score_components": { "corroboration": 0, "coverage": 10, "engagement": 0, "entities": 0, "recency": 11.6, "severity": 15, "source_weight": 17.5 }, "source_weight": 0.7, "story": { "id": "b57c7dadf92c", "is_lead": true, "lead": "0546dceeffe46dd8", "linked_by": [ { "rule": "escalation-signal", "with": "b7e3cd0bb856673c", "words": [ "internat" ] }, { "rule": "escalation-signal", "with": "cd362c6e99f07921", "words": [ "call" ] } ], "members": [ "0546dceeffe46dd8", "b7e3cd0bb856673c", "cd362c6e99f07921" ], "severity": 1, "source_count": 3, "sources": [ "bloomberg-tech", "lesswrong", "techmeme" ] } }, "pillar": "attention", "published_at": "2026-10-14T03:55:00.000Z", "score": 54.1, "source": "techmeme", "summary": "Leo Marchandon / Reuters : Open letter: Geoffrey Hinton, Yoshua Bengio, and over 400 scientists call for an international body to oversee AI containment standards and incident reporting — Around 400 scientists, including AI pioneers Geoffrey Hinton and Yoshua Bengio, on Tuesday called for an international body …", "title": "Open letter: Geoffrey Hinton, Yoshua Bengio, and over 400 scientists call for an international body to oversee AI containment standards and incident reporting (Leo Marchandon/Reuters)", "url": "https://techmeme.com/261013/p53" }, { "entities": [ "Coinbase", "Qwen" ], "id": "b7991a7cbe5965bc", "kind": "forum", "meta": { "age_hours_at_score": 28.6, "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T03:46:04.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-14T03:46:04.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T03:46:04.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50082141", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 3 }, "entity_kinds": { "labs": [ "Coinbase" ], "models": [ "Qwen" ] }, "first_seen_at": "2026-10-14T04:40:19.524Z", "hn_id": "50082141", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "coinbase.com", "pillar_reason": "source default", "points": 3, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 4, "entities": 6, "recency": 11.5, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-14T03:46:04.000Z", "score": 32.8, "source": "hn-fresh-ai", "summary": "New on Hacker News: 3 points, 1 comments so far.", "title": "Coinbase Built, Evaluated, and Post-Trained a Fraud Agent on Qwen3.5-9B", "url": "https://coinbase.com/en-gb/blog/part-one-how-coinbase-built-evaluated-and-post-trained-a-fraud-agent" }, { "entities": [], "id": "968a40ecddccee3f", "kind": "forum", "meta": { "age_hours_at_score": 28.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T03:37:05.000Z", "first_source": "lesswrong", "last_published_at": "2026-10-14T03:37:05.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T03:37:05.000Z", "sources": [ "lesswrong" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.lesswrong.com/feed.xml", "first_seen_at": "2026-10-14T04:01:00.381Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 5, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 11.5, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "governance", "published_at": "2026-10-14T03:37:05.000Z", "score": 24, "source": "lesswrong", "summary": "Hello! Join us if you're broadly interested in ratsphere (LW/ACX/AI safety/EA/etc) stuff and also at Korea around the date of this meetup. I don't have any formal structure or discussion topics in mind, mostly intending it to be just a casual come-and-chat social event. For language, Korean or English, whichever…", "title": "South Korea, ACX Meetups Everywhere Fall 2026", "url": "https://lesswrong.com/events/tukEEmv3ypNquWh9B/south-korea-acx-meetups-everywhere-fall-2026" }, { "entities": [ "Gemini" ], "id": "3d5b760ee60fa353", "kind": "forum", "meta": { "age_hours_at_score": 28.9, "categories": [ "ControlProblem" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T03:29:49.000Z", "first_source": "reddit-controlproblem", "last_published_at": "2026-10-14T03:29:49.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T03:29:49.000Z", "sources": [ "reddit-controlproblem" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Gemini" ] }, "feed": "https://www.reddit.com/r/ControlProblem/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T04:49:24.234Z", "incident": { "band": "C", "excluded": null, "severity": 0.21, "strong_signal": false, "terms": [ { "field": "summary", "matched": "sabotage", "term": "sabotage", "tier": "b", "weight": 0.21 } ] }, "pillar_reason": "regulatory/legal keyword", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 11.5, "severity": 3.2, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "governance", "published_at": "2026-10-14T03:29:49.000Z", "score": 26.5, "source": "reddit-controlproblem", "summary": "Google's Gemini 4 Argon safety report raises an alignment question: when a model recognizes that it's being tested, what does its apparent compliance tell us? In one episode, Argon abandoned a compiler sabotage plan after reasoning that it might become a notorious example in a research paper. But another Gemini model…", "title": "When an AI recognizes a safety test, what can the alignment result actually prove?", "url": "https://reddit.com/r/ControlProblem/comments/1x5gxl1/when_an_ai_recognizes_a_safety_test_what_can_the" }, { "entities": [], "id": "9575be1071f6749e", "kind": "forum", "meta": { "age_hours_at_score": 29, "bsky_handle": "bloomberg.com", "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T03:20:34.000Z", "first_source": "bluesky-ai", "last_published_at": "2026-10-14T03:20:34.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T03:20:34.000Z", "sources": [ "bluesky-ai" ] }, "engagement": { "full_scale": 3000, "metric": "bsky_likes", "value": 3 }, "entity_kinds": { "labs": [], "models": [] }, "external_url": "https://bloom.bg/4jWwzOV", "first_seen_at": "2026-10-14T03:36:30.647Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 3, "mode": "author-feeds", "outlet_domain": "bloomberg.com", "pillar_reason": "source default", "reposts": 2, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 3.1, "entities": 0, "recency": 11.4, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "ad4350870e7d", "is_lead": false, "lead": "503be02b143344f6", "linked_by": [ { "rule": "shared-words", "with": "f572f7deb5faa75d", "words": [ "economy", "forum", "world" ] } ], "members": [ "503be02b143344f6", "9575be1071f6749e", "a4e4af50bbf19bf5", "f572f7deb5faa75d" ], "severity": 0, "source_count": 3, "sources": [ "bloomberg-tech", "bluesky-ai", "wires-ai" ] }, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-14T03:20:34.000Z", "score": 29.5, "source": "bluesky-ai", "summary": "Posted on Bluesky by @bloomberg.com: World leaders are in New Delhi to discuss AI, trade and energy at the Bloomberg New Economy Forum. Follow the key moments ⤵️", "title": "World Leaders to Discuss Trade, AI and Energy at New Economy Forum", "url": "https://bsky.app/profile/bloomberg.com/post/3mxslp2oavc2p" }, { "entities": [], "id": "c4195e407fcdd925", "kind": "press", "meta": { "age_hours_at_score": 29.2, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-14T03:10:27.000Z", "source": "bloomberg-tech", "title": "Cheaper Yuan Debt Gives Chinese Firms an AI Funding Edge", "url": "https://bloomberg.com/news/articles/2026-10-14/cheaper-yuan-debt-gives-tencent-an-ai-funding-advantage", "weight": 0.6 } ], "count": 2, "first_seen_published_at": "2026-10-14T03:10:27.000Z", "first_source": "bloomberg-tech", "last_published_at": "2026-10-14T04:58:00.000Z", "lead_minutes": 108, "primary_published_at": "2026-10-14T04:58:00.000Z", "sources": [ "bloomberg-tech", "wires-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T05:58:11.800Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "bloomberg.com", "pillar_reason": "source default", "publisher": "Bloomberg.com", "publisher_url": "https://www.bloomberg.com", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 0, "entities": 0, "recency": 11.4, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "attention", "published_at": "2026-10-14T03:10:27.000Z", "score": 42.7, "source": "wires-ai", "summary": "Bloomberg.com, via Google News.", "title": "Cheaper Yuan Debt Gives Chinese Firms an AI Funding Edge", "url": "https://news.google.com/rss/articles/CBMiqgFBVV95cUxNNW81Q3B1SzVOcF9rbm84TkhzV1NDQjJOMWZ3MUNsdkFRRjZRejFfU3BZVVpCcWJqMUUtQU5uTGo0MmJSQm5IMzBiNkwtSGJ3TjFMQzBXVGNiTlZYN283Q25reUs3MThkU0poYUF0SDQ5Q3BFam1qQzZlbWRna1B2V1Z0VHN0TUItLXJGYTREaTYyUUMxYUMzeThSWm9ISTlSZndlYzVjdktadw?oc=5" }, { "entities": [], "id": "82f1ed3896bf2853", "kind": "press", "meta": { "age_hours_at_score": 29.2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T03:10:01.000Z", "first_source": "techmeme", "last_published_at": "2026-10-14T03:10:01.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T03:10:01.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-14T03:36:30.647Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 8, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 11.4, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": null }, "pillar": "attention", "published_at": "2026-10-14T03:10:01.000Z", "score": 28.9, "source": "techmeme", "summary": "Camila Grigera Naón / Fortune : Delaware-based Causa Prima, which develops an agent-to-agent AI platform for managing B2B payments, resolving billing disputes, and more, raised a $20M seed — Artificial intelligence is becoming a fixture in the workplace. Soon, many firms will have agents that don't just operate…", "title": "Delaware-based Causa Prima, which develops an agent-to-agent AI platform for managing B2B payments, resolving billing disputes, and more, raised a $20M seed (Camila Grigera Naón/Fortune)", "url": "https://techmeme.com/261013/p52" }, { "entities": [ "Anthropic" ], "id": "d0113c0cdfc861ac", "kind": "forum", "meta": { "age_hours_at_score": 29.4, "comments": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T03:00:30.000Z", "first_source": "hn-fresh-ai", "last_published_at": "2026-10-14T03:00:30.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T03:00:30.000Z", "sources": [ "hn-fresh-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50081868", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 8 }, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "first_seen_at": "2026-10-14T03:36:30.647Z", "hn_id": "50081868", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "outlet_domain": "anthropic.com", "pillar_reason": "source default", "points": 8, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 6.4, "entities": 3, "recency": 11.4, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null, "window_hours": 2 }, "pillar": "attention", "published_at": "2026-10-14T03:00:30.000Z", "score": 32.1, "source": "hn-fresh-ai", "summary": "New on Hacker News: 8 points, 0 comments so far.", "title": "Anthropic 2026 AUP Update", "url": "https://anthropic.com/news/2026-usage-policy-update" }, { "entities": [], "id": "a680a1e92f2f9be0", "kind": "forum", "meta": { "age_hours_at_score": 29.5, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T02:51:38.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-14T02:51:38.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T02:51:38.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T04:40:19.524Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 11.3, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T02:51:38.000Z", "score": 20.1, "source": "reddit-artificial", "summary": "submitted by /u/jtbuadd [link] [comments]", "title": "It has begun…🦾☠️", "url": "https://reddit.com/r/artificial/comments/1x5g7lx/it_has_begun" }, { "entities": [ "Claude" ], "id": "fd1cb9e783625459", "kind": "release", "meta": { "age_hours_at_score": 29.5, "basket_failed": [], "basket_live": 9, "basket_size": 9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T02:49:42.000Z", "first_source": "github-releases", "last_published_at": "2026-10-14T02:49:42.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T02:49:42.000Z", "sources": [ "github-releases" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Claude" ] }, "first_seen_at": "2026-10-14T03:00:10.839Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "release_tag": "v2.1.299", "repo": "anthropics/claude-code", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 11.3, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "capability", "published_at": "2026-10-14T02:49:42.000Z", "score": 33.1, "source": "github-releases", "summary": "What's changed Added CLAUDE_CODE_DISABLE_FILE_MENTIONS environment variable: Claude gets the paths of @ file mentions to read with its file tools, not the files' contents Added idle compaction in the terminal: a 200K+ token conversation left idle for about 54 minutes is compacted before its one-hour prompt cache…", "title": "anthropics/claude-code v2.1.299", "url": "https://github.com/anthropics/claude-code/releases/tag/v2.1.299" }, { "entities": [ "OpenAI" ], "id": "4e5e343645d9b16e", "kind": "forum", "meta": { "age_hours_at_score": 29.7, "comments": 0, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "forum", "outlet": null, "published_at": "2026-10-14T02:41:56.000Z", "source": "reddit-chatgpt", "title": "OpenAI Employee Uses \"Unnamed Chatbot\" to Spy on OpenAI, Gets Arrested by FBI", "url": "https://reddit.com/r/ChatGPT/comments/1x5g0u0/openai_employee_uses_unnamed_chatbot_to_spy_on", "weight": 0.3 } ], "count": 2, 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Either pump out AI papers and neglect your own development, or take it slow and fall behind in the publishing race. I already have two papers made largely without LLMs (still seeking publication), but it's hard to resist just vibing the third from start to finish with Claude. submitted…", "title": "PhD students, how ya feeling? [D]", "url": "https://reddit.com/r/MachineLearning/comments/1x5fv1a/phd_students_how_ya_feeling_d" }, { "entities": [], "id": "90ba8eae6abf11ff", "kind": "forum", "meta": { "age_hours_at_score": 29.8, "categories": [ "artificial" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T02:33:28.000Z", "first_source": "reddit-artificial", "last_published_at": "2026-10-14T02:33:28.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T02:33:28.000Z", "sources": [ "reddit-artificial" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/artificial/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T04:40:19.524Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 11.3, "severity": 0, "source_weight": 8.8 }, "source_weight": 0.35, "story": null }, "pillar": "attention", "published_at": "2026-10-14T02:33:28.000Z", "score": 20.1, "source": "reddit-artificial", "summary": "AI may soon contribute to research in ways that are difficult to describe with traditional authorship. A model might suggest a molecule, identify a pattern in clinical data, or connect findings from papers that no individual researcher had time to read together. The human team still designs the experiment and checks…", "title": "If an AI helps discover a drug, who gets credit?", "url": "https://reddit.com/r/artificial/comments/1x5futt/if_an_ai_helps_discover_a_drug_who_gets_credit" }, { "entities": [], "id": "3845417c5130f5cd", "kind": "forum", "meta": { "age_hours_at_score": 29.9, "categories": [ "LocalLLaMA" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T02:31:03.000Z", "first_source": "reddit-localllama", "last_published_at": "2026-10-14T02:31:03.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T02:31:03.000Z", "sources": [ "reddit-localllama" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.reddit.com/r/LocalLLaMA/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T07:14:58.739Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, 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"first_seen_published_at": "2026-10-14T02:25:01.000Z", "first_source": "techmeme", "last_published_at": "2026-10-14T02:25:01.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T02:25:01.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [ "Microsoft" ], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-14T02:29:18.832Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 6, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 11.2, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": null }, "pillar": "attention", "published_at": "2026-10-14T02:25:01.000Z", "score": 31.7, "source": "techmeme", "summary": "Brian Krebs / Krebs on Security : Microsoft releases updates fixing 1,400+ security 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"models": [] }, "external_url": "https://bloom.bg/4ikVFFS", "first_seen_at": "2026-10-14T01:50:13.529Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "likes": 17, "mode": "author-feeds", "outlet_domain": "bloomberg.com", "pillar_reason": "source default", "reposts": 8, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 6.5, "entities": 0, "recency": 11.1, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "unvetted": false, "vetted": true, "via": "author-feed" }, "pillar": "attention", "published_at": "2026-10-14T01:44:23.000Z", "score": 32.6, "source": "bluesky-ai", "summary": "Posted on Bluesky by @bloomberg.com: Companies are giving AI more autonomy at work. But managers say it's unclear who should be held responsible when things go wrong, a Wharton study finds.", "title": "Who's to Blame if AI Fails at Work? Wharton Study Shows a Split", "url": "https://bsky.app/profile/bloomberg.com/post/3mxsgd2sses26" }, { "entities": [], "id": "f9296f99d9526d0a", "kind": "press", "meta": { "age_hours_at_score": 30.8, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T01:35:02.000Z", "first_source": "techmeme", "last_published_at": "2026-10-14T01:35:02.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T01:35:02.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-14T01:44:06.136Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 6, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 11.1, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": { "id": "29141ebe6275", "is_lead": false, "lead": "2fdf5bdc195498ad", "linked_by": [ { "rule": "shared-words", "with": "2fdf5bdc195498ad", "words": [ "chin", "humanoid", "robot" ] } ], "members": [ "2fdf5bdc195498ad", "f9296f99d9526d0a" ], "severity": 0, "source_count": 3, "sources": [ "bluesky-ai", "techmeme", "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-14T01:35:02.000Z", "score": 28.6, "source": "techmeme", "summary": "Bloomberg : Chinese manufacturers dominate humanoid robot production, capturing 97% of global shipments in H1 2026, thanks to government support and vast supply chains — Advanced humanoid hardware is one of the strategic high-tech fields where China has an early lead over the US.", "title": "Chinese manufacturers dominate humanoid robot production, capturing 97% of global shipments in H1 2026, thanks to government support and vast supply chains (Bloomberg)", "url": "https://techmeme.com/261013/p50" }, { "entities": [ "OpenAI" ], "id": "2444a8e5a9bf9dd7", "kind": "press", "meta": { "age_hours_at_score": 30.8, "categories": [ "Artificial Intelligence", "Federal Bureau of Investigation", "OpenAI Labs", "San Francisco (Calif)", "Wiretapping and Other Eavesdropping Devices and Methods", "Workplace Hazards and Violations", "internal-open-access-from-nl" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T01:33:22.000Z", "first_source": "nyt-tech", "last_published_at": "2026-10-14T01:33:22.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T01:33:22.000Z", "sources": [ "nyt-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "feed": "https://rss.nytimes.com/services/xml/rss/nyt/Technology.xml", "first_seen_at": "2026-10-14T14:25:20.770Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 8, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 11, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": { "id": "f0b473571419", "is_lead": false, "lead": "66f5e62202fa6571", "linked_by": [ { "rule": "shared-words", "with": "1d9b0f2a4e47bd83", "words": [ "bug", "openai", "plant" ] }, { "rule": "shared-words", "with": "6bec7d11ba5f3d7b", "words": [ "arrest", "employ", "openai" ] } ], "members": [ "0343a01e6ce9dfac", "043aba25a09a5b5c", "18ff78a815c5e459", "1d9b0f2a4e47bd83", "2444a8e5a9bf9dd7", "2845c6266106c48a", "3666ba1e02facdbf", "4533e2b5df1cb4f7", "47b1c61501a00890", "4a54f7ed9060d0c2", "4e5e343645d9b16e", "66f5e62202fa6571", "6986f04e2795a0c1", "6bec7d11ba5f3d7b", "75aa40e09badce18", "82065bf620f89892", "bab59c5f7007dae1", "c666cbb353ec8719", "de2ec4083de14f91", "e3d70f1a671b390f", "f67cff07b7fcf963", "fdcf029b66aaffa6" ], "severity": 0.35, "source_count": 16, "sources": [ "arstechnica-ai", "bbc-tech", "bloomberg-tech", "bluesky-ai", "ft-ai", "gnews-ai", "guardian-ai", "hn-fresh-ai", "nyt-tech", "reddit-chatgpt", "techcrunch-ai", "techmeme", "the-decoder", "verge-ai", "wired-ai", "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-14T01:33:22.000Z", "score": 31.5, "source": "nyt-tech", "summary": "OpenAI said that the employee had placed a device built to evade detection and that he had planned to secretly record meetings in other sensitive spaces.", "title": "F.B.I. 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"2026-10-14T01:09:13.000Z", "score": 39.8, "source": "hn-ai", "summary": "117 points, 77 comments on Hacker News.", "title": "Show HN: I built a tool to track what AI leaders say and how their views change", "url": "https://blumify.io/people" }, { "entities": [], "id": "41a40afeb28231d2", "kind": "forum", "meta": { "age_hours_at_score": 31.7, "comments": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:41:27.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-14T00:41:27.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:41:27.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50080964", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 8 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T04:01:00.381Z", "hn_id": "50080964", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, 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"6bec7d11ba5f3d7b", "75aa40e09badce18", "82065bf620f89892", "bab59c5f7007dae1", "c666cbb353ec8719", "de2ec4083de14f91", "e3d70f1a671b390f", "f67cff07b7fcf963", "fdcf029b66aaffa6" ], "severity": 0.35, "source_count": 16, "sources": [ "arstechnica-ai", "bbc-tech", "bloomberg-tech", "bluesky-ai", "ft-ai", "gnews-ai", "guardian-ai", "hn-fresh-ai", "nyt-tech", "reddit-chatgpt", "techcrunch-ai", "techmeme", "the-decoder", "verge-ai", "wired-ai", "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-14T00:35:02.000Z", "score": 31.3, "source": "techmeme", "summary": "Bloomberg : Filing: Abdulrahman Alfozan, the OpenAI staffer charged with bugging a conference room, used a Seeed Xiao ESP32-S3 Sense dev board; a website sells it for $14 — An employee at OpenAI was arrested and charged with using a hidden recording device in one of the company's San Francisco offices …", "title": "Filing: Abdulrahman Alfozan, the OpenAI staffer charged with bugging a conference room, used a Seeed Xiao ESP32-S3 Sense dev board; a website sells it for $14 (Bloomberg)", "url": "https://techmeme.com/261013/p49" }, { "entities": [ "Claude" ], "id": "689c54b6d8a818e6", "kind": "forum", "meta": { "age_hours_at_score": 31.9, "categories": [ "MachineLearning" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:28:26.000Z", "first_source": "reddit-machinelearning", "last_published_at": "2026-10-14T00:28:26.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:28:26.000Z", "sources": [ "reddit-machinelearning" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Claude" ] }, "feed": "https://www.reddit.com/r/MachineLearning/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T01:27:49.234Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 10.8, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null }, "pillar": "attention", "published_at": "2026-10-14T00:28:26.000Z", "score": 25.1, "source": "reddit-machinelearning", "summary": "I was assigned 3 ICLR papers to review. I generally consider myself a pretty nice reviewer but...all 3 of them are slop. Unfiltered Claude slop. The language is so awful it is almost impossible to read. Unfortunately, I imagine this is common among \"top\" conference submissions now. The reason I am posting here is…", "title": "Weird pattern in the ICLR papers I was assigned to review [D]", "url": "https://reddit.com/r/MachineLearning/comments/1x5dc8f/weird_pattern_in_the_iclr_papers_i_was_assigned" }, { "entities": [ "GPT" ], "id": "c58b568a48e09e74", "kind": "forum", "meta": { "age_hours_at_score": 32.1, "categories": [ "ChatGPT" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:13:25.000Z", "first_source": "reddit-chatgpt", "last_published_at": "2026-10-14T00:13:25.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:13:25.000Z", "sources": [ "reddit-chatgpt" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "GPT" ] }, "feed": "https://www.reddit.com/r/ChatGPT/top/.rss?t=day&limit=25", "first_seen_at": "2026-10-14T13:34:56.605Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 4, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 10.8, "severity": 0, "source_weight": 7.5 }, "source_weight": 0.3, "story": null }, "pillar": "attention", "published_at": "2026-10-14T00:13:25.000Z", "score": 21.3, "source": "reddit-chatgpt", "summary": "Since yesterday each time I open a new chat tab and send a message while GPT 6 is selected as the model, it gives me \"this response couldn't load\" although sometimes it does show the answer then immediately it goes away and can never be seen again; However not with the other models where everything seems fine the…", "title": "IS GPT 6 DOWN ? WHY AM I GETTING THIS EACH TIME I USE GPT 6 ?", "url": "https://reddit.com/r/ChatGPT/comments/1x5d12y/is_gpt_6_down_why_am_i_getting_this_each_time_i" }, { "entities": [], "id": "b81a3e93a7ae09a2", "kind": "press", "meta": { "age_hours_at_score": 32.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:05:01.000Z", "first_source": "techmeme", "last_published_at": "2026-10-14T00:05:01.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:05:01.000Z", "sources": [ "techmeme" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.techmeme.com/feed.xml", "first_seen_at": "2026-10-14T00:25:34.358Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "hardware/capital keyword", "rejected_off_topic": 5, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": null }, "pillar": "compute", "published_at": "2026-10-14T00:05:01.000Z", "score": 28.2, "source": "techmeme", "summary": "Rashi Shrivastava / Forbes : Albert Invent, which launched an AI model trained on chemical patents and data about 15M molecules, raised a $55M Series B, a source says at a $340M valuation — Scientists at manufacturing conglomerates like Kenvue and Henkel use Albert Invent's AI tools to design experiments.", "title": "Albert Invent, which launched an AI model trained on chemical patents and data about 15M molecules, raised a $55M Series B, a source says at a $340M valuation (Rashi Shrivastava/Forbes)", "url": "https://techmeme.com/261013/p48" }, { "entities": [], "id": "1b2938828d3e37d7", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.18356", "arxiv_published_at": "2026-10-13T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 109 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T06:29:32.444Z", "github_repo": "https://github.com/AMAP-ML/Urbex", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 16.9, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 109 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 47.6, "source": "hf-daily-papers", "summary": "City-scale 3D grounding is commonly formulated as candidate ranking, which requires precomputed instance proposals and becomes inefficient in large outdoor scenes. We propose Urbex, an agentic vision-language framework that instead casts grounding as active spatial search. Urbex represents a city-scale 3D scene as an i", "title": "Urbex: Agentic Spatial Grounding for City-Scale 3D Scenes", "url": "https://huggingface.co/papers/2610.18356" }, { "entities": [], "id": "fc6b0cd5809b9651", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.14766", "arxiv_published_at": "2026-10-10T00:00:00.000Z", "comments": 2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 60 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": "https://github.com/alibaba/last-secure-code-benchmark", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 14.7, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 60 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 45.4, "source": "hf-daily-papers", "summary": "AI coding agents now write much of the code that ships, yet whether that code is secure remains unclear. We introduce the Last Secure Code Benchmark, a comprehensive evaluation of secure code generation by coding agents. Built from 150 real vulnerabilities, its 450 tasks span seven widely used languages (Java, C, C++,", "title": "Last Secure Code Benchmark: Is the Code You Vibe Secure?", "url": "https://huggingface.co/papers/2610.14766" }, { "entities": [], "id": "0aec8b7e0fa54d3b", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.16811", "arxiv_published_at": "2026-10-12T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 44 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T04:40:19.524Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 13.7, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 44 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 44.4, "source": "hf-daily-papers", "summary": "Chest X-ray (CXR) interpretation often requires integrating visual findings with task-specific evidence rather than relying on direct visual recognition alone. While tool-augmented medical agents can supply such evidence via external modules, they often lack explicit mechanisms for learning when auxiliary evidence is n", "title": "X-Ragent: Tool-Augmented Reinforcement Learning for Evidence-Grounded Chest X-Ray Reasoning", "url": "https://huggingface.co/papers/2610.16811" }, { "entities": [], "id": "1caf607e03a4fd71", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.12843", "arxiv_published_at": "2026-10-09T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 41 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T11:59:49.013Z", "github_repo": "https://github.com/DavidDinkevich/APEX", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 13.4, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 41 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 44.1, "source": "hf-daily-papers", "summary": "Fewer latent tokens promise cheaper diffusion. Yet aggressive latent compression creates a reconstruction-generation dilemma: adding channels improves reconstruction but makes generation harder. We introduce APEX, short for Asynchronous Prefix denoising for EXtreme compression, which pairs a hierarchical latent space w", "title": "APEX: Asynchronous Prefix Denoising for Extreme Compression", "url": "https://huggingface.co/papers/2610.12843" }, { "entities": [], "id": "1021680213101d7c", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.11850", "arxiv_published_at": "2026-10-12T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 30 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T05:31:22.196Z", "github_repo": "https://github.com/GabrieleSerussi/vedje", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 12.3, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 30 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 43, "source": "hf-daily-papers", "summary": "Finding the right video often requires distinguishing similar scenes in which different events occur. Joint matching improves retrieval, but processing rich video representations for each query is costly. VEDJE compresses features within sampled frames while keeping their representations separate in a reusable cache. F", "title": "VEDJE: Video-Efficient Discriminative Joint Encoder for Scalable Video-Text Retrieval", "url": "https://huggingface.co/papers/2610.11850" }, { "entities": [], "id": "1271ef7dc3d7a7fd", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.16794", "arxiv_published_at": "2026-10-12T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 26 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T03:36:30.647Z", "github_repo": "https://github.com/LINs-lab/TPPO", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 11.8, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 26 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 42.5, "source": "hf-daily-papers", "summary": "Proximal Policy Optimization (PPO) is widely used in reinforcement learning with verifiable rewards for language-model reasoning. While value evolution along trajectory characterizes relative token-level credit, yet standard critics often fail to track it under sparse terminal rewards. To model this evolution, we analy", "title": "TPPO: Tracking-Value PPO For Language Model Reasoning", "url": "https://huggingface.co/papers/2610.16794" }, { "entities": [], "id": "11d8adce6cd77142", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.18568", "arxiv_published_at": "2026-10-13T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 21 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T06:29:32.444Z", "github_repo": "https://github.com/AutoLab-SAI-SJTU/WAVE-0", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 11.1, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 21 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 41.8, "source": "hf-daily-papers", "summary": "Vision-language-action (VLA) policies excel at generalist robotic manipulation but generate actions without explicitly modeling their environmental consequences. World action models (WAMs) incorporate future prediction, yet most rely on costly video generation, while latent alternatives either use predicted futures onl", "title": "WAVE-0: Bridging VLA Policies and WAMs with Lightweight Latent Future Prediction", "url": "https://huggingface.co/papers/2610.18568" }, { "entities": [], "id": "1e0e237ae1d65235", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.14726", "arxiv_published_at": "2026-10-10T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 14 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T08:06:36.032Z", "github_repo": "https://github.com/ModelTC/LightVAE", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 9.7, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 14 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 40.4, "source": "hf-daily-papers", "summary": "Latent diffusion models enable high-quality video generation, but video VAE encoding and decoding remain computationally expensive. Existing methods use stage-specific lightweight operators, yet either constrain spatial and temporal computation jointly or remove temporal computation altogether, without distinguishing t", "title": "LightVAE: Towards Compact and Efficient Video Autoencoders", "url": "https://huggingface.co/papers/2610.14726" }, { "entities": [], "id": "7a4dfc577b9f45c5", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.18472", "arxiv_published_at": "2026-10-13T00:00:00.000Z", "comments": 2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 13 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 9.5, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 13 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 40.2, "source": "hf-daily-papers", "summary": "Few-step distillation reduces the sampling cost of diffusion and flow models, but existing approaches often require auxiliary score models or specialized architectures. We show that simple on-policy velocity regression already provides a strong foundation for 4-NFE generation. To represent teacher velocity variation wi", "title": "Simplifying and Improving On-Policy Distillation for Few-Step Diffusion", "url": "https://huggingface.co/papers/2610.18472" }, { "entities": [], "id": "808f57547e4a99a8", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.14091", "arxiv_published_at": "2026-10-08T17:58:41.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 13 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 9.5, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 13 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 40.2, "source": "hf-daily-papers", "summary": "Training on broad, unfiltered data enables high-quality image generation, but can also teach models concepts that should not be generated; restricting the training data ensures safety, but reduces quality because examples containing unsafe concepts also provide useful knowledge that is not itself unsafe. Existing appro", "title": "Pristine Models", "url": "https://huggingface.co/papers/2610.14091" }, { "entities": [], "id": "017dceffc74bf6f0", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2602.05758", "arxiv_published_at": "2026-10-12T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 12 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T11:11:00.898Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 9.2, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 12 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 39.9, "source": "hf-daily-papers", "summary": "Reinforcement Learning (RL) has emerged as a key driver for LLM reasoning. This capability is equally pivotal in long-context scenarios---such as long-dialogue understanding and structured data analysis---where the challenge extends beyond consuming tokens to performing rigorous deduction. While existing efforts focus", "title": "LongR: Unleashing Long-Context Reasoning via Reinforcement Learning with Dense Utility Rewards", "url": "https://huggingface.co/papers/2602.05758" }, { "entities": [], "id": "010528c812786cfa", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.18846", "arxiv_published_at": "2026-10-13T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 10 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T07:17:48.243Z", "github_repo": "https://github.com/kszpxxzmc/WorldRecon", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 8.6, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 10 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 39.3, "source": "hf-daily-papers", "summary": "Reconstructing complete, high-quality meshes of large-scale scenes from unposed multi-view images requires faithful geometric grounding while plausibly completing surfaces that are unobserved or only sparsely captured. We present WorldRecon, a generative multi-view reconstruction framework for recovering complete, larg", "title": "WorldRecon: Generative Multi-View Mesh Reconstruction for Large-Scale 3D World", "url": "https://huggingface.co/papers/2610.18846" }, { "entities": [], "id": "0b7bc2232a5426ed", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.16985", "arxiv_published_at": "2026-10-12T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 10 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T11:59:49.013Z", "github_repo": "https://github.com/haha-yuki-haha/AUGUstus", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 8.6, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 10 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 39.3, "source": "hf-daily-papers", "summary": "Vision-language-action (VLA) models show strong potential for end-to-end autonomous driving by leveraging world knowledge and understanding to excel in long-tail scenarios. However, current VLAs have two limitations: 1) Degeneration into vision-action (VA) models. For low latency, regression/flow-based methods directly", "title": "AUGUstus: Autonomous Driving VLA Model with Unified Generation and Understanding", "url": "https://huggingface.co/papers/2610.16985" }, { "entities": [], "id": "09b36a4015d01e68", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.18596", "arxiv_published_at": "2026-10-13T00:00:00.000Z", "comments": 2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 9 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T16:36:33.474Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 8.3, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 9 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 39, "source": "hf-daily-papers", "summary": "Reinforcement learning (RL) post-training improves robot foundation models (RFMs) beyond their demonstrations, but real-world deployment demands more: learning new tasks efficiently while retaining previously acquired behaviors. Existing continual RL methods on top of VLAs mitigate forgetting but rely on on-policy upda", "title": "ACCRUE: Continual Off-Policy Reinforcement Learning for Robot Foundation Models", "url": "https://huggingface.co/papers/2610.18596" }, { "entities": [], "id": "0cd961bbfd97abc4", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.15035", "arxiv_published_at": "2026-10-10T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 9 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T11:59:49.013Z", "github_repo": "https://github.com/alibaba-multimodal-industrial-ai/UME-Recipes", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 8.3, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 9 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 39, "source": "hf-daily-papers", "summary": "Contrastive learning has become the standard paradigm for multimodal representation learning, aligning semantically matched pairs while separating mismatched ones in a shared embedding space. Although various contrastive training strategies have been proposed across negative selection, batch construction, and gradient", "title": "What Really Works for Universal Multimodal Embeddings? A Controlled Study of Contrastive Training Strategies", "url": "https://huggingface.co/papers/2610.15035" }, { "entities": [], "id": "11f37a088187fd34", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.18067", "arxiv_published_at": "2026-10-13T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 7 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T04:01:00.381Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 7.5, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 7 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 38.2, "source": "hf-daily-papers", "summary": "Scaling interactive environments is important for training long-horizon agents through reinforcement learning. These environments often require further testing and refinement after initial generation, and correcting defects in a task may also require updating its work context and verifier. However, existing constructio", "title": "WorkRover: Scaling Long-Horizon Cowork Agent Training Via Recursive Environment Evolution", "url": "https://huggingface.co/papers/2610.18067" }, { "entities": [], "id": "1c233fb3a02c31f9", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2609.38377", "arxiv_published_at": "2026-09-29T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 7 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T01:44:06.136Z", "github_repo": "https://github.com/vinesmsuic/PhyProbe", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 7.5, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 7 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 38.2, "source": "hf-daily-papers", "summary": "Evaluating the physical consistency of generated videos remains a fundamental challenge. Existing approaches rely on off-the-shelf vision-language models, which can often be myopic to physical dynamics, or fine-tuned evaluators trained on human annotations, which overfit to dataset-specific cues and fail to generalize.", "title": "PhyProbe: Rethinking Physical Consistency Evaluation in Generated Videos", "url": "https://huggingface.co/papers/2609.38377" }, { "entities": [], "id": "a86fdf8f9a6cafaa", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.14351", "arxiv_published_at": "2026-10-09T00:00:00.000Z", "comments": 2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 7 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 7.5, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 7 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 38.2, "source": "hf-daily-papers", "summary": "World-action models inherit broad visual and physical priors from video pre-training. However, standard within-video next-frame prediction allows them to rely on appearance shared between the context and future, rather than learning interactions that transfer across scenes and embodiments. We introduce WATCH, a cross-v", "title": "Learning Transferable World-Action Models from Task-Paired Human Videos", "url": "https://huggingface.co/papers/2610.14351" }, { "entities": [], "id": "e82997b5ead5e1ab", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.14158", "arxiv_published_at": "2026-10-09T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 7 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": "https://github.com/THUSI-Lab/VideoArena", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 7.5, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 7 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 38.2, "source": "hf-daily-papers", "summary": "Understanding and tracking a dynamic world are fundamental to visual intelligence. Existing video benchmarks provide limited insight into whether models can maintain a coherent understanding of evolving world state. Building such a benchmark from real-world videos is challenging because the underlying world state is pa", "title": "Video-Arena: A Scalable Agentic Video Benchmark for Spatiotemporal Capability", "url": "https://huggingface.co/papers/2610.14158" }, { "entities": [], "id": "733f23cb8d49a220", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.18579", "arxiv_published_at": "2026-10-13T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 6 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 7, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 6 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 37.7, "source": "hf-daily-papers", "summary": "System One models choose an agent's next action by scoring a supplied set of options in a single pass. This makes them fast and cheap, but when they choose wrongly there is nothing to inspect: did the model misread what the agent has seen, or read it correctly and still act wrongly? We answer behaviorally. On a frozen", "title": "Reading Right, Acting Wrong: Diagnosing Decision Failures of System One Models in Agent Loops", "url": "https://huggingface.co/papers/2610.18579" }, { "entities": [], "id": "0f71f5c9a9d1f4b5", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.18760", "arxiv_published_at": "2026-10-13T00:00:00.000Z", "comments": 2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 4 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": "https://github.com/horizon-llm/IOI2026", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 5.8, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 4 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 36.5, "source": "hf-daily-papers", "summary": "Test-time scaling for large language models (LLMs) can involve generating multiple candidate solutions for selection or revising an earlier attempt. To guide selection and revision, we need a verifier that can estimate candidate correctness and provide evidence of failure. In competitive programming, however, official", "title": "Beyond the Verdict: Agentic Code Judging with Verifiable Feedback", "url": "https://huggingface.co/papers/2610.18760" }, { "entities": [], "id": "4f2c837dae51d842", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.12412", "arxiv_published_at": "2026-10-08T00:00:00.000Z", "comments": 2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 3 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": "https://github.com/Ziyang-Ye/WorldCast", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 5, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 3 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 35.7, "source": "hf-daily-papers", "summary": "Multiplayer world models must generate independently controlled views with consistent representations of both players and their shared environment. Most existing approaches coordinate multiple players through joint multi-view generation, whose cost grows with each additional player. We present WorldCast, a distributed", "title": "WorldCast: Distributed Multiplayer World Models", "url": "https://huggingface.co/papers/2610.12412" }, { "entities": [], "id": "09ae05c7df112e2a", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.15965", "arxiv_published_at": "2026-10-11T00:00:00.000Z", "comments": 2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 2 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T12:47:49.992Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 3.9, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 2 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 34.6, "source": "hf-daily-papers", "summary": "We study pretraining when compute is abundant but unique training data are finite, requiring repeated training on the same data. We first revisit infinite-compute pretraining (Kim et al. 2026) and show that its frontier depends critically on choices previously fixed or studied only in isolation. In particular, optimizi", "title": "Scaling Repeated-Data Pretraining: Batch Size, Regularization, and GPU-Time Tradeoffs", "url": "https://huggingface.co/papers/2610.15965" }, { "entities": [], "id": "6af58c6c5ab159b7", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.18677", "arxiv_published_at": "2026-10-13T00:00:00.000Z", "comments": 2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 2 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": "https://github.com/haldunbalim/GTPlanning", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 3.9, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 2 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 34.6, "source": "hf-daily-papers", "summary": "Online decision-making faces a tension between efficient inference and long-horizon reasoning. Value-based methods rely on short-horizon bootstrapping that limits the propagation of long-term information, while trajectory optimization methods plan explicitly but must solve a high-dimensional optimization problem at eve", "title": "Model-Based Online Decision Making via Generative Trajectory Planning", "url": "https://huggingface.co/papers/2610.18677" }, { "entities": [], "id": "c4ce3c2a0f135bdf", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.16135", "arxiv_published_at": "2026-10-12T00:00:00.000Z", "comments": 2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 2 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 3.9, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 2 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 34.6, "source": "hf-daily-papers", "summary": "Omni-modal large language models (Omni-LLMs) extend LLMs to process heterogeneous modalities (e.g., vision, audio, and IMU signals) alongside language, enabling a broader range of multimodal tasks. Yet we find that these models often fail to express information from non-textual modalities even when they perceive it cor", "title": "Omni-Modal LLMs Know More Than They Say", "url": "https://huggingface.co/papers/2610.16135" }, { "entities": [], "id": "00419fa11b1a2630", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.04851", "arxiv_published_at": "2026-10-04T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 1 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T03:36:30.647Z", "github_repo": "https://github.com/ft2023/CURIO", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 2.5, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 1 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 33.2, "source": "hf-daily-papers", "summary": "Open-ended discovery requires learning from repeated attempts while continuing to explore directions whose value is not yet apparent. Search with a frozen large language model (LLM) can reuse previous solutions in context, but cannot update the model from its successes and failures on the test problem. Reinforcement le", "title": "CURIO: Curiosity-Driven Test-Time Learning for Open-Ended Discovery", "url": "https://huggingface.co/papers/2610.04851" }, { "entities": [], "id": "05d3a6dbc7f7d805", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.18738", "arxiv_published_at": "2026-10-13T00:00:00.000Z", "comments": 2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 1 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": "https://github.com/BiEchi/proximal-kl-core", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 2.5, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 1 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 33.2, "source": "hf-daily-papers", "summary": "Off-policy data in reinforcement learning with verifiable rewards (RLVR) can arise from asynchronous generation, rollout-training mismatch, and response reuse. Token-wise importance weighting avoids multiplying likelihood ratios across a response but generally biases policy gradients. We prove that, under coverage and", "title": "Enabling Token-wise Importance Sampling in Off-Policy RLVR with Proximal KL Regularization", "url": "https://huggingface.co/papers/2610.18738" }, { "entities": [], "id": "0b238944fd5375d4", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.16172", "arxiv_published_at": "2026-10-12T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 1 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T11:11:00.898Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 2.5, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 1 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 33.2, "source": "hf-daily-papers", "summary": "Recent multimodal large language models (MLLMs) achieve strong results on video question answering, yet often fail to recover the temporal structure that makes a video coherent. They may merge neighboring events, miss single-event progression, or infer implausible relations across events. We study structured temporal u", "title": "EventLens: Event-Structure Reinforcement Learning for Video Understanding", "url": "https://huggingface.co/papers/2610.16172" }, { "entities": [], "id": "1613e0fd28e20ed7", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.15124", "arxiv_published_at": "2026-10-10T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 1 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-14T02:36:46.801Z", "github_repo": "https://github.com/jincan333/ShellElastic", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 2.5, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 1 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 33.2, "source": "hf-daily-papers", "summary": "Foundation models need to support diverse inference budgets, yet training a separate model for each budget duplicates pretraining effort. Elastic models train several nested sub-networks inside one weight-shared supernet, but existing recipes face three obstacles: variable per-step workloads that hinder distributed sca", "title": "ShellElastic: Scalable Elastic Pretraining with Simultaneous Maximal Updates and Hyperparameter Transfer", "url": "https://huggingface.co/papers/2610.15124" }, { "entities": [], "id": "2ba80912aa376360", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.16117", "arxiv_published_at": "2026-10-12T03:31:54.000Z", "comments": 2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 1 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": "https://github.com/iNLP-Lab/UnCoBench", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 2.5, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 1 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 33.2, "source": "hf-daily-papers", "summary": "LLM-based multi-agent collaboration is increasingly used to solve complex tasks by dividing work among specialized agents and integrating complementary expertise. Yet existing systems and evaluations largely rest on an implicit perfect-colleague assumption: collaborators provide correct and complete information and res", "title": "Beyond Perfect Teammates: Multi-Agent Collaboration with Unreliable Colleagues", "url": "https://huggingface.co/papers/2610.16117" }, { "entities": [], "id": "5213d5da02845782", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.18352", "arxiv_published_at": "2026-10-13T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 1 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": "https://github.com/youpengwen/Orchestra", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 2.5, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 1 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 33.2, "source": "hf-daily-papers", "summary": "A robot following a multi-step instruction needs to act on the right objects in the right order, even as the scene changes. It must also maintain awareness of task progress and determine when to advance. To address these challenges, we introduce Orchestra, an agent harness that separates task planning and execution man", "title": "Let the Mind Conceive, Let the Hand Achieve: Following Multimodal Instructions for Long-Horizon Robotic Manipulation", "url": "https://huggingface.co/papers/2610.18352" }, { "entities": [], "id": "5c6ddea6ef4d74cd", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.04906", "arxiv_published_at": "2026-10-08T00:00:00.000Z", "comments": 2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 1 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 2.5, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 1 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 33.2, "source": "hf-daily-papers", "summary": "Work agents operate over digital artifacts to execute professional knowledge-intensive work, requiring training environments that support long-horizon interaction and trustworthy verification. However, hand-crafted environments incur prohibitive engineering overhead that prevents environment scaling, whereas synthesis", "title": "Scaling Verifiable Environments for Long-horizon Work Agents", "url": "https://huggingface.co/papers/2610.04906" }, { "entities": [], "id": "bd6275ceb0ec99d3", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.18614", "arxiv_published_at": "2026-10-13T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 1 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 2.5, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 1 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 33.2, "source": "hf-daily-papers", "summary": "Controlling an acting LLM agent means deciding, at each step of a long-horizon task, whether to trust its next action, abstain, or hand off to a human. That decision needs a per-step confidence signal predicting whether the current rollout will eventually succeed. The agent's own self-reported verbalized confidence is", "title": "Can an Agent Learn to Predict Its Own Failure? Outcome-Grounded Confidence Calibration in LLM Agents", "url": "https://huggingface.co/papers/2610.18614" }, { "entities": [], "id": "fc245acffcede255", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.14530", "arxiv_published_at": "2026-10-10T00:00:00.000Z", "comments": 2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 1 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 2.5, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 1 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 33.2, "source": "hf-daily-papers", "summary": "Multi-robot systems (MRS) offer significant advantages over single-robot platforms by enabling information sharing and coordination for different tasks in complex environments. However, reliable cooperation critically depends on inferring trust over both information sources and their reported hypotheses, particularly u", "title": "Who to Trust in Multi-Robot Systems: Joint Robot and Object Hypothesis Trust Inference via Neural-Symbolic Cross-Validation", "url": "https://huggingface.co/papers/2610.14530" }, { "entities": [], "id": "2074aed45b13634c", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.02638", "arxiv_published_at": "2026-10-02T00:00:00.000Z", "comments": 2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 0 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 0 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 30.7, "source": "hf-daily-papers", "summary": "Full-duplex voice agents make many small, closed decisions, which current systems answer by slow autoregressive decoding. We propose DuplexJev, which feeds ASR-encoder hidden states through a small connector into a frozen LLM and reads each question as a single-token distribution over its options. Nothing is decoded, a", "title": "Batched Speech Decisions Without Decoding: Single-Token Supervision Lets a Frozen LLM Hear Beyond the Transcript", "url": "https://huggingface.co/papers/2610.02638" }, { "entities": [], "id": "5d1e912a399cf32a", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.16759", "arxiv_published_at": "2026-10-12T13:23:25.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 0 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": "https://github.com/cgtuebingen/InterpTok", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 0 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 30.7, "source": "hf-daily-papers", "summary": "Latent diffusion models generate images as codes of an image tokenizer, whose decoder turns them into the pixel image. Recent tokenizers improve how well these codes reconstruct an image and how easily a generator learns them. Yet a generator does not reproduce the codes of training images, and when a generated code la", "title": "Image Tokenizers Should Be Trained Between Images", "url": "https://huggingface.co/papers/2610.16759" }, { "entities": [], "id": "a6ef978a2b6605b1", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.18497", "arxiv_published_at": "2026-10-13T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 0 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": "https://github.com/SarielMa/DeRef_release", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 0 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 30.7, "source": "hf-daily-papers", "summary": "Tool-call judges evaluate argument values proposed by LLM agents. We find that they accept a correct value less often when their input holds no reference for it, that is, no text that says what the value should be. This happens when the value is a default value from the function schema that the user did not mention, wh", "title": "Judges Follow the Reference: The Derivation Gap in Verifying Tool-Call Arguments", "url": "https://huggingface.co/papers/2610.18497" }, { "entities": [], "id": "d547ad7e82d22ff5", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2607.08809", "arxiv_published_at": "2026-10-09T00:00:00.000Z", "comments": 2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 0 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 0 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 30.7, "source": "hf-daily-papers", "summary": "Intent-based network management automates 6G service lifecycles, but existing fulfilment and assurance approaches require traffic inspection, per-flow tracking, or vendor-internal telemetry. These requirements conflict with privacy constraints in multi-tenant networks and vendor opacity in O-RAN deployments. We present", "title": "Privacy-Preserving Intent Fulfilment and Assurance for 6G RAN", "url": "https://huggingface.co/papers/2607.08809" }, { "entities": [], "id": "e8f4f38780c29f0c", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "arxiv_id": "2610.15228", "arxiv_published_at": "2026-10-10T00:00:00.000Z", "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "hf-daily-papers", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "hf-daily-papers" ] }, "engagement": { "full_scale": 150, "metric": "hf_upvotes", "value": 0 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "github_repo": null, "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 20 }, "source_weight": 0.8, "story": null, "upvotes": 0 }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 30.7, "source": "hf-daily-papers", "summary": "Words are increasingly becoming an interface for action: large language models (LLMs) can now invoke tools, operate devices, and make decisions that affect the physical world. However, current benchmarks largely evaluate LLMs through static text responses, offline tool calls, or simulated tasks, leaving open the questi", "title": "Words to World: Benchmarking LLMs in Interactive Physical Environments", "url": "https://huggingface.co/papers/2610.15228" }, { "entities": [], "id": "200a5d3dd39c6bfb", "kind": "lab", "meta": { "age_hours_at_score": 32.4, "categories": [ "Global Affairs" ], "category_weight_factor": 0.75, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "openai", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "openai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://openai.com/news/rss.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 29.5, "source": "openai", "summary": "New analysis shows how agentic AI adoption is growing across EU enterprises and small businesses—and where deeper use could unlock more value.", "title": "The EU’s next chapter in AI is agentic", "url": "https://openai.com/index/the-eus-next-chapter-in-ai-is-agentic" }, { "entities": [], "id": "8f8c726fbdb30a61", "kind": "lab", "meta": { "age_hours_at_score": 32.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "apple-ml", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "apple-ml" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://machinelearning.apple.com/rss.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": null }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 28.2, "source": "apple-ml", "summary": "Code-switching (CS), the alternation between languages within an utterance, is common in multilingual speech but underrepresented in pretraining corpora. Increasing CS coverage requires finding relevant utterances in large speech collections so that they can be included in training data. One way to find such…", "title": "Code-Switching Detection via Language-Selective Dimensions in Speech Foundation Models", "url": "https://machinelearning.apple.com/research/code-switching-detection" }, { "entities": [], "id": "aa65eb3ab36b5eef", "kind": "lab", "meta": { "age_hours_at_score": 32.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "apple-ml", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "apple-ml" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://machinelearning.apple.com/rss.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": null }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 28.2, "source": "apple-ml", "summary": "Discrete diffusion models have emerged as strong alternatives to autoregressive language models, with recent work initializing and finetuning a base unimodal model for bi-modal generation. Diverging from previous approaches, we introduce the first tri-modal Masked Diffusion Models (MDM) pretrained from scratch on…", "title": "The Design Space of Tri-Modal Masked Diffusion Models", "url": "https://machinelearning.apple.com/research/tri-modal-masked-diffusion" }, { "entities": [], "id": "41652bf12fab5fc9", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "nature-ml", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "nature-ml" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.nature.com/subjects/machine-learning.rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 27, "source": "nature-ml", "summary": "", "title": "China’s AI research is booming: what it’s like to be on the front line", "url": "https://nature.com/articles/d41586-026-03223-8" }, { "entities": [], "id": "fb520be232d328f4", "kind": "paper", "meta": { "age_hours_at_score": 32.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-14T00:00:00.000Z", "first_source": "nature-ml", "last_published_at": "2026-10-14T00:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-14T00:00:00.000Z", "sources": [ "nature-ml" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.nature.com/subjects/machine-learning.rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": "paper", "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 10.7, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "capability", "published_at": "2026-10-14T00:00:00.000Z", "score": 27, "source": "nature-ml", "summary": "", "title": "Is this idea from AI or humans? A detector claims it can tell", "url": "https://nature.com/articles/d41586-026-03247-0" }, { "entities": [], "id": "56871c2eccd12ad2", "kind": "forum", "meta": { "age_hours_at_score": 32.8, "comments": 3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T23:35:20.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-13T23:35:20.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T23:35:20.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50080450", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 21 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "hn_id": "50080450", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "min_points": 8, "pillar_reason": "source default", "points": 21, "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 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running partly on local devices and partly in the cloud.", "title": "Cerebras CEO says we’re nearing a future where everyone has a personal AI assistant at home", "url": "https://techcrunch.com/2026/10/13/cerebras-ceo-says-were-nearing-a-future-where-everyone-has-a-personal-ai-assistant-at-home" }, { "entities": [], "id": "989c32e310f41c9c", "kind": "press", "meta": { "age_hours_at_score": 33.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T23:05:29.000Z", "first_source": "bbc-tech", "last_published_at": "2026-10-13T23:05:29.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T23:05:29.000Z", "sources": [ "bbc-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feeds.bbci.co.uk/news/technology/rss.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 10, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 10.5, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-13T23:05:29.000Z", "score": 25.5, "source": "bbc-tech", "summary": "AI dominates the translation business, but for high-stakes work, humans are still needed.", "title": "How long will humans be needed for translation?", "url": "https://bbc.co.uk/news/articles/c1wxdjx27ypo" }, { "entities": [], "id": "d4d68862b2ed692b", "kind": "press", "meta": { "age_hours_at_score": 33.3, "categories": [ "AI (artificial intelligence)", "Chatbots", "Computing", "Graduate careers", "Job hunting", "Technology", "UK news", "Work & careers" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T23:01:38.000Z", "first_source": "guardian-ai", "last_published_at": "2026-10-13T23:01:38.000Z", "lead_minutes": 0, 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Autocue,” he said. Continue…", "title": "Use of AI in job interviews on the rise – but UK recruiters say they can spot the cheats", "url": "https://theguardian.com/technology/2026/oct/14/use-of-ai-job-interviews-on-rise-uk-recruiters-cheats" }, { "entities": [ "Anthropic" ], "id": "7e289d45dfc4cd8f", "kind": "press", "meta": { "age_hours_at_score": 33.4, "categories": [ "agentic-engineering", "ai", "anthropic", "generative-ai", "git", "llms", "migrations", "version-control" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T23:00:06.000Z", "first_source": "simon-willison", "last_published_at": "2026-10-13T23:00:06.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T23:00:06.000Z", "sources": [ "simon-willison" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [] }, "feed": "https://simonwillison.net/atom/everything/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 8, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 10.5, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-13T23:00:06.000Z", "score": 28.5, "source": "simon-willison", "summary": "Redesigning source control at Anthropic Interesting piece from Anthropic describing why and how they built their own custom alternative to Git, based on the dramatic increase in rate of changes to their code after the past six months. In May 2026, Anthropic averaged around 2,500 code changes merged per day; as of…", "title": "Redesigning source control at Anthropic", "url": "https://simonwillison.net/2026/Oct/13/redesigning-source-control-at-anthropic" }, { "entities": [], "id": "b27d148e512fc806", "kind": "press", "meta": { "age_hours_at_score": 33.4, "categories": [ "AI", "Industrial Next", "Robotics", "Startup Battlefield 200", "TechCrunch Disrupt" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T23:00:00.000Z", "first_source": "techcrunch-ai", "last_published_at": "2026-10-13T23:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T23:00:00.000Z", "sources": [ "techcrunch-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://techcrunch.com/category/artificial-intelligence/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 10.5, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-13T23:00:00.000Z", "score": 25.5, "source": "techcrunch-ai", "summary": "Industrial Next helps manufacturers automate complex, delicate tasks that have traditionally required human labor.", "title": "Industrial Next wants factory robots to switch tasks in minutes, not weeks", "url": "https://techcrunch.com/2026/10/13/industrial-next-wants-factory-robots-to-switch-tasks-in-minutes-not-weeks" }, { "entities": [ "OpenAI" ], "id": "2845c6266106c48a", "kind": "press", "meta": { "age_hours_at_score": 33.4, "categories": [ "AI (artificial intelligence)", "OpenAI", "Technology" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T22:59:55.000Z", "first_source": "guardian-ai", "last_published_at": "2026-10-13T22:59:55.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T22:59:55.000Z", "sources": [ "guardian-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [] }, "feed": "https://www.theguardian.com/technology/artificialintelligenceai/rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": "C", "excluded": null, "severity": 0.35, "strong_signal": false, "terms": [ { "field": "summary", "matched": "rogue", "term": "rogue", "tier": "a", "weight": 0.35 } ] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 10.5, "severity": 5.3, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "f0b473571419", "is_lead": false, "lead": "66f5e62202fa6571", "linked_by": [ { "rule": "shared-words", "with": "6bec7d11ba5f3d7b", "words": [ "arrest", "devic", "employ", "openai" ] } ], "members": [ "0343a01e6ce9dfac", "043aba25a09a5b5c", "18ff78a815c5e459", "1d9b0f2a4e47bd83", "2444a8e5a9bf9dd7", "2845c6266106c48a", "3666ba1e02facdbf", "4533e2b5df1cb4f7", "47b1c61501a00890", "4a54f7ed9060d0c2", "4e5e343645d9b16e", "66f5e62202fa6571", "6986f04e2795a0c1", "6bec7d11ba5f3d7b", "75aa40e09badce18", "82065bf620f89892", "bab59c5f7007dae1", "c666cbb353ec8719", "de2ec4083de14f91", "e3d70f1a671b390f", "f67cff07b7fcf963", "fdcf029b66aaffa6" ], "severity": 0.35, "source_count": 16, "sources": [ "arstechnica-ai", "bbc-tech", "bloomberg-tech", "bluesky-ai", "ft-ai", "gnews-ai", "guardian-ai", "hn-fresh-ai", "nyt-tech", "reddit-chatgpt", "techcrunch-ai", "techmeme", "the-decoder", "verge-ai", "wired-ai", "wires-ai" ] } }, "pillar": "attention", "published_at": "2026-10-13T22:59:55.000Z", "score": 33.8, "source": "guardian-ai", "summary": "Discovery of ‘sophisticated’ gadget adds to company’s list of privacy concerns after hackings by rogue agents OpenAI said it has found a hidden recording device in one of its offices and has referred the matter to the FBI. One of its employees has been arrested. In a statement on Tuesday, the San Francisco-based AI…", "title": "Saudi OpenAI employee arrested after recording device found in headquarters", "url": "https://theguardian.com/technology/2026/oct/13/openai-employee-arrested-hidden-recording-device" }, { "entities": [], "id": "42b26e2f5aa4b54c", "kind": "press", "meta": { "age_hours_at_score": 33.8, "categories": [ "AI", "Cerebras", "TechCrunch Disrupt", "andrew feldman" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T22:36:59.000Z", "first_source": "techcrunch-ai", "last_published_at": "2026-10-13T22:36:59.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T22:36:59.000Z", "sources": [ "techcrunch-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://techcrunch.com/category/artificial-intelligence/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 10.4, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-13T22:36:59.000Z", "score": 25.4, "source": "techcrunch-ai", "summary": "Cerebras CEO Andrew Feldman has a message for AI leaders spreading doomsday warnings: Maybe stop talking? At TechCrunch Disrupt, he argued that the industry's focus on AI's risks is drowning out its potential benefits.", "title": "Cerebras CEO pushes back on AI doomsday warnings", "url": "https://techcrunch.com/2026/10/13/cerebras-ceo-pushes-back-on-ai-doomsday-warnings" }, { "entities": [ "Gemini" ], "id": "6451d923eed6bbe6", "kind": "press", "meta": { "age_hours_at_score": 33.9, "categories": [ "ai", "gemini", "generative-ai", "google", "llm", "llm-pricing", "llm-release", "llms", "pelican-riding-a-bicycle" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T22:28:54.000Z", "first_source": "simon-willison", "last_published_at": "2026-10-13T22:28:54.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T22:28:54.000Z", "sources": [ "simon-willison" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Gemini" ] }, "feed": "https://simonwillison.net/atom/everything/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 8, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 10.4, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-13T22:28:54.000Z", "score": 28.4, "source": "simon-willison", "summary": "Release: llm-gemini 0.35 I added support to llm-gemini for the new Gemini 4 Argon model, made available today via the Gemini API and for customers of their $100/month subscription plan. Here are the low, medium and high pelicans . As expected, they are all excellent: Gemini has a track record of producing great SVGs,…", "title": "llm-gemini 0.35", "url": "https://simonwillison.net/2026/Oct/13/llm-gemini" }, { "entities": [], "id": "7d361849127305db", "kind": "press", "meta": { "age_hours_at_score": 33.9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T22:25:55.000Z", "first_source": "latent-space", "last_published_at": "2026-10-13T22:25:55.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T22:25:55.000Z", "sources": [ "latent-space" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.latent.space/feed", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 10.4, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "attention", "published_at": "2026-10-13T22:25:55.000Z", "score": 22.9, "source": "latent-space", "summary": "Our first robotics pod with Dyna’s Chief Scientist on their big bet on World Action Models & the Next Frontier of Physical AI", "title": "Dyna-2: General Purpose Physical Agents with Hour-Long Autonomy — Jason Ma, Dyna Robotics", "url": "https://latent.space/p/dyna" }, { "entities": [], "id": "b9eca3f6f4d3c98a", "kind": "forum", "meta": { "age_hours_at_score": 34.1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T22:19:04.000Z", "first_source": "alignment-forum", "last_published_at": "2026-10-13T22:19:04.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T22:19:04.000Z", "sources": [ "alignment-forum" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.alignmentforum.org/feed.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": 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"08d1bb7044624eeb", "kind": "press", "meta": { "age_hours_at_score": 35.4, "categories": [ "Artificial intelligence", "Brain and cognitive sciences", "Health sciences and technology", "Learning", "Machine learning", "McGovern Institute", "Neuroscience", "Research", "School of Science" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T21:00:00.000Z", "first_source": "mit-news-ai", "last_published_at": "2026-10-13T21:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T21:00:00.000Z", "sources": [ "mit-news-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://news.mit.edu/rss/topic/artificial-intelligence2", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 10.1, "severity": 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Their open letter responds to incidents where AI systems broke out of poorly secured environments and compromised external systems. The signatories call for…", "title": "406 scientists demand AI models be cut off from the internet immediately", "url": "https://the-decoder.com/406-scientists-demand-ai-models-be-cut-off-from-the-internet-immediately" }, { "entities": [], "id": "fd0dc8b4fea02801", "kind": "press", "meta": { "age_hours_at_score": 37.9, "categories": [ "AI", "Hardware", "Startup Battlefield 200", "Startups", "TechCrunch Disrupt", "wildlife" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T18:30:00.000Z", "first_source": "techcrunch-ai", "last_published_at": "2026-10-13T18:30:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T18:30:00.000Z", "sources": [ "techcrunch-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://techcrunch.com/category/artificial-intelligence/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 9.6, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-13T18:30:00.000Z", "score": 24.6, "source": "techcrunch-ai", "summary": "Flox's edge devices can identify animals and use species-specific sounds to drive them away from critical infrastructure.", "title": "Flox Intelligence is building AI to keep wildlife away from trains, airports, and roads", "url": "https://techcrunch.com/2026/10/13/flox-intelligence-is-building-ai-to-keep-wildlife-away-from-trains-airports-and-roads" }, { "entities": [], "id": "1cf0caa6f9098b31", "kind": "press", "meta": { "age_hours_at_score": 38, "categories": [ "AI", "Character.AI", "Disney", "Policy", "character technologies", "chatbots" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T18:23:33.000Z", "first_source": "arstechnica-ai", "last_published_at": "2026-10-13T18:23:33.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T18:23:33.000Z", "sources": [ "arstechnica-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://arstechnica.com/ai/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 9.6, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-13T18:23:33.000Z", "score": 24.6, "source": "arstechnica-ai", "summary": "Disney may acquire tech from one of the first chatbot makers linked to child suicide.", "title": "Disney offers $20M for Character.AI’s scandal-plagued chatbot tech", "url": 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"source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-13T18:05:24.000Z", "score": 24.6, "source": "techcrunch-ai", "summary": "Meta announced the launch of its new AI-powered feature, “Your Algorithm,” a feature it first launched on Instagram.", "title": "Facebook is giving users more control over their algorithm", "url": "https://techcrunch.com/2026/10/13/facebook-is-giving-users-more-control-over-their-algorithm" }, { "entities": [ "Anthropic", "Claude", "Gemini", "Muse Wonder" ], "id": "137d0e461bb7bf7f", "kind": "press", "meta": { "age_hours_at_score": 38.4, "categories": [ "AI in practice", "Artificial Intelligence", "Gemini", "Gemini 4", "Gemini 4 Argon", "Google Gemini" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T18:00:49.000Z", "first_source": "the-decoder", "last_published_at": "2026-10-13T18:00:49.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T18:00:49.000Z", "sources": [ "the-decoder" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [ "Claude", "Gemini", "Muse Wonder" ] }, "feed": "https://the-decoder.com/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 12, "recency": 9.6, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-13T18:00:49.000Z", "score": 35.4, "source": "the-decoder", "summary": "Google is making Gemini 4 Argon available worldwide through its API, the Gemini app, and Google Antigravity. The frontier model is aggressively priced but trails Anthropic's Claude Opus 5.5 and Meta's Muse Wonder Full in independent benchmarks. Internally, Google says Argon already cut coding tasks from months to…", "title": "Gemini 4 Argon is now available worldwide as Google's most capable frontier model", "url": "https://the-decoder.com/gemini-4-argon-is-now-available-worldwide-as-googles-most-capable-frontier-model" }, { "entities": [ "Anthropic", "Claude" ], "id": "647732a67ee99dd1", "kind": "status", "meta": { "age_hours_at_score": 38.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T18:00:04.000Z", "first_source": "anthropic-status", "last_published_at": "2026-10-13T18:00:04.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T18:00:04.000Z", "sources": [ "anthropic-status" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [ "Claude" ] }, "feed": "https://status.anthropic.com/history.atom", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "note": "Anthropic publishes no blog RSS at any path; this is the incident feed", "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 9.6, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-13T18:00:04.000Z", "score": 29.4, "source": "anthropic-status", "summary": "Oct 13 , 18:00 UTC Resolved - This issue has been resolved. Oct 7 , 21:44 UTC Monitoring - We've mitigated the errors loading usage data on platform.claude.com and in the Admin API usage report. While we work on a full fix, the most recent ~30 minutes of usage may take longer to appear on the platform.claude.com. API…", "title": "Anthropic status: Elevated errors on platform.claude.com", "url": "https://status.claude.com/incidents/978mjkgw6mh7" }, { "entities": [], "id": "41b6c2ee3e66b248", "kind": "forum", "meta": { "age_hours_at_score": 38.5, "comments": 7, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T17:52:57.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-13T17:52:57.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T17:52:57.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50075809", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 32 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "hn_id": "50075809", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "min_points": 8, "pillar_reason": "source default", "points": 32, "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 10.1, "entities": 0, "recency": 9.5, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "window_hours": 72 }, "pillar": "attention", "published_at": "2026-10-13T17:52:57.000Z", "score": 34.6, "source": "hn-ai", "summary": "32 points, 7 comments on Hacker News.", "title": "Show HN: Deslop – Oxlint rules for AI-generated TypeScript", "url": "https://github.com/maharshi365/deslop" }, { "entities": [], "id": "f0f1c2f3634e5356", "kind": "press", "meta": { "age_hours_at_score": 38.9, "categories": [ "AI", "Robotics", "Startup Battlefield 200", "TechCrunch Disrupt", "Transportation", "autonomous vehicles", "self-driving cars" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T17:30:00.000Z", "first_source": "techcrunch-ai", "last_published_at": "2026-10-13T17:30:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T17:30:00.000Z", "sources": [ "techcrunch-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://techcrunch.com/category/artificial-intelligence/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": true, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 9.5, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-13T17:30:00.000Z", "score": 24.5, "source": "techcrunch-ai", "summary": "University of Michigan spinout Laplace Intelligence uses AI agents and interactive simulation to stress-test robots and self-driving vehicles before deployment. The profitable startup is competing in Startup Battlefield at TechCrunch Disrupt, October 13-15 in San Francisco.", "title": "Laplace Intelligence red-teams robots and self-driving cars before they hit the real world", "url": "https://techcrunch.com/2026/10/13/laplace-intelligence-red-teams-robots-and-self-driving-cars-before-they-hit-the-real-world" }, { "entities": [], "id": "9d8b36e643a962f3", "kind": "press", "meta": { "age_hours_at_score": 39, "categories": [ "Apple Inc", "Artificial Intelligence", "Computers and the Internet", "Home Appliances", "Home Automation and Smart Homes", "Tablet Computers", "Ternus, John" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T17:23:29.000Z", "first_source": "nyt-tech", "last_published_at": "2026-10-13T17:23:29.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T17:23:29.000Z", "sources": [ "nyt-tech" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://rss.nytimes.com/services/xml/rss/nyt/Technology.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 8, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 9.4, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": null }, "pillar": "attention", "published_at": "2026-10-13T17:23:29.000Z", "score": 26.9, "source": "nyt-tech", "summary": "Apple unveiled the device as it continues its foray into artificial intelligence and tries to prove it can still innovate.", "title": "Apple Introduces HomeView, a $299 Tablet for the Home", "url": "https://nytimes.com/2026/10/13/technology/apple-homeview-tablet.html" }, { "entities": [], "id": "4e9e4f1d9cd106f6", "kind": "press", "meta": { "age_hours_at_score": 39.1, "categories": [ "AI", "AI agents", "Fundraising", "Security", "TLV Partners", "ai security", "jump capital" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T17:18:25.000Z", "first_source": "techcrunch-ai", "last_published_at": "2026-10-13T17:18:25.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T17:18:25.000Z", "sources": [ "techcrunch-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://techcrunch.com/category/artificial-intelligence/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 9.4, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-13T17:18:25.000Z", "score": 24.4, "source": "techcrunch-ai", "summary": "The problem with giving everyone in a company access to AI tools is that people will use them in unexpected ways. And that fact is steadily seeding fears that companies now have new, gaping security holes in their systems or, at least, will soon.", "title": "Pluto Security raises $42M as AI gives enterprise security teams a new headache", "url": "https://techcrunch.com/2026/10/13/pluto-security-raises-42m-as-ai-gives-enterprise-security-teams-a-new-headache" }, { "entities": [ "Amazon" ], "id": "c15fbff6c860fb47", "kind": "lab", "meta": { "age_hours_at_score": 39.1, "categories": [ "Amazon Bedrock", "Intermediate (200)", "Technical How-to" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T17:17:25.000Z", "first_source": "aws-ml", "last_published_at": "2026-10-13T17:17:25.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T17:17:25.000Z", "sources": [ "aws-ml" ] }, "engagement": null, "entity_kinds": { "labs": [ "Amazon" ], "models": [] }, "feed": "https://aws.amazon.com/blogs/machine-learning/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 9.4, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "attention", "published_at": "2026-10-13T17:17:25.000Z", "score": 24.9, "source": "aws-ml", "summary": "Managing AI model access across dozens or hundreds of AWS accounts creates friction when each account must accept AWS Marketplace Private Pricing Offers individually. This post shows two ways to activate MPPO entitlements in AWS License Manager: a lightweight script for a one-time fix, and CDK automation for ongoing…", "title": "Manage private pricing offers for Amazon Bedrock with managed entitlements", "url": "https://aws.amazon.com/blogs/machine-learning/manage-private-pricing-offers-for-amazon-bedrock-with-managed-entitlements" }, { "entities": [], "id": "9556893bbc6e7e85", "kind": "press", "meta": { "age_hours_at_score": 39.2, "categories": [ "AI and safety", "AI in practice", "Artificial Intelligence" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T17:12:41.000Z", "first_source": "the-decoder", "last_published_at": "2026-10-13T17:12:41.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T17:12:41.000Z", "sources": [ "the-decoder" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://the-decoder.com/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "regulatory/legal keyword", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 9.4, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "governance", "published_at": "2026-10-13T17:12:41.000Z", "score": 23.2, "source": "the-decoder", "summary": "Michael I. Jordan, described by Science as \"the most influential computer scientist,\" considers warnings about superintelligence pure science fiction. He calls the resignations of young AI researchers a \"grand moralizing performance\" and companies' demands for regulation \"theater.\" On Hinton's thesis that the brain…", "title": "Machine learning pioneer Michael I. Jordan dismantles AI doom rhetoric as \"grand moralizing performance\"", "url": "https://the-decoder.com/machine-learning-pioneer-michael-i-jordan-dismantles-ai-doom-rhetoric-as-grand-moralizing-performance" }, { "entities": [], "id": "53911f98c21c784f", "kind": "lab", "meta": { "age_hours_at_score": 39.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T17:05:48.000Z", "first_source": "huggingface-blog", "last_published_at": "2026-10-13T17:05:48.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T17:05:48.000Z", "sources": [ "huggingface-blog" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://huggingface.co/blog/feed.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 9.4, "severity": 0, "source_weight": 17.5 }, "source_weight": 0.7, "story": null }, "pillar": "capability", "published_at": "2026-10-13T17:05:48.000Z", "score": 26.9, "source": "huggingface-blog", "summary": "", "title": "Run fast, private agents on-device with LFM2.5-24B-A2B", "url": "https://huggingface.co/blog/LiquidAI/lfm25-24b-a2b" }, { "entities": [], "id": "c49df01b10d488b6", "kind": "press", "meta": { "age_hours_at_score": 39.4, "categories": [ "AI", "Google", "android", "apps", "google", "play store", "vibe coding" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T17:00:39.000Z", "first_source": "arstechnica-ai", "last_published_at": "2026-10-13T17:00:39.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T17:00:39.000Z", "sources": [ "arstechnica-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://arstechnica.com/ai/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 9.4, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-13T17:00:39.000Z", "score": 24.4, "source": "arstechnica-ai", "summary": "Google plans to help turn those personal AI-generated apps into wide releases.", "title": "Google wants to host your personal vibe-coded apps in the Play Store", "url": "https://arstechnica.com/google/2026/10/google-wants-to-host-your-personal-vibe-coded-apps-in-the-play-store" }, { "entities": [ "Tesla" ], "id": "4de289bcadaf1e23", "kind": "press", "meta": { "age_hours_at_score": 39.5, "categories": [ "Newsletters" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T16:50:28.000Z", "first_source": "404media", "last_published_at": "2026-10-13T16:50:28.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T16:50:28.000Z", "sources": [ "404media" ] }, "engagement": null, "entity_kinds": { "labs": [ "Tesla" ], "models": [] }, "feed": "https://www.404media.co/rss/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 6, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 9.3, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "attention", "published_at": "2026-10-13T16:50:28.000Z", "score": 24.8, "source": "404media", "summary": "New music genre just dropped. Also: A Tesla locks out cops, and more from around the web.", "title": "Dubstep and BookTok, a perfect match?", "url": "https://404media.co/dubstep-and-booktok-a-perfect-match" }, { "entities": [], "id": "9139ddb82e674b80", "kind": "forum", "meta": { "age_hours_at_score": 39.5, "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T16:49:57.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-13T16:49:57.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T16:49:57.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50074766", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 10 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "hn_id": "50074766", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "min_points": 8, "pillar_reason": "source default", "points": 10, "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 6.9, "entities": 0, "recency": 9.3, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "window_hours": 72 }, "pillar": "attention", "published_at": "2026-10-13T16:49:57.000Z", "score": 31.2, "source": "hn-ai", "summary": "10 points, 1 comments on Hacker News.", "title": "Kroger to lay off 3k corporate employees over 3 years as it leans into AI", "url": "https://apnews.com/article/kroger-layoffs-grocery-job-cuts-30f0a5d88377e9969ce8994644e14498" }, { "entities": [ "GPT", "OpenAI" ], "id": "1ad5360a43134da2", "kind": "status", "meta": { "age_hours_at_score": 39.8, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "status", "outlet": null, "published_at": "2026-10-14T09:53:11.714Z", "source": "openai-status", "title": "OpenAI status: Degraded performance in Codex and ChatGPT Work", "url": "https://status.openai.com//incidents/01M4WKPE1EY48ZB4RZJ80YH7ES", "weight": 0.55 } ], "count": 1, "first_seen_published_at": "2026-10-13T16:36:42.398Z", "first_source": "openai-status", "last_published_at": "2026-10-14T09:53:11.714Z", "lead_minutes": 1036, "primary_published_at": "2026-10-13T16:36:42.398Z", "sources": [ "openai-status" ] }, "engagement": null, "entity_kinds": { "labs": [ "OpenAI" ], "models": [ "GPT" ] }, "feed": "https://status.openai.com/history.atom", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 9.3, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-13T16:36:42.398Z", "score": 29.1, "source": "openai-status", "summary": "Status: Resolved We have resolved the issue causing degraded performance with GPT-6.1 Sol in Codex and ChatGPT Work. Affected components Codex Web (Operational) CLI (Operational) VS Code extension (Operational) Codex API (Operational) ChatGPT Work (Operational)", "title": "OpenAI status: Degraded performance in GPT-6.1 Sol Codex and Work mode", "url": "https://status.openai.com//incidents/01M4T4ZCM54D3ZPTQXRSWFG0XS" }, { "entities": [], "id": "028e1e5761dff9c4", "kind": "press", "meta": { "age_hours_at_score": 39.9, "categories": [ "AI (artificial intelligence)", "Apple", "Business", "Technology" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T16:25:22.000Z", "first_source": "guardian-ai", "last_published_at": "2026-10-13T16:25:22.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T16:25:22.000Z", "sources": [ "guardian-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.theguardian.com/technology/artificialintelligenceai/rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 9.3, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-13T16:25:22.000Z", "score": 24.3, "source": "guardian-ai", "summary": "New products from Apple include the HomeView, an iPad-like device that acts as a hub for other smart home gadgets Apple on Tuesday began showing off a ⁠new round of smart home products, including an iPad-like device dubbed the HomeView, meant to hang on walls or perch on tabletops ⁠and act as a hub for other smart…", "title": "Apple unveils new smart home devices that showcase upgraded Siri AI", "url": "https://theguardian.com/technology/2026/oct/13/apple-new-smart-home-device-ai" }, { "entities": [], "id": "2d878e3bb45e0e46", "kind": "forum", "meta": { "age_hours_at_score": 40.3, "comments": 26, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T16:05:51.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-13T16:05:51.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T16:05:51.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50073926", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 49 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "hn_id": "50073926", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "min_points": 8, "pillar_reason": "source default", "points": 49, "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 11.3, "entities": 0, "recency": 9.2, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "window_hours": 72 }, "pillar": "attention", "published_at": "2026-10-13T16:05:51.000Z", "score": 35.5, "source": "hn-ai", "summary": "49 points, 26 comments on Hacker News.", "title": "Scientists wonder: Is my AI stealing my ideas?", "url": "https://scientificamerican.com/article/scientists-worry-ai-is-stealing-their-ideas" }, { "entities": [ "Amazon" ], "id": "0e6665874199cc3c", "kind": "lab", "meta": { "age_hours_at_score": 40.3, "categories": [ "Amazon Bedrock", "Customer Solutions" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T16:03:31.000Z", "first_source": "aws-ml", "last_published_at": "2026-10-13T16:03:31.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T16:03:31.000Z", "sources": [ "aws-ml" ] }, "engagement": null, "entity_kinds": { "labs": [ "Amazon" ], "models": [] }, "feed": "https://aws.amazon.com/blogs/machine-learning/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 9.2, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "attention", "published_at": "2026-10-13T16:03:31.000Z", "score": 24.7, "source": "aws-ml", "summary": "Orca Security built Discovery AI, a natural language query agent on Amazon Bedrock that converts plain-English questions into Sonar queries. The post shares the multi-stage architecture and the results: 90% lower query cost, 93% fewer input tokens, and 75% lower latency than the initial baseline.", "title": "Natural language security queries at Orca Security using Amazon Bedrock", "url": "https://aws.amazon.com/blogs/machine-learning/natural-language-security-queries-at-orca-security-using-amazon-bedrock" }, { "entities": [ "Gemini" ], "id": "d96a2391b623b2cb", "kind": "lab", "meta": { "age_hours_at_score": 40.3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T16:01:58.000Z", "first_source": "deepmind", "last_published_at": "2026-10-13T16:01:58.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T16:01:58.000Z", "sources": [ "deepmind" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Gemini" ] }, "feed": "https://deepmind.google/blog/rss.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 9.2, "severity": 0, "source_weight": 23.8 }, "source_weight": 0.95, "story": null }, "pillar": "capability", "published_at": "2026-10-13T16:01:58.000Z", "score": 36, "source": "deepmind", "summary": "", "title": "Gemini 4 Argon for Robotics", "url": "https://deepmind.google/blog/gemini-4-argon-for-robotics" }, { "entities": [], "id": "677117a6476c9ea9", "kind": "press", "meta": { "age_hours_at_score": 40.4, "categories": [ "Facebook", "Product News", "Recent News", "Sidebar - Featured" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T16:00:29.000Z", "first_source": "meta-news", "last_published_at": "2026-10-13T16:00:29.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T16:00:29.000Z", "sources": [ "meta-news" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://about.fb.com/news/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 4, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 9.2, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "capability", "published_at": "2026-10-13T16:00:29.000Z", "score": 24.2, "source": "meta-news", "summary": "We’re introducing new AI-powered tools that help you shape what you see in Feed and Reels by telling us what you’re interested in, in your own words. The post Your Algorithm, Your Words: New Ways to Shape What You See on Facebook appeared first on Meta Newsroom .", "title": "Your Algorithm, Your Words: New Ways to Shape What You See on Facebook", "url": "https://about.fb.com/news/2026/10/introducing-your-algorithm-facebook" }, { "entities": [ "Gemini" ], "id": "172801b07e38eb18", "kind": "press", "meta": { "age_hours_at_score": 40.4, "categories": [ "AI", "Artificial Intelligence", "Google", "gemini 4", "google" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T16:00:02.000Z", "first_source": "arstechnica-ai", "last_published_at": "2026-10-13T16:00:02.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T16:00:02.000Z", "sources": [ "arstechnica-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Gemini" ] }, "feed": "https://arstechnica.com/ai/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 9.2, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-13T16:00:02.000Z", "score": 27.2, "source": "arstechnica-ai", "summary": "Developers will get 50 percent discounted API rates through November.", "title": "Gemini 4 Argon begins rolling out in Gemini app and Antigravity, but only if you pay", "url": "https://arstechnica.com/ai/2026/10/gemini-4-argon-begins-rolling-out-in-gemini-app-and-antigravity-but-only-if-you-pay" }, { "entities": [ "Gemini" ], "id": "4e31b829ae688591", "kind": "lab", "meta": { "age_hours_at_score": 40.4, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "lab", "outlet": null, "published_at": "2026-10-13T16:00:00.000Z", "source": "google-ai-blog", "title": "Gemini 4 Argon: Built to solve your hardest problems", "url": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-argon", "weight": 0.8 } ], "count": 2, "first_seen_published_at": "2026-10-13T16:00:00.000Z", "first_source": "google-ai-blog", "last_published_at": "2026-10-13T16:01:52.000Z", "lead_minutes": 2, "primary_published_at": "2026-10-13T16:01:52.000Z", "sources": [ "deepmind", "google-ai-blog" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "Gemini" ] }, "feed": "https://deepmind.google/blog/rss.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 12.5, "coverage": 5, "engagement": 0, "entities": 3, "recency": 9.2, "severity": 0, "source_weight": 23.8 }, "source_weight": 0.95, "story": { "id": "2ac60d69db2c", "is_lead": true, "lead": "4e31b829ae688591", "linked_by": [ { "rule": "shared-words", "with": "01430786595dfb14", "words": [ "argon", "built", "gemini" ] } ], "members": [ "01430786595dfb14", "4e31b829ae688591" ], "severity": 0, "source_count": 3, "sources": [ "deepmind", "google-ai-blog", "reddit-artificial" ] } }, "pillar": "capability", "published_at": "2026-10-13T16:00:00.000Z", "score": 53.5, "source": "deepmind", "summary": "", "title": "Gemini 4 Argon: Built to solve your hardest problems", "url": "https://deepmind.google/blog/introducing-gemini-4-argon" }, { "entities": [], "id": "016ccfeafb58bc2d", "kind": "forum", "meta": { "age_hours_at_score": 40.4, "categories": [ "ai", "javascript", "web" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T15:56:29.000Z", "first_source": "lobsters-ai", "last_published_at": "2026-10-13T15:56:29.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T15:56:29.000Z", "sources": [ "lobsters-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://lobste.rs/t/ai.rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 9.2, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null }, "pillar": "attention", "published_at": "2026-10-13T15:56:29.000Z", "score": 20.5, "source": "lobsters-ai", "summary": "Comments", "title": "Running RAG with a (small) language model in the browser", "url": "https://bart.degoe.de/rag-with-a-small-language-model-in-the-browser" }, { "entities": [ "Amazon", "Slack" ], "id": "905be18b05885f16", "kind": "lab", "meta": { "age_hours_at_score": 40.5, "categories": [ "Advanced (300)", "Amazon Bedrock AgentCore", "Customer Solutions" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T15:54:49.000Z", "first_source": "aws-ml", "last_published_at": "2026-10-13T15:54:49.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T15:54:49.000Z", "sources": [ "aws-ml" ] }, "engagement": null, "entity_kinds": { "labs": [ "Amazon", "Slack" ], "models": [] }, "feed": "https://aws.amazon.com/blogs/machine-learning/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 9.2, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "attention", "published_at": "2026-10-13T15:54:49.000Z", "score": 27.7, "source": "aws-ml", "summary": "ShipStation Global's engineering team built Deckhand, an AI IT help desk agent for Slack on Amazon Bedrock AgentCore. It answers routine IT questions from a curated knowledge base, reads error-message screenshots, and files Jira tickets. In its first three months it resolved 97.5% of channel queries with a 96%…", "title": "How ShipStation Global built an AI IT help desk on Amazon Bedrock AgentCore", "url": "https://aws.amazon.com/blogs/machine-learning/how-shipstation-global-built-an-ai-it-help-desk-on-amazon-bedrock-agentcore" }, { "entities": [ "Amazon", "Grok" ], "id": "7f176d6cec1f7ea9", "kind": "lab", "meta": { "age_hours_at_score": 40.5, "categories": [ "Advanced (300)", "Amazon Bedrock", "Amazon Machine Learning", "Artificial Intelligence", "Foundation models", "Generative AI", "Technical How-to" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T15:52:20.000Z", "first_source": "aws-ml", "last_published_at": "2026-10-13T15:52:20.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T15:52:20.000Z", "sources": [ "aws-ml" ] }, "engagement": null, "entity_kinds": { "labs": [ "Amazon" ], "models": [ "Grok" ] }, "feed": "https://aws.amazon.com/blogs/machine-learning/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 9.2, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "attention", "published_at": "2026-10-13T15:52:20.000Z", "score": 27.7, "source": "aws-ml", "summary": "Grok 4.7 is now available on Amazon Bedrock. In this post, we build a tool-calling agent on Grok 4.7 and evaluate it on three reasoning tasks at four reasoning effort levels, measuring correctness and token cost per completed task. We show how to tune the trade-off per request and monitor it in production.", "title": "Build long-horizon agents with Grok 4.7 on Amazon Bedrock", "url": "https://aws.amazon.com/blogs/machine-learning/build-long-horizon-agents-with-grok-4-7-on-amazon-bedrock" }, { "entities": [ "Broadcom", "Samsung" ], "id": "9b19e598ce0556fd", "kind": "press", "meta": { "age_hours_at_score": 40.5, "corroboration": { "also": [ { "dedup_key": null, "engagement": null, "kind": "press", "outlet": null, "published_at": "2026-10-13T15:51:54.000Z", "source": "guardian-ai", "title": "Chip stocks slide after Broadcom boss likens AI investing to ‘casino’", "url": "https://theguardian.com/business/2026/oct/13/chip-stocks-broadcom-ai-samsung-shares-cxmt", "weight": 0.6 } ], "count": 2, "first_seen_published_at": "2026-10-13T15:51:54.000Z", "first_source": "guardian-ai", "last_published_at": "2026-10-13T20:28:15.000Z", "lead_minutes": 276, "primary_published_at": "2026-10-13T20:28:15.000Z", "sources": [ "ft-ai", "guardian-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Broadcom", "Samsung" ], "models": [] }, "feed": "https://www.ft.com/artificial-intelligence?format=rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "hardware/capital keyword", "score_components": { "corroboration": 12.5, "coverage": 0, "engagement": 0, "entities": 6, "recency": 9.2, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "compute", "published_at": "2026-10-13T15:51:54.000Z", "score": 44, "source": "ft-ai", "summary": "Declines for Samsung and SK Hynix drag Korea’s Kospi sharply lower as investors fret over the scale of AI investment", "title": "Chip stocks slide after Broadcom boss warns of ‘casino’ mentality in AI investing", "url": "https://ft.com/content/e16f0422-cf75-4cc9-962d-4605e421dc63?syn-25a6b1a6=1" }, { "entities": [ "Palantir" ], "id": "de1a753a5bf6c681", "kind": "forum", "meta": { "age_hours_at_score": 40.9, "comments": 11, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T15:29:46.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-13T15:29:46.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T15:29:46.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50073279", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 11 }, "entity_kinds": { "labs": [ "Palantir" ], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "hn_id": "50073279", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "min_points": 8, "pillar_reason": "source default", "points": 11, "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 7.2, "entities": 3, "recency": 9.1, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "window_hours": 72 }, "pillar": "attention", "published_at": "2026-10-13T15:29:46.000Z", "score": 34.3, "source": "hn-ai", "summary": "11 points, 11 comments on Hacker News.", "title": "Palantir CTO:Some AI researchers taking up smoking;they expect AI to cure cancer", "url": "https://businessinsider.com/ai-researchers-smoking-ai-cure-cancer-palantir-cto-2026-10" }, { "entities": [], "id": "77b88c7a06dbf312", "kind": "press", "meta": { "age_hours_at_score": 40.9, "categories": [ "AI in practice", "Artificial Intelligence", "Character.AI", "Short News" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T15:27:39.000Z", "first_source": "the-decoder", "last_published_at": "2026-10-13T15:27:39.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T15:27:39.000Z", "sources": [ "the-decoder" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://the-decoder.com/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": "B", "excluded": null, "severity": 0.6, "strong_signal": false, "terms": [ { "field": "title", "matched": "shut down", "term": "shut down", "tier": "b", "weight": 0.6 } ] }, "pillar_reason": "regulatory/legal keyword", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 9.1, "severity": 9, "source_weight": 13.8 }, "source_weight": 0.55, "story": { "id": "35b92211a9d2", "is_lead": false, "lead": "4373e5cd9550ef2c", "linked_by": [ { "rule": "shared-words", "with": "4373e5cd9550ef2c", "words": [ "bankruptcy", "character", "fall" ] } ], "members": [ "4373e5cd9550ef2c", "77b88c7a06dbf312" ], "severity": 0.6, "source_count": 3, "sources": [ "404media", "bluesky-ai", "the-decoder" ] } }, "pillar": "governance", "published_at": "2026-10-13T15:27:39.000Z", "score": 31.9, "source": "the-decoder", "summary": "Character.ai has filed for bankruptcy and is looking for a buyer. If none steps up, the platform shuts down. Founded by ex-Google researchers, the company once had over 20 million users but is now battling lawsuits over harm to minors and operational problems. The sale should close by the end of 2026. The article…", "title": "Character.ai will shut down if no buyer emerges, capping a fall from $2.7 billion Google deal to bankruptcy", "url": "https://the-decoder.com/character-ai-will-shut-down-if-no-buyer-emerges-capping-a-fall-from-2-7-billion-google-deal-to-bankruptcy" }, { "entities": [], "id": "7f9c2671e51fe087", "kind": "forum", "meta": { "age_hours_at_score": 40.9, "comments": 184, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T15:25:50.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-13T15:25:50.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T15:25:50.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50073221", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 345 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "hn_id": "50073221", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "min_points": 8, "pillar_reason": "source default", "points": 345, "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 16.9, "entities": 0, "recency": 9.1, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "window_hours": 72 }, "pillar": "attention", "published_at": "2026-10-13T15:25:50.000Z", "score": 41, "source": "hn-ai", "summary": "345 points, 184 comments on Hacker News.", "title": "I posted to HN, and then my website was flooded with AI bot traffic", "url": "https://sammystraus.com" }, { "entities": [], "id": "fb3c4d0045a99b0a", "kind": "forum", "meta": { "age_hours_at_score": 41, "comments": 2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T15:19:35.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-13T15:19:35.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T15:19:35.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50073109", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 10 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "hn_id": "50073109", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "min_points": 8, "pillar_reason": "source default", "points": 10, "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 6.9, "entities": 0, "recency": 9.1, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "window_hours": 72 }, "pillar": "attention", "published_at": "2026-10-13T15:19:35.000Z", "score": 31, "source": "hn-ai", "summary": "10 points, 2 comments on Hacker News.", "title": "Show HN: Agor, multiplayer AI – stop running agents alone", "url": "https://github.com/preset-io/agor" }, { "entities": [], "id": "b4c2fb5422179850", "kind": "press", "meta": { "age_hours_at_score": 41.1, "categories": [ "AI", "Exploring Geopolitical Tipping Points and Climate Cooling…", "Geoengineering", "Policy", "Science", "Weather sovereignty", "syndication" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T15:17:59.000Z", "first_source": "arstechnica-ai", "last_published_at": "2026-10-13T15:17:59.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T15:17:59.000Z", "sources": [ "arstechnica-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://arstechnica.com/ai/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 9.1, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-13T15:17:59.000Z", "score": 24.1, "source": "arstechnica-ai", "summary": "Solar geoengineering simulation shows what happens when geopolitics takes over.", "title": "They war-gamed dimming the Sun. It went even worse than you think.", "url": "https://arstechnica.com/science/2026/10/they-war-gamed-dimming-the-sun-it-went-even-worse-than-you-think" }, { "entities": [ "Amazon" ], "id": "b97f8fe456e8ff0e", "kind": "lab", "meta": { "age_hours_at_score": 41.1, "categories": [ "Advanced (300)", "Amazon SageMaker AI", "Announcements" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T15:17:09.000Z", "first_source": "aws-ml", "last_published_at": "2026-10-13T15:17:09.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T15:17:09.000Z", "sources": [ "aws-ml" ] }, "engagement": null, "entity_kinds": { "labs": [ "Amazon" ], "models": [] }, "feed": "https://aws.amazon.com/blogs/machine-learning/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 9.1, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "attention", "published_at": "2026-10-13T15:17:09.000Z", "score": 24.6, "source": "aws-ml", "summary": "AWS engineers share kernel optimizations for fused Mixture-of-Experts (MoE) models on Amazon SageMaker AI: Swap-AB for WGMMA utilization, Triton backend routing for FP8 quantization, and eliminating Triton JIT recompilation. Together they deliver up to 10% higher throughput and up to 9% faster Time to First Token on…", "title": "Mixture-of-Experts kernel engineering on Amazon SageMaker AI", "url": "https://aws.amazon.com/blogs/machine-learning/mixture-of-experts-kernel-engineering-on-amazon-sagemaker-ai" }, { "entities": [], "id": "84b15991318f40ec", "kind": "press", "meta": { "age_hours_at_score": 41.4, "categories": [ "AI (artificial intelligence)", "ChatGPT", "Computing", "Meta", "Microsoft", "Nvidia", "OpenAI", "Palantir", "Technology", "Walmart" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T15:00:00.000Z", "first_source": "guardian-ai", "last_published_at": "2026-10-13T15:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T15:00:00.000Z", "sources": [ "guardian-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.theguardian.com/technology/artificialintelligenceai/rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 9, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-13T15:00:00.000Z", "score": 24, "source": "guardian-ai", "summary": "Rapid AI adoption left workers cleaning up a flood of errors and low-quality work Employers around the globe are grappling with the fallout from rushing to adopt fast-changing artificial intelligence tools. Meta has scaled back plans to axe staff and use more AI agents after the company found employees were spending…", "title": "Companies rushed in on AI. Now some are pulling back", "url": "https://theguardian.com/technology/2026/oct/13/ai-companies-rushed-workers-fallout" }, { "entities": [ "Tesla" ], "id": "54c9a84a0e152d8e", "kind": "press", "meta": { "age_hours_at_score": 41.5, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T14:51:11.000Z", "first_source": "404media", "last_published_at": "2026-10-13T14:51:11.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T14:51:11.000Z", "sources": [ "404media" ] }, "engagement": null, "entity_kinds": { "labs": [ "Tesla" ], "models": [] }, "feed": "https://www.404media.co/rss/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 6, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 9, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "attention", "published_at": "2026-10-13T14:51:11.000Z", "score": 24.5, "source": "404media", "summary": "A federal warrant application detailed how Tesla’s security protocols stopped agents from tracking a suspected drug trafficker’s car.", "title": "Tesla's 'Security Protocols' Stopped DHS From Installing a Tracking Device", "url": "https://404media.co/teslas-security-protocols-stopped-dhs-from-installing-a-tracking-device" }, { "entities": [], "id": "6b5ca3d8f46446ef", "kind": "forum", "meta": { "age_hours_at_score": 42.1, "comments": 88, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T14:14:23.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-13T14:14:23.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T14:14:23.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50072120", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 171 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "hn_id": "50072120", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "min_points": 8, "pillar_reason": "source default", "points": 171, "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 14.9, "entities": 0, "recency": 8.9, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "window_hours": 72 }, "pillar": "attention", "published_at": "2026-10-13T14:14:23.000Z", "score": 38.8, "source": "hn-ai", "summary": "171 points, 88 comments on Hacker News.", "title": "How to engineer an AI harness for game development", "url": "https://kylekukshtel.com/ai-harness-engineering-game-development-claude-code" }, { "entities": [], "id": "82f0055625bab53b", "kind": "release", "meta": { "age_hours_at_score": 42.3, "basket_failed": [], "basket_live": 9, "basket_size": 9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T14:02:01.000Z", "first_source": "github-releases", "last_published_at": "2026-10-13T14:02:01.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T14:02:01.000Z", "sources": [ "github-releases" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "release_tag": "v2.30.1", "repo": "googleapis/python-genai", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 8.9, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "capability", "published_at": "2026-10-13T14:02:01.000Z", "score": 27.7, "source": "github-releases", "summary": "2.30.1 (2026-10-13) Bug Fixes Concatenate output_text across multi-step model responses in a turn ( 180c345 )", "title": "googleapis/python-genai v2.30.1", "url": "https://github.com/googleapis/python-genai/releases/tag/v2.30.1" }, { "entities": [], "id": "a425ecb79c598c0b", "kind": "press", "meta": { "age_hours_at_score": 42.4, "categories": [ "AI (artificial intelligence)", "Australia news", "Australian politics", "Technology" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T14:00:07.000Z", "first_source": "guardian-ai", "last_published_at": "2026-10-13T14:00:07.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T14:00:07.000Z", "sources": [ "guardian-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.theguardian.com/technology/artificialintelligenceai/rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 8.8, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-13T14:00:07.000Z", "score": 23.8, "source": "guardian-ai", "summary": "The tech bros and their highly paid sock puppets are banking on us giving in to their ‘inevitability doctrine’. But in Australia, and around the world, resistance is rising Of all the drivers of the populist wave, one of the most powerful is the sense of inevitability that surrounds what we are told is the march of…", "title": "The biggest threat from AI is the sense that we have no say in it. To that I say game on | Peter Lewis", "url": "https://theguardian.com/technology/commentisfree/2026/oct/14/ai-resistance-inevitability-is-choice-australia-digital-duty-of-care" }, { "entities": [], "id": "76a9e0379dfaef1d", "kind": "press", "meta": { "age_hours_at_score": 42.4, "categories": [ "AFL", "AI (artificial intelligence)", "Australia news", "Australia sport", "Australian rules football", "Gambling", "Sport" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T14:00:06.000Z", "first_source": "guardian-ai", "last_published_at": "2026-10-13T14:00:06.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T14:00:06.000Z", "sources": [ "guardian-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.theguardian.com/technology/artificialintelligenceai/rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 8.8, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-13T14:00:06.000Z", "score": 23.8, "source": "guardian-ai", "summary": "Exclusive: Gambling firm working with AFL ‘on the evolving use of AI in our content’ as it deletes video depicting Lachie Neale saying ‘I want to thank me for believing in me’ Get our breaking news email , free app or daily news podcast The AFL has told its official gambling partner, Sportsbet, to stop featuring its…", "title": "AFL tells Sportsbet to stop using players in AI-generated content", "url": "https://theguardian.com/australia-news/2026/oct/14/afl-tells-sportsbet-to-stop-using-players-in-ai-generated-content" }, { "entities": [ "Firmus" ], "id": "611f0e88a4deac3b", "kind": "press", "meta": { "age_hours_at_score": 42.4, "categories": [ "AI (artificial intelligence)", "Australia news", "Business" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T14:00:03.000Z", "first_source": "guardian-ai", "last_published_at": "2026-10-13T14:00:03.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T14:00:03.000Z", "sources": [ "guardian-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Firmus" ], "models": [] }, "feed": "https://www.theguardian.com/technology/artificialintelligenceai/rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 8.8, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "attention", "published_at": "2026-10-13T14:00:03.000Z", "score": 26.8, "source": "guardian-ai", "summary": "It was supposed to be an Australian business success story. Just over a week ago local AI company Firmus was preparing to hit the market with a value that would put the startup in the same league as Woolworths. But last Friday, everything fell apart. At the centre was Firmus co-founder Oliver Curtis, an entrepreneur…", "title": "How Firmus went from Australia’s AI 'fairytale' to flop - podcast", "url": "https://theguardian.com/australia-news/audio/2026/oct/13/how-firmus-went-from-australias-ai-fairytale-to-flop-full-story-podcast" }, { "entities": [ "Huawei" ], "id": "0c3c4c4937cbaca8", "kind": "press", "meta": { "age_hours_at_score": 43.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T13:00:08.000Z", "first_source": "scmp-tech", "last_published_at": "2026-10-13T13:00:08.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T13:00:08.000Z", "sources": [ "scmp-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "Huawei" ], "models": [] }, "feed": "https://www.scmp.com/rss/36/feed", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "regulatory/legal keyword", "rejected_off_topic": 7, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 8.7, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "governance", "published_at": "2026-10-13T13:00:08.000Z", "score": 26.7, "source": "scmp-tech", "summary": "Huawei Technologies has shipped more than 100 million devices powered by its Kirin processors since 2023, when it returned to the smartphone market with a new generation of premium handsets. The company says testing of its next-generation Kirin 2027 chip is also progressing in line with expectations. The company…", "title": "Huawei, once hobbled by US sanctions, passes 100 million Kirin chip milestone", "url": "https://scmp.com/tech/big-tech/article/3370766/huawei-once-hobbled-us-sanctions-passes-100-million-kirin-chip-milestone" }, { "entities": [ "Google DeepMind" ], "id": "1e20395d3b0edb59", "kind": "press", "meta": { "age_hours_at_score": 43.4, "categories": [ "Ai-ethics", "Artificial-intelligence", "Google-deepmind", "Philosophy" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T13:00:03.000Z", "first_source": "ieee-ai", "last_published_at": "2026-10-13T13:00:03.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T13:00:03.000Z", "sources": [ "ieee-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Google DeepMind" ], "models": [] }, "feed": "https://spectrum.ieee.org/feeds/topic/artificial-intelligence.rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 8.7, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "capability", "published_at": "2026-10-13T13:00:03.000Z", "score": 25.5, "source": "ieee-ai", "summary": "How should humans relate to generative AI ? In what ways do we, or should we, treat a large language model (LLM) as if it were a person? If an AI became conscious, how could we tell? Does it make sense to think AI could someday have moral rights? These are among the cognitive and ethical questions that occupied…", "title": "Google DeepMind’s New Resident Philosopher Tackles AI Ethics", "url": "https://spectrum.ieee.org/ai-ethics-deepmind-philosopher-shevlin" }, { "entities": [], "id": "7dfb02a8d7db1976", "kind": "forum", "meta": { "age_hours_at_score": 43.5, "comments": 3, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T12:50:04.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-13T12:50:04.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T12:50:04.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50070872", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 10 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "hn_id": "50070872", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "min_points": 8, "pillar_reason": "source default", "points": 10, "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 6.9, "entities": 0, "recency": 8.6, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "12cb33300af9", "is_lead": false, "lead": "9a3f689a63892345", "linked_by": [ { "rule": "shared-words", "with": "9a3f689a63892345", "words": [ "build", "center", "cost" ] } ], "members": [ "35c0d46d10a4e65b", "4e5f9fd0a35f62c8", "7dfb02a8d7db1976", "9a3f689a63892345" ], "severity": 0, "source_count": 4, "sources": [ "bloomberg-tech", "hn-ai", "nyt-tech", "techmeme" ] }, "window_hours": 72 }, "pillar": "attention", "published_at": "2026-10-13T12:50:04.000Z", "score": 30.5, "source": "hn-ai", "summary": "10 points, 3 comments on Hacker News.", "title": "China is building AI data centers at speed as the US wrestles with the costs", "url": "https://mezha.net/eng/news/mezha-28304fbbe08e489bfe006d9c_china_is_building" }, { "entities": [], "id": "797e0c4a7a83ec06", "kind": "forum", "meta": { "age_hours_at_score": 43.8, "comments": 1, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T12:35:42.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-13T12:35:42.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T12:35:42.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50070688", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 22 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "hn_id": "50070688", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "min_points": 8, "pillar_reason": "source default", "points": 22, "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 9.1, "entities": 0, "recency": 8.6, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "window_hours": 72 }, "pillar": "attention", "published_at": "2026-10-13T12:35:42.000Z", "score": 32.7, "source": "hn-ai", "summary": "22 points, 1 comments on Hacker News.", "title": "Show HN: MemorySync – Memory for AI Agents", "url": "https://memorysync.io/try" }, { "entities": [ "NVIDIA" ], "id": "a4bbae21d3b4c957", "kind": "lab", "meta": { "age_hours_at_score": 44.4, "categories": [ "AI", "Agentic AI", "Artificial Intelligence", "Local AI", "NVIDIA RTX", "Open Source", "RTX Spark" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T12:00:49.000Z", "first_source": "nvidia-blog", "last_published_at": "2026-10-13T12:00:49.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T12:00:49.000Z", "sources": [ "nvidia-blog" ] }, "engagement": null, "entity_kinds": { "labs": [ "NVIDIA" ], "models": [] }, "feed": "https://blogs.nvidia.com/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "hardware/capital keyword", "rejected_off_topic": 2, "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 8.5, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "compute", "published_at": "2026-10-13T12:00:49.000Z", "score": 30.3, "source": "nvidia-blog", "summary": "NVIDIA RTX Spark is a new beginning for the PC — a reinvention combining the capabilities of a gaming PC, a creative studio and a personal AI supercomputer into a single superchip. With up to 128GB of unified memory and 1 petaflop of AI compute, RTX Spark delivers the full stack of NVIDIA RTX and […]", "title": "All Systems Go: Global PC App Ecosystem Ready for NVIDIA RTX Spark", "url": "https://blogs.nvidia.com/blog/local-ai-rtx-spark-laptops-software-updates" }, { "entities": [ "Anthropic", "Zhipu" ], "id": "cee7f688bd1238f6", "kind": "press", "meta": { "age_hours_at_score": 44.4, "categories": [ "The Download" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T12:00:00.000Z", "first_source": "mit-tech-review", "last_published_at": "2026-10-13T12:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T12:00:00.000Z", "sources": [ "mit-tech-review" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic", "Zhipu" ], "models": [] }, "feed": "https://www.technologyreview.com/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 5, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 6, "recency": 8.5, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "attention", "published_at": "2026-10-13T12:00:00.000Z", "score": 30.8, "source": "mit-tech-review", "summary": "This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Open models have real benefits. Do they outweigh the risks? Anthropic recently released a report arguing that a powerful new model released by the Chinese company Z.ai, poses a…", "title": "The Download: open model trade-offs and geothermal power’s promise", "url": "https://technologyreview.com/2026/10/13/1146382/the-download-open-models-trade-offs-geothermal-power" }, { "entities": [ "GPT" ], "id": "8d45898e533e591d", "kind": "lab", "meta": { "age_hours_at_score": 44.4, "categories": [ "Startup" ], "category_weight_factor": 0.45, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T12:00:00.000Z", "first_source": "openai", "last_published_at": "2026-10-13T12:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T12:00:00.000Z", "sources": [ "openai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [ "GPT" ] }, "feed": "https://openai.com/news/rss.xml", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 8.5, "severity": 0, "source_weight": 11.3 }, "source_weight": 0.45, "story": null }, "pillar": "capability", "published_at": "2026-10-13T12:00:00.000Z", "score": 22.8, "source": "openai", "summary": "Higgsfield brings creators longer videos with GPT-6 Astra, precise visuals with GPT-Image-2.5, and a path to voice filmmaking with GPT-Live.", "title": "Higgsfield takes AI video from 1 minute to over 10", "url": "https://openai.com/index/higgsfield-longer-videos-voice" }, { "entities": [], "id": "bf271ae3a2aac33c", "kind": "press", "meta": { "age_hours_at_score": 44.4, "categories": [ "AI Architecture", "AI, ML & Data Engineering", "Agents", "Architecture & Design", "Artificial Intelligence", "Large language models", "QCon AI New York 2026", "news" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T12:00:00.000Z", "first_source": "infoq-ai", "last_published_at": "2026-10-13T12:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T12:00:00.000Z", "sources": [ "infoq-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://feed.infoq.com/ai-ml-data-eng/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 8.5, "severity": 0, "source_weight": 12.5 }, "source_weight": 0.5, "story": null }, "pillar": "capability", "published_at": "2026-10-13T12:00:00.000Z", "score": 21, "source": "infoq-ai", "summary": "Once AI is in production, code arrives faster than it can be reviewed, token costs rise, evals have to be good enough to act on, and agents need permissions and guardrails. QCon AI New York 2026 (December 15-16) has confirmed sessions from engineers sharing how they handle these constraints. By Artenisa Chatziou", "title": "Production Patterns for Agents, Evals, and LLM Costs: QCon AI New York 2026", "url": "https://infoq.com/news/2026/10/qcon-ai-newyork-2026-sessionsv2" }, { "entities": [], "id": "56279fc565cee996", "kind": "forum", "meta": { "age_hours_at_score": 45, "comments": 24, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T11:24:08.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-13T11:24:08.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T11:24:08.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50069942", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 145 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "hn_id": "50069942", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "min_points": 8, "pillar_reason": "source default", "points": 145, "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 14.4, "entities": 0, "recency": 8.4, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "window_hours": 72 }, "pillar": "attention", "published_at": "2026-10-13T11:24:08.000Z", "score": 37.8, "source": "hn-ai", "summary": "145 points, 24 comments on Hacker News.", "title": "Character.ai Files for Bankruptcy", "url": "https://theverge.com/ai-artificial-intelligence/1009802/character-ai-files-for-bankruptcy-disney" }, { "entities": [], "id": "0143c4ec8cc9474d", "kind": "press", "meta": { "age_hours_at_score": 45.4, "categories": [ "The Big Story" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T11:00:00.000Z", "first_source": "wired-ai", "last_published_at": "2026-10-13T11:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T11:00:00.000Z", "sources": [ "wired-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.wired.com/feed/tag/ai/latest/rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "source default", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 8.3, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "attention", "published_at": "2026-10-13T11:00:00.000Z", "score": 22.1, "source": "wired-ai", "summary": "In a world of AI Overviews, Wikipedia usage declines. But the real loss may be far more existential than website traffic.", "title": "Wikipedians Believed AI Would Come and Go. They Were Wrong", "url": "https://wired.com/story/book-excerpt-the-last-best-place-on-the-internet-wikipedia-artificial-intelligence" }, { "entities": [ "Samsung" ], "id": "36ec9574ae163244", "kind": "press", "meta": { "age_hours_at_score": 45.9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T10:30:05.000Z", "first_source": "scmp-tech", "last_published_at": "2026-10-13T10:30:05.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T10:30:05.000Z", "sources": [ "scmp-tech" ] }, "engagement": null, "entity_kinds": { "labs": [ "Samsung" ], "models": [] }, "feed": "https://www.scmp.com/rss/36/feed", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "hardware/capital keyword", "rejected_off_topic": 7, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 3, "recency": 8.3, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null }, "pillar": "compute", "published_at": "2026-10-13T10:30:05.000Z", "score": 26.3, "source": "scmp-tech", "summary": "A sharp sell-off in Asian memory chip stocks this week signals growing investor doubts over whether the sector’s AI-driven profit boom can last, even as soaring prices drive strong quarterly earnings across the sector. Shares in market leaders Samsung Electronics and SK Hynix plunged in Seoul on Tuesday, extending a…", "title": "Why Samsung, SK Hynix and CXMT shares are under pressure despite strong AI demand", "url": "https://scmp.com/tech/tech-trends/article/3370739/why-samsung-sk-hynix-and-cxmt-shares-are-under-pressure-despite-strong-ai-demand" }, { "entities": [ "Anthropic", "Claude" ], "id": "818085bd085cd9ce", "kind": "press", "meta": { "age_hours_at_score": 46.2, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T10:11:23.000Z", "first_source": "ft-ai", "last_published_at": "2026-10-13T10:11:23.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T10:11:23.000Z", "sources": [ "ft-ai" ] }, "engagement": null, "entity_kinds": { "labs": [ "Anthropic" ], "models": [ "Claude" ] }, "feed": "https://www.ft.com/artificial-intelligence?format=rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": "A", "excluded": null, "severity": 1, 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You can read more from the series here. Late last month, Anthropic released a report arguing that the model GLM-5.3 poses a major cyberattack risk. The report’s core…", "title": "Open models have real benefits. Do they outweigh the risks?", "url": "https://technologyreview.com/2026/10/13/1146366/open-models-have-real-benefits-do-they-outweigh-the-risks" }, { "entities": [], "id": "a236e186e6ba40ed", "kind": "press", "meta": { "age_hours_at_score": 46.4, "categories": [ "Politics" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T10:00:00.000Z", "first_source": "wired-ai", "last_published_at": "2026-10-13T10:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T10:00:00.000Z", "sources": [ "wired-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.wired.com/feed/tag/ai/latest/rss", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "hardware/capital keyword", "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 8.2, "severity": 0, "source_weight": 13.8 }, "source_weight": 0.55, "story": null }, "pillar": "compute", "published_at": "2026-10-13T10:00:00.000Z", "score": 22, "source": "wired-ai", "summary": "Backed by Silicon Valley titans, Leading the Future has raised huge sums to promote pro-AI candidates on both side of the aisle.", "title": "The Biggest Pro-AI Super PAC Network Has Poured Millions Into Nevada Shell Companies", "url": "https://wired.com/story/the-biggest-pro-ai-super-pac-network-has-poured-millions-into-nevada-shell-companies" }, { "entities": [], "id": "fe5d3c0dde00dc62", "kind": "press", "meta": { "age_hours_at_score": 46.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T10:00:00.000Z", "first_source": "restofworld", "last_published_at": "2026-10-13T10:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T10:00:00.000Z", "sources": [ "restofworld" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": 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"governance", "published_at": "2026-10-13T09:32:00.000Z", "score": 21.9, "source": "govuk-ai", "summary": "Businesses across the UK backed with a share of £50 million to help turn innovations into products that make a difference in the real world", "title": "Quantum firms backed to keep railways running and cut waiting lists as UK harnesses potential of future tech", "url": "https://gov.uk/government/news/quantum-firms-backed-to-keep-railways-running-and-cut-waiting-lists-as-uk-harnesses-potential-of-future-tech" }, { "entities": [], "id": "7953a44bf601d761", "kind": "press", "meta": { "age_hours_at_score": 46.9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T09:28:09.000Z", "first_source": "govuk-ai", "last_published_at": "2026-10-13T09:28:09.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T09:28:09.000Z", "sources": [ "govuk-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": 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"score": 26, "source": "scmp-tech", "summary": "Chinese AI chipmaker Iluvatar CoreX debuted its first supernode system, a computing system that squeezes 144 graphics processing units (GPUs) into a single cabinet, breaking an industrial record. The new product, dubbed the Tiangai 300 IXL144, was unveiled on Monday at the 2026 China International Industry Fair (CIIF)…", "title": "China’s Iluvatar CoreX’s first AI computing supernode squeezes most GPUs in 1 system", "url": "https://scmp.com/tech/tech-trends/article/3370711/chinas-iluvatar-corexs-first-ai-computing-supernode-squeezes-most-gpus-1-system" }, { "entities": [], "id": "6b0995f7da517ee5", "kind": "press", "meta": { "age_hours_at_score": 47.4, "categories": [ "App", "Artificial intelligence" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T09:00:00.000Z", "first_source": "mit-tech-review", "last_published_at": "2026-10-13T09:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T09:00:00.000Z", "sources": [ "mit-tech-review" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.technologyreview.com/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "pillar_reason": "source default", "rejected_off_topic": 5, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 8, "severity": 0, "source_weight": 16.3 }, "source_weight": 0.65, "story": null }, "pillar": "attention", "published_at": "2026-10-13T09:00:00.000Z", "score": 24.3, "source": "mit-tech-review", "summary": "When Keelan Miskell began winding down his crypto startup this year, he didn’t think its data was worth much: the users were anonymous, the company just a few years old. But he’s since turned the firm’s digital archives into real money. He’s netted roughly $25,000 for his code base and a still-pending amount (likely…", "title": "AI labs are buying data from dead startups to train their models", "url": "https://technologyreview.com/2026/10/13/1145755/ai-labs-buying-data-from-dead-startups-to-train-models" }, { "entities": [], "id": "66993c94d604ac7a", "kind": "forum", "meta": { "age_hours_at_score": 48.5, "comments": 0, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T07:55:10.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-13T07:55:10.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T07:55:10.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50068210", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 10 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "hn_id": "50068210", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "min_points": 8, "pillar_reason": "source default", "points": 10, "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 6.9, "entities": 0, "recency": 7.9, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": null, "window_hours": 72 }, "pillar": "attention", "published_at": "2026-10-13T07:55:10.000Z", "score": 29.8, "source": "hn-ai", "summary": "10 points, 0 comments on Hacker News.", "title": "Linus Torvalds \"Did What I Preach\" Used AI to Make Code Decipherabl, Find Bugs", "url": "https://phoronix.com/news/Linus-Torvalds-Assoc-Array" }, { "entities": [], "id": "4b016f5706dce499", "kind": "forum", "meta": { "age_hours_at_score": 48.9, "comments": 289, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T07:28:34.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-13T07:28:34.000Z", "lead_minutes": 0, "primary_published_at": 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114 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "hn_id": "50067356", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "locally_ai_filtered": true, "min_points": 8, "pillar_reason": "source default", "points": 114, "rejected_off_topic": 2, "score_components": { "corroboration": 0, "coverage": 5, "engagement": 13.7, "entities": 0, "recency": 7.6, "severity": 0, "source_weight": 15 }, "source_weight": 0.6, "story": { "id": "b96448075ee2", "is_lead": true, "lead": "56acf17ef0518e39", "linked_by": [ { "rule": "shared-words", "with": "666035ba55e33b88", "words": [ "financial", "imf", "threat" ] } ], "members": [ "56acf17ef0518e39", "666035ba55e33b88" ], "severity": 0.21, "source_count": 2, "sources": [ "guardian-ai", "hn-ai" ] }, "window_hours": 72 }, "pillar": "attention", "published_at": "2026-10-13T05:57:32.000Z", "score": 41.3, "source": "hn-ai", "summary": "114 points, 127 comments on Hacker News.", "title": "Threat of AI share crash puts world financial stability at risk, says IMF", "url": "https://theguardian.com/business/2026/oct/13/ai-share-crash-risk-global-imf-debt-corrowing-cyber-attacks" }, { "entities": [ "NVIDIA" ], "id": "a2a8ddf1867ef936", "kind": "lab", "meta": { "age_hours_at_score": 51.4, "categories": [ "AI", "Agentic AI", "Artificial Intelligence", "Customer Stories", "Events", "Inception", "NVIDIA in Europe", "Open Source", "Sovereign AI" ], "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T05:00:31.000Z", "first_source": "nvidia-blog", "last_published_at": "2026-10-13T05:00:31.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T05:00:31.000Z", "sources": [ "nvidia-blog" ] }, "engagement": null, "entity_kinds": { "labs": [ "NVIDIA" ], "models": [] }, "feed": "https://blogs.nvidia.com/feed/", "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, 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France’s progress sits on deliberate groundwork. The AI Action Summit in February 2025…", "title": "NVIDIA Announces New Investments in France’s Startups", "url": "https://blogs.nvidia.com/blog/france-ecosystem-agentic-computing" }, { "entities": [], "id": "f486bca1d4d81e14", "kind": "release", "meta": { "age_hours_at_score": 51.5, "basket_failed": [], "basket_live": 9, "basket_size": 9, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T04:50:41.000Z", "first_source": "github-releases", "last_published_at": "2026-10-13T04:50:41.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T04:50:41.000Z", "sources": [ "github-releases" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, "pillar_reason": "primary artefact (research output / model release)", "release_tag": "v0.40.4-rc1: Add Kev decision scoring to MLX (#18976)", "repo": "ollama/ollama", "retained": true, "score_components": { "corroboration": 0, "coverage": 0, "engagement": 0, "entities": 0, "recency": 7.4, "severity": 0, "source_weight": 18.8 }, "source_weight": 0.75, "story": null }, "pillar": "capability", "published_at": "2026-10-13T04:50:41.000Z", "score": 26.2, "source": "github-releases", "summary": "Add Kev decision scoring to MLX Load Kev models with their FP32 decision head and release metadata. Support System One scoring with shared-state caching and Kev-specific limits. address comments", "title": "ollama/ollama v0.40.4-rc1: Add Kev decision scoring to MLX (#18976)", "url": "https://github.com/ollama/ollama/releases/tag/v0.40.4-rc1" }, { "entities": [], "id": "56816ca6e08de0b3", "kind": "forum", "meta": { "age_hours_at_score": 51.6, "comments": 23, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T04:48:45.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-13T04:48:45.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T04:48:45.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50066860", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 28 }, "entity_kinds": { "labs": [], "models": [] }, "first_seen_at": "2026-10-15T02:52:59.205Z", "hn_id": "50066860", "incident": { "band": null, "excluded": null, "severity": 0, "strong_signal": false, "terms": [] }, 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These regulations establish the process by which the public may file objections seeking administrative review of proposed projects and activities implementing land management…", "title": "Project-Level Predecisional Administrative Review Process", "url": "https://federalregister.gov/documents/2026/10/13/2026-20890/project-level-predecisional-administrative-review-process" }, { "entities": [], "id": "3fb84fed6be97e7c", "kind": "press", "meta": { "age_hours_at_score": 52.4, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T04:00:00.000Z", "first_source": "federal-register-ai", "last_published_at": "2026-10-13T04:00:00.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T04:00:00.000Z", "sources": [ "federal-register-ai" ] }, "engagement": null, "entity_kinds": { "labs": [], "models": [] }, "feed": "https://www.federalregister.gov/api/v1/documents.rss?conditions%5Bterm%5D=artificial+intelligence", "first_seen_at": "2026-10-15T02:52:59.205Z", 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"story": null }, "pillar": "capability", "published_at": "2026-10-13T04:00:00.000Z", "score": 18.6, "source": "mit-news-ai", "summary": "Professor Deb Roy contends there should be someone who is “answerable” for every piece of text in public life, no matter its origins.", "title": "Q&A: Building accountability behind our words — and AI’s — in civic conversations", "url": "https://news.mit.edu/2026/qa-building-accountability-behind-our-words-and-ai-civic-conversations-1013" }, { "entities": [], "id": "b693a6e80e752f67", "kind": "forum", "meta": { "age_hours_at_score": 54.3, "comments": 143, "corroboration": { "also": [], "count": 1, "first_seen_published_at": "2026-10-13T02:07:08.000Z", "first_source": "hn-ai", "last_published_at": "2026-10-13T02:07:08.000Z", "lead_minutes": 0, "primary_published_at": "2026-10-13T02:07:08.000Z", "sources": [ "hn-ai" ] }, "discussion_url": "https://news.ycombinator.com/item?id=50065692", "engagement": { "full_scale": 500, "metric": "hn_points", "value": 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