Daily snapshot 2026-05-04

2026-05-04/agents-activity.jsonl

@@ -1,3 +1,32 @@
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2026-05-04/ai-lawsuits.jsonl

@@ -0,0 +1,19 @@
+{"id":"nyt-v-openai","name":"The New York Times Co. v. Microsoft Corp. & OpenAI","plaintiff":"The New York Times Company","defendants":["Microsoft Corporation","OpenAI, Inc.","OpenAI LP","OpenAI GP"],"jurisdiction":"US (S.D.N.Y.)","court":"United States District Court for the Southern District of New York","caseNumber":"1:23-cv-11195","filed":"2023-12-27","status":"active","stage":"discovery","claims":["copyright-infringement","dmca-violation","unfair-competition"],"summary":"NYT alleges OpenAI and Microsoft trained GPT-class models on millions of NYT articles without license and that ChatGPT outputs reproduce articles verbatim. Most-cited AI training-data case; partial motion-to-dismiss granted in 2025-04 on some claims, copyright claims survived.","sources":["https://www.courtlistener.com/docket/68117049/the-new-york-times-company-v-microsoft-corporation/","https://www.nytimes.com/2023/12/27/business/media/new-york-times-open-ai-microsoft-lawsuit.html"]}
+{"id":"daily-news-v-openai","name":"Daily News, LP et al. v. Microsoft Corp. & OpenAI","plaintiff":"Daily News LP, Chicago Tribune, Denver Post, Orlando Sentinel, Sun Sentinel, San Jose Mercury News, Orange County Register, St. Paul Pioneer Press (eight Alden Global Capital papers)","defendants":["Microsoft Corporation","OpenAI, Inc.","OpenAI subsidiaries"],"jurisdiction":"US (S.D.N.Y.)","court":"United States District Court for the Southern District of New York","caseNumber":"1:24-cv-03285","filed":"2024-04-30","status":"consolidated","stage":"discovery","claims":["copyright-infringement","dmca-violation"],"summary":"Eight US newspapers owned by Alden Global Capital consolidated their suits with NYT v OpenAI alleging the same training-data harvesting pattern. Procedural consolidation under the NYT case for discovery.","sources":["https://www.alden.global","https://www.theverge.com/2024/4/30/24145402/alden-global-newspapers-openai-microsoft-lawsuit-copyright"]}
+{"id":"cir-v-openai","name":"Center for Investigative Reporting v. OpenAI & Microsoft","plaintiff":"Center for Investigative Reporting (Mother Jones, Reveal)","defendants":["OpenAI, Inc.","Microsoft Corporation"],"jurisdiction":"US (S.D.N.Y.)","court":"United States District Court for the Southern District of New York","caseNumber":"1:24-cv-04872","filed":"2024-06-27","status":"active","stage":"discovery","claims":["copyright-infringement","dmca-violation"],"summary":"Investigative-journalism nonprofit alleges OpenAI scraped CIR's reporting from Mother Jones and Reveal for training, and that ChatGPT regurgitates articles without attribution.","sources":["https://revealnews.org/article/center-for-investigative-reporting-sues-openai-microsoft/"]}
+{"id":"raw-story-v-openai","name":"Raw Story Media v. OpenAI","plaintiff":"Raw Story Media; AlterNet Media","defendants":["OpenAI, Inc."],"jurisdiction":"US (S.D.N.Y.)","court":"United States District Court for the Southern District of New York","caseNumber":"1:24-cv-01514","filed":"2024-02-28","status":"dismissed","stage":"closed","claims":["dmca-violation"],"summary":"DMCA-only theory (CMI removal) without a copyright claim. Dismissed for lack of standing 2024-11; plaintiffs filed an amended complaint.","sources":["https://www.reuters.com/legal/litigation/openai-defeats-news-outlets-copyright-lawsuit-over-ai-training-data-2024-11-08/"]}
+{"id":"newscorp-v-perplexity","name":"Dow Jones & NYP Holdings v. Perplexity AI","plaintiff":"Dow Jones & Company; NYP Holdings (News Corp)","defendants":["Perplexity AI, Inc."],"jurisdiction":"US (S.D.N.Y.)","court":"United States District Court for the Southern District of New York","caseNumber":"1:24-cv-07984","filed":"2024-10-21","status":"active","stage":"motion-to-dismiss","claims":["copyright-infringement","unfair-competition","trademark-infringement"],"summary":"WSJ and NY Post owners allege Perplexity's answer engine copies and republishes their reporting verbatim and falsely attributes its summaries. First major lawsuit specifically targeting an AI search-and-summarize product, distinct from the model-training cases.","sources":["https://www.wsj.com/business/media/news-corp-sues-perplexity-ai-claiming-massive-illegal-copying-of-articles-c30b3eb6"]}
+{"id":"authors-guild-v-openai","name":"Authors Guild et al. v. OpenAI (consolidated)","plaintiff":"Authors Guild, John Grisham, George R.R. Martin, Jodi Picoult, David Baldacci, Michael Connelly, others","defendants":["OpenAI, Inc.","Microsoft Corporation"],"jurisdiction":"US (S.D.N.Y.)","court":"United States District Court for the Southern District of New York","caseNumber":"1:23-cv-08292","filed":"2023-09-19","status":"consolidated","stage":"discovery","claims":["copyright-infringement"],"summary":"Class action by 17 named novelists plus the Authors Guild alleging OpenAI trained on copyrighted novels via Books2/Books3 and similar shadow-library corpora. Consolidated with related novelist suits (Tremblay, Silverman, Chabon) under MDL coordination.","sources":["https://authorsguild.org/news/ag-and-authors-file-class-action-suit-against-openai/","https://www.courtlistener.com/docket/67810111/authors-guild-v-openai-inc/"]}
+{"id":"silverman-v-openai","name":"Silverman v. OpenAI","plaintiff":"Sarah Silverman, Christopher Golden, Richard Kadrey","defendants":["OpenAI, Inc."],"jurisdiction":"US (N.D. Cal.)","court":"United States District Court for the Northern District of California","caseNumber":"3:23-cv-03416","filed":"2023-07-07","status":"consolidated","stage":"discovery","claims":["copyright-infringement","dmca-violation","unjust-enrichment"],"summary":"Author class action alleging training on copyrighted books from shadow libraries (Books3 / LibGen). Most secondary claims (DMCA, negligence, unjust enrichment) dismissed 2024-02; copyright claims survived and entered consolidated discovery.","sources":["https://www.courtlistener.com/docket/67569326/tremblay-v-openai-inc/","https://www.theverge.com/2023/7/9/23788741/sarah-silverman-openai-meta-chatgpt-llama-copyright-infringement-chatbots-artificial-intelligence-ai"]}
+{"id":"kadrey-v-meta","name":"Kadrey v. Meta Platforms (Llama)","plaintiff":"Richard Kadrey, Sarah Silverman, Christopher Golden","defendants":["Meta Platforms, Inc."],"jurisdiction":"US (N.D. Cal.)","court":"United States District Court for the Northern District of California","caseNumber":"3:23-cv-03417","filed":"2023-07-07","status":"active","stage":"summary-judgment","claims":["copyright-infringement"],"summary":"Same plaintiff group, parallel claim against Meta over Llama training on Books3. Discovery surfaced internal Meta communications discussing the use of pirated book corpora; partial summary judgment briefing in 2025.","sources":["https://www.courtlistener.com/docket/67569329/kadrey-v-meta-platforms-inc/","https://arstechnica.com/tech-policy/2025/01/meta-staff-discussed-using-pirated-books-to-train-ai-court-filings-show/"]}
+{"id":"doe-v-github","name":"Doe et al. v. GitHub, OpenAI, Microsoft (Copilot)","plaintiff":"Anonymous open-source developers","defendants":["GitHub, Inc.","Microsoft Corporation","OpenAI, Inc."],"jurisdiction":"US (N.D. Cal.)","court":"United States District Court for the Northern District of California","caseNumber":"4:22-cv-06823","filed":"2022-11-03","status":"active","stage":"discovery","claims":["dmca-violation","breach-of-contract","unfair-competition","unjust-enrichment"],"summary":"Putative class action by open-source developers alleging GitHub Copilot reproduces their code without honoring open-source license attribution requirements. Most copyright claims dismissed 2023-2024; DMCA and breach-of-license claims survived to discovery.","sources":["https://githubcopilotlitigation.com","https://www.courtlistener.com/docket/63992594/doe-v-github-inc/"]}
+{"id":"andersen-v-stability","name":"Andersen v. Stability AI, Midjourney, DeviantArt, Runway","plaintiff":"Sarah Andersen, Kelly McKernan, Karla Ortiz, others (artist class)","defendants":["Stability AI Ltd.","Stability AI, Inc.","Midjourney, Inc.","DeviantArt, Inc.","Runway AI, Inc."],"jurisdiction":"US (N.D. Cal.)","court":"United States District Court for the Northern District of California","caseNumber":"3:23-cv-00201","filed":"2023-01-13","status":"active","stage":"discovery","claims":["copyright-infringement","dmca-violation","right-of-publicity","unfair-competition"],"summary":"First major class action by visual artists alleging diffusion-model training on LAION-scraped artwork without license. Amended complaint in 2023-11 added Runway and Midjourney; key copyright claims survived motion-to-dismiss in 2024-08.","sources":["https://www.courtlistener.com/docket/66735377/andersen-v-stability-ai-ltd/","https://www.theverge.com/2024/8/13/24220231/stability-midjourney-deviantart-runway-andersen-copyright-class-action-allowed-proceed"]}
+{"id":"getty-v-stability-us","name":"Getty Images (US) v. Stability AI","plaintiff":"Getty Images (US), Inc.","defendants":["Stability AI, Inc.","Stability AI Ltd."],"jurisdiction":"US (D. Del.)","court":"United States District Court for the District of Delaware","caseNumber":"1:23-cv-00135","filed":"2023-02-03","status":"active","stage":"discovery","claims":["copyright-infringement","trademark-infringement","unfair-competition"],"summary":"Getty alleges Stable Diffusion was trained on millions of Getty-watermarked images and reproduces the Getty watermark in some generations. The watermark issue is rare among training-data cases because it provides direct evidence of the training corpus contents.","sources":["https://newsroom.gettyimages.com/en/getty-images/getty-images-statement","https://www.courtlistener.com/docket/66878049/getty-images-us-inc-v-stability-ai-inc/"]}
+{"id":"getty-v-stability-uk","name":"Getty Images (UK) v. Stability AI Ltd.","plaintiff":"Getty Images (US), Inc.; Getty Images International (UK)","defendants":["Stability AI Ltd."],"jurisdiction":"UK (England and Wales)","court":"High Court of Justice, Business and Property Courts of England and Wales","caseNumber":"IL-2023-000007","filed":"2023-01","status":"active","stage":"trial","claims":["copyright-infringement","trademark-infringement"],"summary":"Parallel UK proceeding to the Delaware case. UK trial reached merits in 2025 with closer scrutiny of where training actually occurred (UK courts apply territorial limits to copyright). One of the first AI training cases to reach trial in any jurisdiction.","sources":["https://www.judiciary.uk/judgments/getty-images-v-stability-ai/"]}
+{"id":"concord-v-anthropic","name":"Concord Music Group v. Anthropic","plaintiff":"Concord Music Group, Universal Music Publishing Group, ABKCO","defendants":["Anthropic PBC"],"jurisdiction":"US (M.D. Tenn.)","court":"United States District Court for the Middle District of Tennessee","caseNumber":"3:23-cv-01092","filed":"2023-10-18","status":"active","stage":"discovery","claims":["copyright-infringement","dmca-violation","unfair-competition"],"summary":"Music publishers allege Claude was trained on copyrighted song lyrics and outputs them verbatim on request. Anthropic implemented output filters in 2024 in response; preliminary injunction denied 2024-03 on a narrow set of claims, case continues on the broader theory.","sources":["https://www.courtlistener.com/docket/67862254/concord-music-group-inc-v-anthropic-pbc/","https://www.billboard.com/business/legal/anthropic-claude-ai-music-publishers-lyrics-lawsuit-1235427547/"]}
+{"id":"riaa-v-suno","name":"RIAA v. Suno","plaintiff":"UMG Recordings, Sony Music Entertainment, Warner Records (RIAA member labels)","defendants":["Suno, Inc."],"jurisdiction":"US (D. Mass.)","court":"United States District Court for the District of Massachusetts","caseNumber":"1:24-cv-11611","filed":"2024-06-24","status":"active","stage":"discovery","claims":["copyright-infringement"],"summary":"Major labels allege Suno trained its music-generation model on copyrighted recordings, with outputs that closely replicate the style and in some cases the melodic content of named tracks. Companion case RIAA v. Udio filed same day in S.D.N.Y.","sources":["https://www.riaa.com/wp-content/uploads/2024/06/UMG-Recordings-et-al-v-Suno-Inc-et-al-Complaint.pdf"]}
+{"id":"riaa-v-udio","name":"RIAA v. Udio (Uncharted Labs)","plaintiff":"UMG Recordings, Sony Music Entertainment, Warner Records (RIAA member labels)","defendants":["Uncharted Labs, Inc. (d/b/a Udio)"],"jurisdiction":"US (S.D.N.Y.)","court":"United States District Court for the Southern District of New York","caseNumber":"1:24-cv-04777","filed":"2024-06-24","status":"active","stage":"discovery","claims":["copyright-infringement"],"summary":"Companion case to RIAA v Suno, same theory against Udio. Filed same day. Both cases consolidated in their respective districts for procedural efficiency.","sources":["https://www.riaa.com/wp-content/uploads/2024/06/Sony-Music-et-al-v-Uncharted-Labs-dba-Udio-Complaint.pdf"]}
+{"id":"lehrman-v-lovo","name":"Lehrman v. Lovo","plaintiff":"Paul Skye Lehrman, Linnea Sage (voice actors)","defendants":["Lovo, Inc."],"jurisdiction":"US (S.D.N.Y.)","court":"United States District Court for the Southern District of New York","caseNumber":"1:24-cv-03770","filed":"2024-05-16","status":"active","stage":"discovery","claims":["copyright-infringement","right-of-publicity","unfair-competition"],"summary":"Voice actors allege their voices were cloned and offered as TTS products on Lovo without consent. Test case for AI voice cloning and right-of-publicity in the federal courts.","sources":["https://www.nytimes.com/2024/05/16/technology/lovo-ai-voice-actors-lawsuit.html"]}
+{"id":"ftc-openai-microsoft-inquiry","name":"FTC inquiry: AI partnerships and investments","plaintiff":"US Federal Trade Commission (Section 6(b) inquiry)","defendants":["Microsoft","Alphabet","Amazon","OpenAI","Anthropic"],"jurisdiction":"US (federal regulatory)","court":"Federal Trade Commission","caseNumber":"FTC Inquiry P246201","filed":"2024-01-25","status":"active","stage":"discovery","claims":["antitrust","regulatory-investigation"],"summary":"FTC ordered Microsoft, Alphabet, Amazon, OpenAI, and Anthropic to submit details of generative-AI investments and partnerships, focused on whether large-cloud-provider stakes in frontier labs distort competition. Staff report issued 2025-01.","sources":["https://www.ftc.gov/news-events/news/press-releases/2024/01/ftc-launches-inquiry-generative-ai-investments-partnerships"]}
+{"id":"eu-microsoft-openai-review","name":"EU Commission review: Microsoft / OpenAI partnership","plaintiff":"European Commission (Directorate-General for Competition)","defendants":["Microsoft Corporation","OpenAI"],"jurisdiction":"EU","court":"European Commission","caseNumber":"M.11766","filed":"2024-01","status":"active","stage":"discovery","claims":["antitrust","regulatory-investigation"],"summary":"EU competition authorities reviewing whether Microsoft's investment in and integration with OpenAI amounts to a notifiable concentration under the EU Merger Regulation. As of late 2024 EC concluded no de jure control by Microsoft, but informal market inquiries continue.","sources":["https://ec.europa.eu/competition/mergers/cases1/202404/M_11766_11254399_25_3.pdf"]}
+{"id":"garcia-v-character-ai","name":"Garcia v. Character Technologies","plaintiff":"Megan Garcia (estate of Sewell Setzer III)","defendants":["Character Technologies, Inc.","Google LLC","Alphabet Inc."],"jurisdiction":"US (M.D. Fla.)","court":"United States District Court for the Middle District of Florida","caseNumber":"6:24-cv-01903","filed":"2024-10-22","status":"active","stage":"motion-to-dismiss","claims":["tort","consumer-protection","unfair-competition"],"summary":"Wrongful-death and product-liability suit alleging Character.AI's chatbot contributed to a 14-year-old's suicide. First high-profile product-liability test for a consumer LLM chatbot.","sources":["https://www.nytimes.com/2024/10/23/technology/characterai-lawsuit-teen-suicide.html"]}

2026-05-04/attention.jsonl

@@ -0,0 +1,12 @@
+{"id":"openai","name":"OpenAI","news_24h":1,"news_7d":13,"trending_repos":2,"agent_hits":1,"raw_score":21.05,"attention_score":100,"rank":1,"top_articles":[{"title":"I tested ChatGPT and Perplexity AI as my CarPlay voice assistants - both made Siri look bad","source":"ZDNet AI","published_at":"2026-05-03T17:00:45.000Z"},{"title":"Musk v. Altman week 1: Elon Musk says he was duped, warns AI could kill us all, and admits that xAI distills OpenAI’s models","source":"MIT Technology Review","published_at":"2026-05-01T22:08:19.000Z"},{"title":"A Dark-Money Campaign Is Paying Influencers to Frame Chinese AI as a Threat","source":"WIRED AI","published_at":"2026-05-01T20:25:28.000Z"}]}
+{"id":"anthropic","name":"Anthropic","news_24h":0,"news_7d":2,"trending_repos":4,"agent_hits":0,"raw_score":10,"attention_score":47.5,"rank":2,"top_articles":[{"title":"Disneyland Now Uses Face Recognition on Visitors","source":"WIRED AI","published_at":"2026-05-02T10:30:00.000Z"},{"title":"Pentagon strikes classified AI deals with OpenAI, Google, and Nvidia — but not Anthropic","source":"The Verge AI","published_at":"2026-05-01T14:09:56.000Z"}]}
+{"id":"nvidia","name":"NVIDIA","news_24h":0,"news_7d":6,"trending_repos":0,"agent_hits":0,"raw_score":6,"attention_score":28.5,"rank":3,"top_articles":[{"title":"Pentagon strikes classified AI deals with OpenAI, Google, and Nvidia — but not Anthropic","source":"The Verge AI","published_at":"2026-05-01T14:09:56.000Z"},{"title":"Nemotron Labs: What OpenClaw Agents Mean for Every Organization","source":"NVIDIA AI Blog","published_at":"2026-04-30T20:00:39.000Z"},{"title":"It’s Gonna Be May: 16 Games Hit the Cloud This Month, With More NVIDIA GeForce RTX 5080 Power","source":"NVIDIA AI Blog","published_at":"2026-04-30T13:00:47.000Z"}]}
+{"id":"huggingface","name":"Hugging Face","news_24h":0,"news_7d":4,"trending_repos":1,"agent_hits":0,"raw_score":6,"attention_score":28.5,"rank":4,"top_articles":[{"title":"AI evals are becoming the new compute bottleneck","source":"Hugging Face Blog","published_at":"2026-04-29T16:45:09.000Z"},{"title":"Granite 4.1 LLMs: How They’re Built","source":"Hugging Face Blog","published_at":"2026-04-29T15:01:48.000Z"},{"title":"DeepInfra on Hugging Face Inference Providers 🔥","source":"Hugging Face Blog","published_at":"2026-04-29T00:00:00.000Z"}]}
+{"id":"google","name":"Google","news_24h":0,"news_7d":3,"trending_repos":1,"agent_hits":0,"raw_score":5,"attention_score":23.8,"rank":5,"top_articles":[{"title":"Google Maps vs. Waze: I compared the two best navigation apps, and this one wins","source":"ZDNet AI","published_at":"2026-05-01T17:46:00.000Z"},{"title":"Celebrating 20 years of Google Translate: Fun facts, tips and new features to try","source":"Google AI Blog","published_at":"2026-04-28T16:00:00.000Z"},{"title":"Join the new AI Agents Vibe Coding Course from Google and Kaggle","source":"Google AI Blog","published_at":"2026-04-27T13:00:00.000Z"}]}
+{"id":"cohere","name":"Cohere","news_24h":1,"news_7d":1,"trending_repos":0,"agent_hits":0,"raw_score":5,"attention_score":23.8,"rank":6,"top_articles":[{"title":"Thinking in Text and Images: Interleaved Vision--Language Reasoning Traces for Long-Horizon Robot Manipulation","source":"arXiv cs.AI","published_at":"2026-05-04T04:00:00.000Z"}]}
+{"id":"perplexity","name":"Perplexity","news_24h":1,"news_7d":1,"trending_repos":0,"agent_hits":0,"raw_score":5,"attention_score":23.8,"rank":7,"top_articles":[{"title":"I tested ChatGPT and Perplexity AI as my CarPlay voice assistants - both made Siri look bad","source":"ZDNet AI","published_at":"2026-05-03T17:00:45.000Z"}]}
+{"id":"xai","name":"xAI","news_24h":0,"news_7d":4,"trending_repos":0,"agent_hits":0,"raw_score":4,"attention_score":19,"rank":8,"top_articles":[{"title":"Musk v. Altman week 1: Elon Musk says he was duped, warns AI could kill us all, and admits that xAI distills OpenAI’s models","source":"MIT Technology Review","published_at":"2026-05-01T22:08:19.000Z"},{"title":"Pentagon strikes classified AI deals with OpenAI, Google, and Nvidia — but not Anthropic","source":"The Verge AI","published_at":"2026-05-01T14:09:56.000Z"},{"title":"Elon Musk confirms xAI used OpenAI’s models to train Grok","source":"The Verge AI","published_at":"2026-04-30T18:16:57.000Z"}]}
+{"id":"mistral","name":"Mistral","news_24h":0,"news_7d":0,"trending_repos":0,"agent_hits":1,"raw_score":0.05,"attention_score":0.2,"rank":9,"top_articles":[]}
+{"id":"meta","name":"Meta","news_24h":0,"news_7d":0,"trending_repos":0,"agent_hits":0,"raw_score":0,"attention_score":0,"rank":10,"top_articles":[]}
+{"id":"deepseek","name":"DeepSeek","news_24h":0,"news_7d":0,"trending_repos":0,"agent_hits":0,"raw_score":0,"attention_score":0,"rank":11,"top_articles":[]}
+{"id":"cursor","name":"Cursor","news_24h":0,"news_7d":0,"trending_repos":0,"agent_hits":0,"raw_score":0,"attention_score":0,"rank":12,"top_articles":[]}

2026-05-04/benchmark-registry.jsonl

@@ -1,24 +1,24 @@
-{"id":"mmlu","name":"MMLU","category":"knowledge","description":"Massive Multitask Language Understanding. 57-subject multiple-choice covering law, medicine, history, math, etc. The original general-knowledge benchmark.","released":"2020","size":"15,908 questions","scoreRange":"0-100% accuracy","frontierScore":"~92%","status":"saturated","contaminationRisk":"high","maintainer":"Hendrycks et al.","paperUrl":"https://arxiv.org/abs/2009.03300","repoUrl":"https://github.com/hendrycks/test","leaderboardUrl":null,"whoCares":"General assistants. Mostly saturated; use MMLU-Pro for current frontier signal."}
-{"id":"mmlu-pro","name":"MMLU-Pro","category":"knowledge","description":"Harder successor to MMLU. 10-choice questions designed to require multi-step reasoning over memorization. Standard \"is this model smart\" benchmark for general-purpose use.","released":"2024","size":"12,032 questions","scoreRange":"0-100% accuracy","frontierScore":"~94%","status":"active","contaminationRisk":"medium","maintainer":"TIGER Lab","paperUrl":"https://arxiv.org/abs/2406.01574","repoUrl":"https://github.com/TIGER-AI-Lab/MMLU-Pro","leaderboardUrl":"https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro","whoCares":"General chat assistants and research synthesis agents."}
-{"id":"gpqa-diamond","name":"GPQA Diamond","category":"knowledge","description":"Graduate-level Google-Proof Questions, Diamond subset. 198 expert-validated PhD-hard physics, chemistry, biology questions.","released":"2023","size":"198 questions","scoreRange":"0-100% accuracy","frontierScore":"~80%","status":"active","contaminationRisk":"low","maintainer":"Rein et al.","paperUrl":"https://arxiv.org/abs/2311.12022","repoUrl":"https://github.com/idavidrein/gpqa","leaderboardUrl":null,"whoCares":"Research agents, technical writing agents, anyone who needs scientific reasoning beyond fact recall."}
-{"id":"hle","name":"Humanity's Last Exam","category":"knowledge","description":"Joint CAIS/Scale benchmark of 3,000 expert-validated questions across 100+ disciplines. The hardest broad-knowledge benchmark in 2025-2026.","released":"2025-01","size":"3,000 questions","scoreRange":"0-100% accuracy","frontierScore":"~30%","status":"active","contaminationRisk":"low","maintainer":"CAIS + Scale AI","paperUrl":"https://lastexam.ai","repoUrl":"https://github.com/centerforaisafety/hle","leaderboardUrl":"https://lastexam.ai","whoCares":"Frontier-model evaluators. The benchmark every model release in 2026 reports against."}
-{"id":"arc-agi-2","name":"ARC-AGI-2","category":"knowledge","description":"Abstract pattern-matching benchmark from Francois Chollet. ARC-AGI-2 is the harder 2025 successor; tests novel problem solving without training-data overlap.","released":"2025-03","size":"~400 tasks public, more private","scoreRange":"0-100% accuracy","frontierScore":"~25%","status":"active","contaminationRisk":"low","maintainer":"ARC Prize Foundation","paperUrl":"https://arxiv.org/abs/2412.04604","repoUrl":"https://github.com/arcprize/ARC-AGI-2","leaderboardUrl":"https://arcprize.org/leaderboard","whoCares":"AGI researchers. The least-saturated frontier benchmark; current models lag far behind humans."}
-{"id":"math","name":"MATH","category":"math","description":"Competition-level math problems (AMC, AIME, Putnam). Multi-step algebra, geometry, combinatorics. Exact-match scoring.","released":"2021","size":"12,500 problems","scoreRange":"0-100% accuracy","frontierScore":"~96%","status":"saturated","contaminationRisk":"high","maintainer":"Hendrycks et al.","paperUrl":"https://arxiv.org/abs/2103.03874","repoUrl":"https://github.com/hendrycks/math","leaderboardUrl":null,"whoCares":"Quantitative agents. Largely saturated; use AIME 2025 for current signal."}
-{"id":"aime-2025","name":"AIME 2025","category":"math","description":"American Invitational Mathematics Examination 2025. 30 hard high-school competition math problems, integer answers. Released contamination-free in 2025.","released":"2025","size":"30 problems","scoreRange":"0-30 correct","frontierScore":"~28/30","status":"active","contaminationRisk":"low","maintainer":"MAA","paperUrl":null,"repoUrl":null,"leaderboardUrl":"https://artificialanalysis.ai","whoCares":"Frontier reasoning evaluators. Standard reasoning-tier test in 2025-2026 model release announcements."}
-{"id":"gsm8k","name":"GSM8K","category":"math","description":"Grade-school math word problems. Multi-step arithmetic with chain-of-thought as the canonical solution form.","released":"2021","size":"8,500 problems","scoreRange":"0-100% accuracy","frontierScore":"~97%","status":"saturated","contaminationRisk":"high","maintainer":"OpenAI","paperUrl":"https://arxiv.org/abs/2110.14168","repoUrl":"https://github.com/openai/grade-school-math","leaderboardUrl":null,"whoCares":"Mostly saturated. Useful as a sanity check that a model can do basic chain-of-thought reasoning."}
-{"id":"humaneval","name":"HumanEval","category":"code","description":"OpenAI's 164 Python programming problems with unit tests. The original code-gen benchmark.","released":"2021","size":"164 problems","scoreRange":"0-100% pass@1","frontierScore":"~97%","status":"saturated","contaminationRisk":"high","maintainer":"OpenAI","paperUrl":"https://arxiv.org/abs/2107.03374","repoUrl":"https://github.com/openai/human-eval","leaderboardUrl":"https://paperswithcode.com/sota/code-generation-on-humaneval","whoCares":"Capability floor only. Not a frontier signal; use SWE-bench Verified or LiveCodeBench instead."}
-{"id":"mbpp","name":"MBPP","category":"code","description":"Mostly Basic Python Problems. 1k crowd-sourced introductory programming problems.","released":"2021","size":"974 problems","scoreRange":"0-100% pass@1","frontierScore":"~92%","status":"saturated","contaminationRisk":"high","maintainer":"Google Research","paperUrl":"https://arxiv.org/abs/2108.07732","repoUrl":"https://github.com/google-research/google-research/tree/master/mbpp","leaderboardUrl":null,"whoCares":"Same role as HumanEval. Saturated; use newer benchmarks."}
-{"id":"livecodebench","name":"LiveCodeBench","category":"code","description":"Continuously refreshed code benchmark. Pulls competitive programming problems (LeetCode, AtCoder, Codeforces) from after a model's training cutoff. Contamination-free by construction.","released":"2024","size":"1k+ problems, growing","scoreRange":"0-100% pass@1","frontierScore":"~75%","status":"active","contaminationRisk":"low","maintainer":"UC Berkeley + UW","paperUrl":"https://arxiv.org/abs/2403.07974","repoUrl":"https://github.com/LiveCodeBench/LiveCodeBench","leaderboardUrl":"https://livecodebench.github.io/leaderboard.html","whoCares":"Coding agents. The contamination-resistant alternative to HumanEval."}
-{"id":"swe-bench-verified","name":"SWE-bench Verified","category":"code","description":"500 human-validated GitHub issues across 12 Python repos. Patch must resolve the issue and pass the project test suite.","released":"2024-08","size":"500 instances","scoreRange":"0-100% resolved","frontierScore":"~75%","status":"active","contaminationRisk":"medium","maintainer":"OpenAI / Princeton","paperUrl":"https://arxiv.org/abs/2310.06770","repoUrl":"https://github.com/swe-bench/SWE-bench","leaderboardUrl":"https://www.swebench.com/","whoCares":"Coding agents. The single benchmark most production agentic-coding tools report against."}
-{"id":"aider-polyglot","name":"Aider Polyglot","category":"code","description":"225 of the hardest Exercism exercises across C++, Go, Java, JavaScript, Python, Rust. Edit-by-diff scoring (closer to real-world coding work).","released":"2024","size":"225 problems","scoreRange":"0-100% pass@2","frontierScore":"~85%","status":"active","contaminationRisk":"medium","maintainer":"Aider","paperUrl":null,"repoUrl":"https://github.com/Aider-AI/polyglot-benchmark","leaderboardUrl":"https://aider.chat/docs/leaderboards/","whoCares":"Multilingual coding agents. The cross-language complement to SWE-bench."}
-{"id":"terminal-bench","name":"Terminal-Bench","category":"code","description":"Stanford / Anthropic agentic terminal-task benchmark. Each task gives the agent a goal and a sandboxed shell; success measured by deterministic post-condition.","released":"2025-01","size":"~80 tasks","scoreRange":"0-100% solved","frontierScore":"~52%","status":"active","contaminationRisk":"low","maintainer":"Stanford + Anthropic","paperUrl":"https://arxiv.org/abs/2501.18099","repoUrl":"https://github.com/laude-institute/terminal-bench","leaderboardUrl":"https://www.tbench.ai/","whoCares":"Terminal-shaped agents (Claude Code, Codex CLI, Aider). Tests the loop, not the language model."}
-{"id":"swe-lancer","name":"SWE-Lancer","category":"code","description":"OpenAI benchmark of paid Upwork engineering tasks ($1M+ in real bounties). Includes diff-style fixes and longer feature work judged against original buyer acceptance criteria.","released":"2025-02","size":"1,488 tasks","scoreRange":"0-100% earned","frontierScore":"~42%","status":"active","contaminationRisk":"low","maintainer":"OpenAI","paperUrl":"https://arxiv.org/abs/2502.12115","repoUrl":"https://github.com/openai/SWELancer-Benchmark","leaderboardUrl":null,"whoCares":"Agentic-coding evaluators. Most realistic eval of \"could you replace a freelance engineer for this task.\""}
-{"id":"mmmu-pro","name":"MMMU-Pro","category":"multimodal","description":"Massive Multi-discipline Multimodal Understanding. College-level questions with images across 30 subjects. Pro version drops text-only solvable variants.","released":"2024","size":"~3,500 questions","scoreRange":"0-100% accuracy","frontierScore":"~78%","status":"active","contaminationRisk":"medium","maintainer":"TIGER Lab + IN.AI","paperUrl":"https://arxiv.org/abs/2409.02813","repoUrl":"https://github.com/MMMU-Benchmark/MMMU","leaderboardUrl":"https://mmmu-benchmark.github.io","whoCares":"Vision-capable agents. Standard benchmark in multimodal model release announcements."}
-{"id":"mathvista","name":"MathVista","category":"multimodal","description":"Visual math reasoning across diagrams, charts, geometry, and graphs.","released":"2024","size":"6,141 problems","scoreRange":"0-100% accuracy","frontierScore":"~80%","status":"active","contaminationRisk":"medium","maintainer":"UCLA","paperUrl":"https://arxiv.org/abs/2310.02255","repoUrl":"https://github.com/lupantech/MathVista","leaderboardUrl":"https://mathvista.github.io","whoCares":"Multimodal reasoning agents (chart understanding, scientific diagrams)."}
-{"id":"tau-bench","name":"Tau-Bench","category":"agents","description":"Sierra benchmark of customer-service agents. Tests realistic multi-turn tool-use in airline and retail domains with deterministic evaluators.","released":"2024-06","size":"~250 tasks","scoreRange":"0-100% pass@1","frontierScore":"~70%","status":"active","contaminationRisk":"low","maintainer":"Sierra","paperUrl":"https://arxiv.org/abs/2406.12045","repoUrl":"https://github.com/sierra-research/tau-bench","leaderboardUrl":null,"whoCares":"Customer-service and tool-use agents. The most cited evaluation of \"would this actually work in production support.\""}
-{"id":"gaia","name":"GAIA","category":"agents","description":"General AI Assistants benchmark. 466 real-world questions requiring web browsing, file ops, and multi-step reasoning. Three difficulty levels.","released":"2023","size":"466 tasks","scoreRange":"0-100% accuracy","frontierScore":"~75% (level 1), ~50% (level 3)","status":"active","contaminationRisk":"low","maintainer":"HF + Meta","paperUrl":"https://arxiv.org/abs/2311.12983","repoUrl":"https://huggingface.co/datasets/gaia-benchmark/GAIA","leaderboardUrl":"https://huggingface.co/spaces/gaia-benchmark/leaderboard","whoCares":"Generalist autonomous agents. Tests the full agent loop, not isolated capabilities."}
-{"id":"webarena","name":"WebArena","category":"agents","description":"Browser-agent benchmark. 812 tasks across 5 simulated websites (e-commerce, GitLab, Reddit, mapping, content management). Deterministic post-condition checks.","released":"2023","size":"812 tasks","scoreRange":"0-100% solved","frontierScore":"~58%","status":"active","contaminationRisk":"low","maintainer":"CMU + UW","paperUrl":"https://arxiv.org/abs/2307.13854","repoUrl":"https://github.com/web-arena-x/webarena","leaderboardUrl":"https://webarena.dev/leaderboard","whoCares":"Browser-automation agents. Standard eval for \"can this agent click around the web reliably.\""}
-{"id":"osworld","name":"OSWorld","category":"agents","description":"Computer-use benchmark. Real Ubuntu, Windows, macOS desktops with 369 tasks covering OS-level workflows (file management, app interaction, research).","released":"2024","size":"369 tasks","scoreRange":"0-100% solved","frontierScore":"~28%","status":"active","contaminationRisk":"low","maintainer":"HKU + Salesforce","paperUrl":"https://arxiv.org/abs/2404.07972","repoUrl":"https://github.com/xlang-ai/OSWorld","leaderboardUrl":"https://os-world.github.io","whoCares":"Computer-use agents (Anthropic Computer Use, OpenAI Operator, etc). Hardest agent benchmark in 2026."}
-{"id":"bfcl","name":"BFCL v3","category":"agents","description":"Berkeley Function Calling Leaderboard. Multi-turn, multi-language function calling with parallel and chained tool use.","released":"2024","size":"4,400+ test cases","scoreRange":"0-100% accuracy","frontierScore":"~85%","status":"active","contaminationRisk":"medium","maintainer":"UC Berkeley","paperUrl":null,"repoUrl":"https://github.com/ShishirPatil/gorilla","leaderboardUrl":"https://gorilla.cs.berkeley.edu/leaderboard.html","whoCares":"Tool-using agents. Most-cited function-calling benchmark; checks parallel and dependent tool calls."}
-{"id":"hcast","name":"HCAST","category":"agents","description":"METR's long-horizon agentic task suite. Measures how long a task an agent can autonomously complete. Calibrated against human researcher time-to-complete.","released":"2024","size":"~70 tasks","scoreRange":"minutes-of-human-work","frontierScore":"~30 min equivalent","status":"active","contaminationRisk":"low","maintainer":"METR","paperUrl":"https://metr.github.io/autonomy-evals-guide/","repoUrl":null,"leaderboardUrl":null,"whoCares":"Frontier autonomy evaluators. The benchmark behind the \"AI does 50% of tasks under N minutes\" framing."}
-{"id":"ruler","name":"RULER","category":"long-context","description":"NVIDIA benchmark for long-context retrieval and reasoning. Multi-needle, variable-tracking, common-words. Tests effective context length, not just claimed.","released":"2024","size":"~13 task categories","scoreRange":"0-100% accuracy","frontierScore":"effective ~256k for 1M-claimed models","status":"active","contaminationRisk":"low","maintainer":"NVIDIA","paperUrl":"https://arxiv.org/abs/2404.06654","repoUrl":"https://github.com/NVIDIA/RULER","leaderboardUrl":null,"whoCares":"Anyone using long-context models. RULER reveals the gap between claimed and effective context length."}
+{"id":"mmlu","name":"MMLU","category":"knowledge","description":"Massive Multitask Language Understanding. 57-subject multiple-choice covering law, medicine, history, math, etc. The original general-knowledge benchmark.","released":"2020","size":"15,908 questions","scoreRange":"0-100% accuracy","frontierScore":"~92%","frontierModel":null,"frontierDate":null,"frontierSource":null,"status":"saturated","contaminationRisk":"high","maintainer":"Hendrycks et al.","paperUrl":"https://arxiv.org/abs/2009.03300","repoUrl":"https://github.com/hendrycks/test","leaderboardUrl":null,"whoCares":"General assistants. Mostly saturated; use MMLU-Pro for current frontier signal."}
+{"id":"mmlu-pro","name":"MMLU-Pro","category":"knowledge","description":"Harder successor to MMLU. 10-choice questions designed to require multi-step reasoning over memorization. Standard \"is this model smart\" benchmark for general-purpose use.","released":"2024","size":"12,032 questions","scoreRange":"0-100% accuracy","frontierScore":"~94%","frontierModel":"GPT-5 (high reasoning)","frontierDate":"2025-08","frontierSource":"https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro","status":"active","contaminationRisk":"medium","maintainer":"TIGER Lab","paperUrl":"https://arxiv.org/abs/2406.01574","repoUrl":"https://github.com/TIGER-AI-Lab/MMLU-Pro","leaderboardUrl":"https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro","whoCares":"General chat assistants and research synthesis agents."}
+{"id":"gpqa-diamond","name":"GPQA Diamond","category":"knowledge","description":"Graduate-level Google-Proof Questions, Diamond subset. 198 expert-validated PhD-hard physics, chemistry, biology questions.","released":"2023","size":"198 questions","scoreRange":"0-100% accuracy","frontierScore":"~88%","frontierModel":"o3 (high compute)","frontierDate":"2024-12","frontierSource":"https://openai.com/index/learning-to-reason-with-llms/","status":"active","contaminationRisk":"low","maintainer":"Rein et al.","paperUrl":"https://arxiv.org/abs/2311.12022","repoUrl":"https://github.com/idavidrein/gpqa","leaderboardUrl":null,"whoCares":"Research agents, technical writing agents, anyone who needs scientific reasoning beyond fact recall."}
+{"id":"hle","name":"Humanity's Last Exam","category":"knowledge","description":"Joint CAIS/Scale benchmark of 3,000 expert-validated questions across 100+ disciplines. The hardest broad-knowledge benchmark in 2025-2026.","released":"2025-01","size":"3,000 questions","scoreRange":"0-100% accuracy","frontierScore":"~30%","frontierModel":null,"frontierDate":null,"frontierSource":null,"status":"active","contaminationRisk":"low","maintainer":"CAIS + Scale AI","paperUrl":"https://lastexam.ai","repoUrl":"https://github.com/centerforaisafety/hle","leaderboardUrl":"https://lastexam.ai","whoCares":"Frontier-model evaluators. The benchmark every model release in 2026 reports against. Leaderboard is the freshness anchor."}
+{"id":"arc-agi-2","name":"ARC-AGI-2","category":"knowledge","description":"Abstract pattern-matching benchmark from Francois Chollet. ARC-AGI-2 is the harder 2025 successor; tests novel problem solving without training-data overlap.","released":"2025-03","size":"~400 tasks public, more private","scoreRange":"0-100% accuracy","frontierScore":"~25%","frontierModel":null,"frontierDate":null,"frontierSource":null,"status":"active","contaminationRisk":"low","maintainer":"ARC Prize Foundation","paperUrl":"https://arxiv.org/abs/2412.04604","repoUrl":"https://github.com/arcprize/ARC-AGI-2","leaderboardUrl":"https://arcprize.org/leaderboard","whoCares":"AGI researchers. The least-saturated frontier benchmark; current models lag far behind humans. Leaderboard updates frequently."}
+{"id":"math","name":"MATH","category":"math","description":"Competition-level math problems (AMC, AIME, Putnam). Multi-step algebra, geometry, combinatorics. Exact-match scoring.","released":"2021","size":"12,500 problems","scoreRange":"0-100% accuracy","frontierScore":"~96%","frontierModel":null,"frontierDate":null,"frontierSource":null,"status":"saturated","contaminationRisk":"high","maintainer":"Hendrycks et al.","paperUrl":"https://arxiv.org/abs/2103.03874","repoUrl":"https://github.com/hendrycks/math","leaderboardUrl":null,"whoCares":"Quantitative agents. Largely saturated; use AIME 2025 for current signal."}
+{"id":"aime-2025","name":"AIME 2025","category":"math","description":"American Invitational Mathematics Examination 2025. 30 hard high-school competition math problems, integer answers. Released contamination-free in 2025.","released":"2025","size":"30 problems","scoreRange":"0-30 correct","frontierScore":"~28/30","frontierModel":"o3 (high compute)","frontierDate":"2025-02","frontierSource":"https://artificialanalysis.ai","status":"active","contaminationRisk":"low","maintainer":"MAA","paperUrl":null,"repoUrl":null,"leaderboardUrl":"https://artificialanalysis.ai","whoCares":"Frontier reasoning evaluators. Standard reasoning-tier test in 2025-2026 model release announcements."}
+{"id":"gsm8k","name":"GSM8K","category":"math","description":"Grade-school math word problems. Multi-step arithmetic with chain-of-thought as the canonical solution form.","released":"2021","size":"8,500 problems","scoreRange":"0-100% accuracy","frontierScore":"~97%","frontierModel":null,"frontierDate":null,"frontierSource":null,"status":"saturated","contaminationRisk":"high","maintainer":"OpenAI","paperUrl":"https://arxiv.org/abs/2110.14168","repoUrl":"https://github.com/openai/grade-school-math","leaderboardUrl":null,"whoCares":"Mostly saturated. Useful as a sanity check that a model can do basic chain-of-thought reasoning."}
+{"id":"humaneval","name":"HumanEval","category":"code","description":"OpenAI's 164 Python programming problems with unit tests. The original code-gen benchmark.","released":"2021","size":"164 problems","scoreRange":"0-100% pass@1","frontierScore":"~97%","frontierModel":null,"frontierDate":null,"frontierSource":null,"status":"saturated","contaminationRisk":"high","maintainer":"OpenAI","paperUrl":"https://arxiv.org/abs/2107.03374","repoUrl":"https://github.com/openai/human-eval","leaderboardUrl":"https://paperswithcode.com/sota/code-generation-on-humaneval","whoCares":"Capability floor only. Not a frontier signal; use SWE-bench Verified or LiveCodeBench instead."}
+{"id":"mbpp","name":"MBPP","category":"code","description":"Mostly Basic Python Problems. 1k crowd-sourced introductory programming problems.","released":"2021","size":"974 problems","scoreRange":"0-100% pass@1","frontierScore":"~92%","frontierModel":null,"frontierDate":null,"frontierSource":null,"status":"saturated","contaminationRisk":"high","maintainer":"Google Research","paperUrl":"https://arxiv.org/abs/2108.07732","repoUrl":"https://github.com/google-research/google-research/tree/master/mbpp","leaderboardUrl":null,"whoCares":"Same role as HumanEval. Saturated; use newer benchmarks."}
+{"id":"livecodebench","name":"LiveCodeBench","category":"code","description":"Continuously refreshed code benchmark. Pulls competitive programming problems (LeetCode, AtCoder, Codeforces) from after a model's training cutoff. Contamination-free by construction.","released":"2024","size":"1k+ problems, growing","scoreRange":"0-100% pass@1","frontierScore":"~75%","frontierModel":null,"frontierDate":null,"frontierSource":null,"status":"active","contaminationRisk":"low","maintainer":"UC Berkeley + UW","paperUrl":"https://arxiv.org/abs/2403.07974","repoUrl":"https://github.com/LiveCodeBench/LiveCodeBench","leaderboardUrl":"https://livecodebench.github.io/leaderboard.html","whoCares":"Coding agents. The contamination-resistant alternative to HumanEval. Leaderboard rotates fast as the problem set grows."}
+{"id":"swe-bench-verified","name":"SWE-bench Verified","category":"code","description":"500 human-validated GitHub issues across 12 Python repos. Patch must resolve the issue and pass the project test suite.","released":"2024-08","size":"500 instances","scoreRange":"0-100% resolved","frontierScore":"~75%","frontierModel":"Claude Sonnet 4.6","frontierDate":"2025-09","frontierSource":"https://www.swebench.com/","status":"active","contaminationRisk":"medium","maintainer":"OpenAI / Princeton","paperUrl":"https://arxiv.org/abs/2310.06770","repoUrl":"https://github.com/swe-bench/SWE-bench","leaderboardUrl":"https://www.swebench.com/","whoCares":"Coding agents. The single benchmark most production agentic-coding tools report against."}
+{"id":"aider-polyglot","name":"Aider Polyglot","category":"code","description":"225 of the hardest Exercism exercises across C++, Go, Java, JavaScript, Python, Rust. Edit-by-diff scoring (closer to real-world coding work).","released":"2024","size":"225 problems","scoreRange":"0-100% pass@2","frontierScore":"~85%","frontierModel":"Claude Sonnet 4.6","frontierDate":"2025-09","frontierSource":"https://aider.chat/docs/leaderboards/","status":"active","contaminationRisk":"medium","maintainer":"Aider","paperUrl":null,"repoUrl":"https://github.com/Aider-AI/polyglot-benchmark","leaderboardUrl":"https://aider.chat/docs/leaderboards/","whoCares":"Multilingual coding agents. The cross-language complement to SWE-bench."}
+{"id":"terminal-bench","name":"Terminal-Bench","category":"code","description":"Stanford / Anthropic agentic terminal-task benchmark. Each task gives the agent a goal and a sandboxed shell; success measured by deterministic post-condition.","released":"2025-01","size":"~80 tasks","scoreRange":"0-100% solved","frontierScore":"~52%","frontierModel":"Claude Sonnet 4.6","frontierDate":"2025-09","frontierSource":"https://www.tbench.ai/","status":"active","contaminationRisk":"low","maintainer":"Stanford + Anthropic","paperUrl":"https://arxiv.org/abs/2501.18099","repoUrl":"https://github.com/laude-institute/terminal-bench","leaderboardUrl":"https://www.tbench.ai/","whoCares":"Terminal-shaped agents (Claude Code, Codex CLI, Aider). Tests the loop, not the language model."}
+{"id":"swe-lancer","name":"SWE-Lancer","category":"code","description":"OpenAI benchmark of paid Upwork engineering tasks ($1M+ in real bounties). Includes diff-style fixes and longer feature work judged against original buyer acceptance criteria.","released":"2025-02","size":"1,488 tasks","scoreRange":"0-100% earned","frontierScore":"~42%","frontierModel":"GPT-5","frontierDate":"2025-08","frontierSource":"https://arxiv.org/abs/2502.12115","status":"active","contaminationRisk":"low","maintainer":"OpenAI","paperUrl":"https://arxiv.org/abs/2502.12115","repoUrl":"https://github.com/openai/SWELancer-Benchmark","leaderboardUrl":null,"whoCares":"Agentic-coding evaluators. Most realistic eval of \"could you replace a freelance engineer for this task.\""}
+{"id":"mmmu-pro","name":"MMMU-Pro","category":"multimodal","description":"Massive Multi-discipline Multimodal Understanding. College-level questions with images across 30 subjects. Pro version drops text-only solvable variants.","released":"2024","size":"~3,500 questions","scoreRange":"0-100% accuracy","frontierScore":"~78%","frontierModel":"Gemini 2.5 Pro","frontierDate":"2025-06","frontierSource":"https://mmmu-benchmark.github.io","status":"active","contaminationRisk":"medium","maintainer":"TIGER Lab + IN.AI","paperUrl":"https://arxiv.org/abs/2409.02813","repoUrl":"https://github.com/MMMU-Benchmark/MMMU","leaderboardUrl":"https://mmmu-benchmark.github.io","whoCares":"Vision-capable agents. Standard benchmark in multimodal model release announcements."}
+{"id":"mathvista","name":"MathVista","category":"multimodal","description":"Visual math reasoning across diagrams, charts, geometry, and graphs.","released":"2024","size":"6,141 problems","scoreRange":"0-100% accuracy","frontierScore":"~80%","frontierModel":null,"frontierDate":null,"frontierSource":null,"status":"active","contaminationRisk":"medium","maintainer":"UCLA","paperUrl":"https://arxiv.org/abs/2310.02255","repoUrl":"https://github.com/lupantech/MathVista","leaderboardUrl":"https://mathvista.github.io","whoCares":"Multimodal reasoning agents (chart understanding, scientific diagrams)."}
+{"id":"tau-bench","name":"Tau-Bench","category":"agents","description":"Sierra benchmark of customer-service agents. Tests realistic multi-turn tool-use in airline and retail domains with deterministic evaluators.","released":"2024-06","size":"~250 tasks","scoreRange":"0-100% pass@1","frontierScore":"~70%","frontierModel":"Claude Sonnet 4.6","frontierDate":"2025-09","frontierSource":"https://www.anthropic.com/news/claude-sonnet-4-5","status":"active","contaminationRisk":"low","maintainer":"Sierra","paperUrl":"https://arxiv.org/abs/2406.12045","repoUrl":"https://github.com/sierra-research/tau-bench","leaderboardUrl":null,"whoCares":"Customer-service and tool-use agents. The most cited evaluation of \"would this actually work in production support.\""}
+{"id":"gaia","name":"GAIA","category":"agents","description":"General AI Assistants benchmark. 466 real-world questions requiring web browsing, file ops, and multi-step reasoning. Three difficulty levels.","released":"2023","size":"466 tasks","scoreRange":"0-100% accuracy","frontierScore":"~75% (level 1), ~50% (level 3)","frontierModel":null,"frontierDate":null,"frontierSource":null,"status":"active","contaminationRisk":"low","maintainer":"HF + Meta","paperUrl":"https://arxiv.org/abs/2311.12983","repoUrl":"https://huggingface.co/datasets/gaia-benchmark/GAIA","leaderboardUrl":"https://huggingface.co/spaces/gaia-benchmark/leaderboard","whoCares":"Generalist autonomous agents. Tests the full agent loop, not isolated capabilities. HF Space leaderboard rotates frequently."}
+{"id":"webarena","name":"WebArena","category":"agents","description":"Browser-agent benchmark. 812 tasks across 5 simulated websites (e-commerce, GitLab, Reddit, mapping, content management). Deterministic post-condition checks.","released":"2023","size":"812 tasks","scoreRange":"0-100% solved","frontierScore":"~58%","frontierModel":null,"frontierDate":null,"frontierSource":null,"status":"active","contaminationRisk":"low","maintainer":"CMU + UW","paperUrl":"https://arxiv.org/abs/2307.13854","repoUrl":"https://github.com/web-arena-x/webarena","leaderboardUrl":"https://webarena.dev/leaderboard","whoCares":"Browser-automation agents. Standard eval for \"can this agent click around the web reliably.\""}
+{"id":"osworld","name":"OSWorld","category":"agents","description":"Computer-use benchmark. Real Ubuntu, Windows, macOS desktops with 369 tasks covering OS-level workflows (file management, app interaction, research).","released":"2024","size":"369 tasks","scoreRange":"0-100% solved","frontierScore":"~28%","frontierModel":"Claude Sonnet 4.6 (Computer Use)","frontierDate":"2025-09","frontierSource":"https://os-world.github.io","status":"active","contaminationRisk":"low","maintainer":"HKU + Salesforce","paperUrl":"https://arxiv.org/abs/2404.07972","repoUrl":"https://github.com/xlang-ai/OSWorld","leaderboardUrl":"https://os-world.github.io","whoCares":"Computer-use agents (Anthropic Computer Use, OpenAI Operator, etc). Hardest agent benchmark in 2026."}
+{"id":"bfcl","name":"BFCL v3","category":"agents","description":"Berkeley Function Calling Leaderboard. Multi-turn, multi-language function calling with parallel and chained tool use.","released":"2024","size":"4,400+ test cases","scoreRange":"0-100% accuracy","frontierScore":"~85%","frontierModel":null,"frontierDate":null,"frontierSource":null,"status":"active","contaminationRisk":"medium","maintainer":"UC Berkeley","paperUrl":null,"repoUrl":"https://github.com/ShishirPatil/gorilla","leaderboardUrl":"https://gorilla.cs.berkeley.edu/leaderboard.html","whoCares":"Tool-using agents. Most-cited function-calling benchmark; checks parallel and dependent tool calls."}
+{"id":"hcast","name":"HCAST","category":"agents","description":"METR's long-horizon agentic task suite. Measures how long a task an agent can autonomously complete. Calibrated against human researcher time-to-complete.","released":"2024","size":"~70 tasks","scoreRange":"minutes-of-human-work","frontierScore":"~30 min equivalent","frontierModel":null,"frontierDate":null,"frontierSource":null,"status":"active","contaminationRisk":"low","maintainer":"METR","paperUrl":"https://metr.github.io/autonomy-evals-guide/","repoUrl":null,"leaderboardUrl":null,"whoCares":"Frontier autonomy evaluators. The benchmark behind the \"AI does 50% of tasks under N minutes\" framing. METR site is the freshness anchor."}
+{"id":"ruler","name":"RULER","category":"long-context","description":"NVIDIA benchmark for long-context retrieval and reasoning. Multi-needle, variable-tracking, common-words. Tests effective context length, not just claimed.","released":"2024","size":"~13 task categories","scoreRange":"0-100% accuracy","frontierScore":"effective ~256k for 1M-claimed models","frontierModel":null,"frontierDate":null,"frontierSource":null,"status":"active","contaminationRisk":"low","maintainer":"NVIDIA","paperUrl":"https://arxiv.org/abs/2404.06654","repoUrl":"https://github.com/NVIDIA/RULER","leaderboardUrl":null,"whoCares":"Anyone using long-context models. RULER reveals the gap between claimed and effective context length."}

2026-05-04/embodied-ai.jsonl

@@ -0,0 +1,25 @@
+{"id":"pi-0","name":"pi-0","org":"Physical Intelligence","category":"foundation_model","parameters":"3.3B","released":"2024-10","license":"Apache-2.0 (open weights)","paperUrl":"https://www.physicalintelligence.company/blog/pi0","codeUrl":"https://github.com/Physical-Intelligence/openpi","demoUrl":"https://www.physicalintelligence.company","notes":"Generalist VLA built on PaliGemma. Trained on 10K+ hours of cross-embodiment teleoperation data. The open release that broadly shifted the field toward VLA foundation models."}
+{"id":"pi-0.5","name":"pi-0.5","org":"Physical Intelligence","category":"foundation_model","parameters":"3.3B","released":"2025-04","license":"open weights (research)","paperUrl":"https://www.physicalintelligence.company/blog/pi05","codeUrl":"https://github.com/Physical-Intelligence/openpi","demoUrl":"https://www.physicalintelligence.company","notes":"Successor to pi-0 with open-world generalization to homes the model never saw during training. Demonstrated end-to-end household tasks in unseen kitchens and bedrooms."}
+{"id":"gr00t-n1","name":"GR00T N1","org":"NVIDIA","category":"foundation_model","parameters":"2B","released":"2025-03","license":"open weights","paperUrl":"https://research.nvidia.com/labs/gear/gr00t/","codeUrl":"https://huggingface.co/nvidia/GR00T-N1-2B","demoUrl":"https://www.nvidia.com/en-us/ai/gr00t/","notes":"NVIDIA dual-system VLA targeting humanoids: System-2 reasoning over a vision-language backbone, System-1 diffusion policy. First open NVIDIA humanoid foundation model."}
+{"id":"rt-2","name":"RT-2","org":"Google DeepMind","category":"foundation_model","parameters":"55B","released":"2023-07","license":"proprietary","paperUrl":"https://robotics-transformer2.github.io","codeUrl":null,"demoUrl":null,"notes":"First VLM-to-action transformer. PaLM-E-class backbone outputs robot actions as tokens. Closed but seeded the entire VLA category."}
+{"id":"openvla","name":"OpenVLA","org":"Stanford / Berkeley / Toyota Research","category":"foundation_model","parameters":"7B","released":"2024-06","license":"MIT","paperUrl":"https://openvla.github.io","codeUrl":"https://github.com/openvla/openvla","demoUrl":"https://huggingface.co/openvla/openvla-7b","notes":"Open replication of RT-2 trained on Open X-Embodiment. The reference open VLA before pi-0; still widely fine-tuned for academic robotics work."}
+{"id":"octo","name":"Octo","org":"Berkeley AI Research","category":"foundation_model","parameters":"93M","released":"2024-05","license":"MIT","paperUrl":"https://octo-models.github.io","codeUrl":"https://github.com/octo-models/octo","demoUrl":null,"notes":"Small generalist transformer policy (93M and 27M variants) trained on 800K Open X-Embodiment trajectories. Designed as a fine-tunable base."}
+{"id":"rdt-1b","name":"RDT-1B","org":"Tsinghua University","category":"foundation_model","parameters":"1.2B","released":"2024-10","license":"MIT","paperUrl":"https://rdt-robotics.github.io/rdt-robotics/","codeUrl":"https://github.com/thu-ml/RoboticsDiffusionTransformer","demoUrl":null,"notes":"Largest open bimanual manipulation diffusion transformer at release. Pretrained on 1M+ multi-robot episodes, fine-tuned for bimanual ALOHA-class hardware."}
+{"id":"helix","name":"Helix","org":"Figure AI","category":"foundation_model","parameters":"undisclosed","released":"2025-02","license":"proprietary","paperUrl":"https://www.figure.ai/news/helix","codeUrl":null,"demoUrl":"https://www.figure.ai","notes":"Dual-system VLA powering Figure 02. Runs entirely onboard the humanoid (no cloud), controls both arms at 200Hz. First commercial humanoid foundation model in production."}
+{"id":"figure-02","name":"Figure 02","org":"Figure AI","category":"humanoid","parameters":null,"released":"2024-08","license":"commercial","paperUrl":"https://www.figure.ai/news/introducing-figure-02","codeUrl":null,"demoUrl":"https://www.figure.ai","notes":"5'6\", 70kg humanoid with onboard NVIDIA compute. Shipping in BMW and other industrial pilots. Helix VLA runs locally on the robot."}
+{"id":"1x-neo","name":"1X NEO","org":"1X Technologies","category":"humanoid","parameters":null,"released":"2025-10","license":"commercial (consumer)","paperUrl":"https://www.1x.tech/neo","codeUrl":null,"demoUrl":"https://www.1x.tech","notes":"First humanoid pitched as a household consumer product. Soft, lightweight (30kg) with tendon-driven joints for safety around people. Pre-orders opened late 2025."}
+{"id":"tesla-optimus-gen3","name":"Optimus Gen 3","org":"Tesla","category":"humanoid","parameters":null,"released":"2025-10","license":"commercial","paperUrl":"https://www.tesla.com/AI","codeUrl":null,"demoUrl":"https://www.tesla.com/AI","notes":"Third-generation Tesla humanoid with redesigned hand (22 DoF). Tesla manufacturing factory deployment claimed for 2026, external sales 2027."}
+{"id":"apptronik-apollo","name":"Apollo","org":"Apptronik","category":"humanoid","parameters":null,"released":"2024-08","license":"commercial","paperUrl":"https://apptronik.com/apollo","codeUrl":null,"demoUrl":"https://apptronik.com","notes":"Industrial humanoid pilots with Mercedes and GXO logistics. Built on Apptronik's prior Astra arm work. NASA partnership."}
+{"id":"unitree-g1","name":"Unitree G1","org":"Unitree","category":"humanoid","parameters":null,"released":"2024-05","license":"commercial","paperUrl":"https://www.unitree.com/g1","codeUrl":"https://github.com/unitreerobotics","demoUrl":"https://www.unitree.com/g1","notes":"Lowest-priced commercially available humanoid (entry around $16K). Popular for academic research and home tinkering. SDK and Python bindings published."}
+{"id":"unitree-h1","name":"Unitree H1","org":"Unitree","category":"humanoid","parameters":null,"released":"2023-12","license":"commercial","paperUrl":"https://www.unitree.com/h1","codeUrl":"https://github.com/unitreerobotics","demoUrl":"https://www.unitree.com/h1","notes":"Larger sibling to G1. Holds the unofficial humanoid sprint record (3.3 m/s). Common research platform for whole-body control papers."}
+{"id":"boston-dynamics-atlas-electric","name":"Atlas (Electric)","org":"Boston Dynamics","category":"humanoid","parameters":null,"released":"2024-04","license":"commercial (research / pilot)","paperUrl":"https://bostondynamics.com/blog/electric-new-era-for-atlas/","codeUrl":null,"demoUrl":"https://bostondynamics.com/atlas/","notes":"All-electric Atlas successor to the hydraulic platform. Hyundai pilots in automotive manufacturing. Range of motion exceeds human anatomy."}
+{"id":"agility-digit","name":"Digit","org":"Agility Robotics","category":"humanoid","parameters":null,"released":"2023-09","license":"commercial","paperUrl":"https://agilityrobotics.com/products/digit","codeUrl":null,"demoUrl":"https://agilityrobotics.com","notes":"Bipedal humanoid optimized for warehouse case-handling. RaaS pricing with Amazon and GXO deployments. Bird-leg morphology (knees backward) for energy efficiency."}
+{"id":"sanctuary-phoenix","name":"Phoenix","org":"Sanctuary AI","category":"humanoid","parameters":null,"released":"2023-05","license":"commercial","paperUrl":"https://www.sanctuary.ai/phoenix","codeUrl":null,"demoUrl":"https://www.sanctuary.ai","notes":"Hydraulic-driven hands with 21 DoF per hand, the highest dexterity in any commercial humanoid. Now part of Apptronik post-2025 acquisition."}
+{"id":"open-x-embodiment","name":"Open X-Embodiment","org":"Google DeepMind + 21 institutions","category":"dataset","parameters":null,"released":"2023-10","license":"CC-BY-4.0","paperUrl":"https://robotics-transformer-x.github.io","codeUrl":"https://github.com/google-deepmind/open_x_embodiment","demoUrl":"https://huggingface.co/datasets/jxu124/OpenX-Embodiment","notes":"Standardized cross-embodiment dataset spanning 22 robot embodiments and 1M+ trajectories. The pretraining substrate behind Octo, OpenVLA, and many followups."}
+{"id":"droid","name":"DROID","org":"Stanford / Berkeley / Toyota Research / Google","category":"dataset","parameters":null,"released":"2024-03","license":"CC-BY-4.0","paperUrl":"https://droid-dataset.github.io","codeUrl":"https://github.com/droid-dataset/droid","demoUrl":"https://huggingface.co/datasets/KarlP/droid","notes":"76K teleoperated trajectories across 564 scenes and 86 tasks, all on Franka arms. Largest single-embodiment manipulation dataset. Used as the high-quality slice in pi-0 training."}
+{"id":"agibot-world","name":"AgiBot World","org":"AgiBot","category":"dataset","parameters":null,"released":"2024-12","license":"CC-BY-NC-4.0","paperUrl":"https://agibot-world.com","codeUrl":"https://huggingface.co/datasets/agibot-world/AgiBotWorld-Alpha","demoUrl":"https://agibot-world.com","notes":"Million-trajectory dataset on AgiBot G1 humanoid. Contains long-horizon dual-arm manipulation in real homes and offices. Released alongside the AgiBot platform."}
+{"id":"mobile-aloha","name":"Mobile ALOHA","org":"Stanford","category":"dataset","parameters":null,"released":"2024-01","license":"MIT","paperUrl":"https://mobile-aloha.github.io","codeUrl":"https://github.com/MarkFzp/mobile-aloha","demoUrl":"https://mobile-aloha.github.io","notes":"Open hardware bimanual mobile manipulator (~$32K) plus a 50-task imitation-learning dataset. The reference open platform for mobile bimanual research."}
+{"id":"bridgedata-v2","name":"BridgeData V2","org":"Berkeley AI Research","category":"dataset","parameters":null,"released":"2023-08","license":"CC-BY-4.0","paperUrl":"https://rail-berkeley.github.io/bridgedata","codeUrl":"https://github.com/rail-berkeley/bridge_data_v2","demoUrl":null,"notes":"60K+ trajectories on WidowX arms across 24 environments. Foundational for skill generalization research; included in Open X-Embodiment."}
+{"id":"isaac-lab","name":"Isaac Lab","org":"NVIDIA","category":"simulator","parameters":null,"released":"2024-03","license":"BSD-3-Clause","paperUrl":"https://isaac-sim.github.io/IsaacLab/","codeUrl":"https://github.com/isaac-sim/IsaacLab","demoUrl":"https://developer.nvidia.com/isaac-sim","notes":"GPU-accelerated robot learning framework on Isaac Sim. Supersedes Isaac Gym. Used for sim-to-real RL training of locomotion and manipulation policies."}
+{"id":"mujoco-playground","name":"MuJoCo Playground","org":"Google DeepMind","category":"simulator","parameters":null,"released":"2025-01","license":"Apache-2.0","paperUrl":"https://playground.mujoco.org","codeUrl":"https://github.com/google-deepmind/mujoco_playground","demoUrl":"https://playground.mujoco.org","notes":"JAX-based MuJoCo training suite with battery-included locomotion and manipulation environments. Designed for massively parallel sim-to-real RL."}
+{"id":"genesis","name":"Genesis","org":"Genesis Embodied AI","category":"simulator","parameters":null,"released":"2024-12","license":"Apache-2.0","paperUrl":"https://genesis-embodied-ai.github.io","codeUrl":"https://github.com/Genesis-Embodied-AI/Genesis","demoUrl":"https://genesis-embodied-ai.github.io","notes":"Universal physics engine claiming 80M FPS on a single RTX 4090. Bundled generative pipeline auto-creates scenes from text. Open-sourced by a 19-lab collaboration."}

2026-05-04/harnesses.jsonl

@@ -0,0 +1,4 @@
+{"id":"swe_bench_verified","name":"SWE-bench Verified","description":"500 human-validated GitHub issues across 12 Python repos. The harness must produce a patch that resolves the issue and passes the project's test suite.","maxScore":100,"unit":"% resolved","sourceUrl":"https://www.swebench.com/"}
+{"id":"terminal_bench","name":"Terminal-Bench","description":"Stanford and Anthropic benchmark of agentic terminal tasks. Each task gives the agent a goal and a sandboxed shell; success is measured by a deterministic post-condition.","maxScore":100,"unit":"% solved","sourceUrl":"https://www.tbench.ai/"}
+{"id":"aider_polyglot","name":"Aider Polyglot","description":"225 of the hardest Exercism coding exercises across C++, Go, Java, JavaScript, Python, and Rust. Measures whole-file edit-by-diff quality, not just code generation.","maxScore":100,"unit":"% pass2","sourceUrl":"https://aider.chat/docs/leaderboards/"}
+{"id":"swe_lancer","name":"SWE-Lancer","description":"OpenAI benchmark of paid Upwork engineering tasks ($1M+ in real bounties). Includes both diff-style fixes and longer feature work judged against the original buyer's acceptance criteria.","maxScore":100,"unit":"% earned","sourceUrl":"https://github.com/openai/SWELancer-Benchmark"}

2026-05-04/incidents.jsonl

@@ -0,0 +1,70 @@
+{"id":"Claude API-1774947303129","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-03-31T08:55:03.129Z","resolvedAt":"2026-03-31T09:45:03.232Z","durationMinutes":50}
+{"id":"OpenAI API-1774968014352","service":"OpenAI API","provider":"OpenAI","severity":"minor","title":"OpenAI API degraded performance","startedAt":"2026-03-31T14:40:14.352Z","resolvedAt":"2026-03-31T15:05:22.408Z","durationMinutes":25}
+{"id":"Claude API-1774986313178","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-03-31T19:45:13.178Z","resolvedAt":"2026-03-31T22:10:14.500Z","durationMinutes":145}
+{"id":"Claude API-1775007014887","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-01T01:30:14.887Z","resolvedAt":"2026-04-01T02:15:14.635Z","durationMinutes":45}
+{"id":"Claude API-1775034614960","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-01T09:10:14.960Z","resolvedAt":"2026-04-01T10:45:14.826Z","durationMinutes":95}
+{"id":"OpenAI API-1775115640832","service":"OpenAI API","provider":"OpenAI","severity":"minor","title":"OpenAI API degraded performance","startedAt":"2026-04-02T07:40:40.832Z","resolvedAt":"2026-04-02T09:45:40.431Z","durationMinutes":125}
+{"id":"OpenAI API-1775139648402","service":"OpenAI API","provider":"OpenAI","severity":"major","title":"OpenAI API outage","startedAt":"2026-04-02T14:20:48.402Z","resolvedAt":"2026-04-02T17:50:52.492Z","durationMinutes":210}
+{"id":"Claude API-1775172056894","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-02T23:20:56.894Z","resolvedAt":"2026-04-03T02:55:45.527Z","durationMinutes":215}
+{"id":"Claude API-1775240142594","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-03T18:15:42.594Z","resolvedAt":"2026-04-03T19:25:42.260Z","durationMinutes":70}
+{"id":"OpenAI API-1775455847654","service":"OpenAI API","provider":"OpenAI","severity":"minor","title":"OpenAI API degraded performance","startedAt":"2026-04-06T06:10:47.654Z","resolvedAt":"2026-04-06T08:40:47.602Z","durationMinutes":150}
+{"id":"OpenAI API-1775470246013","service":"OpenAI API","provider":"OpenAI","severity":"major","title":"OpenAI API outage","startedAt":"2026-04-06T10:10:46.013Z","resolvedAt":"2026-04-06T10:40:54.630Z","durationMinutes":30}
+{"id":"Claude API-1775490657303","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-06T15:50:57.303Z","resolvedAt":"2026-04-06T17:20:54.316Z","durationMinutes":90}
+{"id":"Claude API-1775511046353","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-06T21:30:46.353Z","resolvedAt":"2026-04-06T21:50:42.673Z","durationMinutes":20}
+{"id":"OpenAI API-1775557836151","service":"OpenAI API","provider":"OpenAI","severity":"major","title":"OpenAI API outage","startedAt":"2026-04-07T10:30:36.151Z","resolvedAt":"2026-04-07T11:50:36.126Z","durationMinutes":80}
+{"id":"Claude API-1775573735846","service":"Claude API","provider":"Anthropic","severity":"major","title":"Claude API outage","startedAt":"2026-04-07T14:55:35.846Z","resolvedAt":"2026-04-07T16:00:43.679Z","durationMinutes":65}
+{"id":"Claude API-1775629547960","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-08T06:25:47.960Z","resolvedAt":"2026-04-08T09:05:48.009Z","durationMinutes":160}
+{"id":"OpenAI API-1775669128876","service":"OpenAI API","provider":"OpenAI","severity":"minor","title":"OpenAI API degraded performance","startedAt":"2026-04-08T17:25:28.876Z","resolvedAt":"2026-04-08T17:45:28.756Z","durationMinutes":20}
+{"id":"Claude API-1775670630144","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-08T17:50:30.144Z","resolvedAt":"2026-04-08T17:55:28.691Z","durationMinutes":5}
+{"id":"Claude API-1775722239376","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-09T08:10:39.376Z","resolvedAt":"2026-04-09T08:55:37.537Z","durationMinutes":45}
+{"id":"Replicate-1775753147528","service":"Replicate","provider":"Replicate","severity":"major","title":"Replicate outage","startedAt":"2026-04-09T16:45:47.528Z","resolvedAt":"2026-04-09T17:30:46.575Z","durationMinutes":45}
+{"id":"Claude API-1775753746663","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-09T16:55:46.663Z","resolvedAt":"2026-04-09T17:35:45.304Z","durationMinutes":40}
+{"id":"Claude API-1775795438367","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-10T04:30:38.367Z","resolvedAt":"2026-04-10T04:40:37.814Z","durationMinutes":10}
+{"id":"OpenAI API-1775798138135","service":"OpenAI API","provider":"OpenAI","severity":"minor","title":"OpenAI API degraded performance","startedAt":"2026-04-10T05:15:38.135Z","resolvedAt":"2026-04-10T05:20:38.095Z","durationMinutes":5}
+{"id":"Claude API-1775838649486","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-10T16:30:49.486Z","resolvedAt":"2026-04-10T16:55:49.202Z","durationMinutes":25}
+{"id":"Cohere-1775921729418","service":"Cohere","provider":"Cohere","severity":"minor","title":"Cohere degraded performance","startedAt":"2026-04-11T15:35:29.418Z","resolvedAt":"2026-04-11T15:40:30.472Z","durationMinutes":5}
+{"id":"Claude API-1776095139238","service":"Claude API","provider":"Anthropic","severity":"major","title":"Claude API outage","startedAt":"2026-04-13T15:45:39.238Z","resolvedAt":"2026-04-13T16:40:39.232Z","durationMinutes":55}
+{"id":"Cohere-1776109239214","service":"Cohere","provider":"Cohere","severity":"minor","title":"Cohere degraded performance","startedAt":"2026-04-13T19:40:39.214Z","resolvedAt":"2026-04-13T19:45:39.221Z","durationMinutes":5}
+{"id":"OpenAI API-1776123040558","service":"OpenAI API","provider":"OpenAI","severity":"minor","title":"OpenAI API degraded performance","startedAt":"2026-04-13T23:30:40.558Z","resolvedAt":"2026-04-14T08:46:16.705Z","durationMinutes":556}
+{"id":"Claude API-1776158769244","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-14T09:26:09.244Z","resolvedAt":"2026-04-14T15:25:11.771Z","durationMinutes":359}
+{"id":"OpenAI API-1776185409755","service":"OpenAI API","provider":"OpenAI","severity":"minor","title":"OpenAI API degraded performance","startedAt":"2026-04-14T16:50:09.755Z","resolvedAt":"2026-04-14T17:10:10.314Z","durationMinutes":20}
+{"id":"Claude API-1776265208118","service":"Claude API","provider":"Anthropic","severity":"major","title":"Claude API outage","startedAt":"2026-04-15T15:00:08.118Z","resolvedAt":"2026-04-15T17:45:06.443Z","durationMinutes":165}
+{"id":"OpenAI API-1776278106481","service":"OpenAI API","provider":"OpenAI","severity":"major","title":"OpenAI API outage","startedAt":"2026-04-15T18:35:06.481Z","resolvedAt":"2026-04-15T18:55:06.494Z","durationMinutes":20}
+{"id":"Claude API-1776322561245","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-16T06:56:01.245Z","resolvedAt":"2026-04-16T07:46:01.866Z","durationMinutes":50}
+{"id":"Claude API-1776367501940","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-16T19:25:01.940Z","resolvedAt":"2026-04-16T21:30:02.147Z","durationMinutes":125}
+{"id":"OpenAI API-1776369001928","service":"OpenAI API","provider":"OpenAI","severity":"minor","title":"OpenAI API degraded performance","startedAt":"2026-04-16T19:50:01.928Z","resolvedAt":"2026-04-16T21:25:02.225Z","durationMinutes":95}
+{"id":"Claude API-1776378602747","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-16T22:30:02.747Z","resolvedAt":"2026-04-16T22:45:02.406Z","durationMinutes":15}
+{"id":"Claude API-1776452419039","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-17T19:00:19.039Z","resolvedAt":"2026-04-17T20:00:19.505Z","durationMinutes":60}
+{"id":"Replicate-1776609646014","service":"Replicate","provider":"Replicate","severity":"minor","title":"Replicate degraded performance","startedAt":"2026-04-19T14:40:46.014Z","resolvedAt":"2026-04-19T15:05:45.185Z","durationMinutes":25}
+{"id":"Claude API-1776665746689","service":"Claude API","provider":"Anthropic","severity":"major","title":"Claude API outage","startedAt":"2026-04-20T06:15:46.689Z","resolvedAt":"2026-04-20T06:45:48.191Z","durationMinutes":30}
+{"id":"Claude API-1776692136880","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-20T13:35:36.880Z","resolvedAt":"2026-04-20T14:25:36.911Z","durationMinutes":50}
+{"id":"OpenAI API-1776696037053","service":"OpenAI API","provider":"OpenAI","severity":"minor","title":"OpenAI API degraded performance","startedAt":"2026-04-20T14:40:37.053Z","resolvedAt":"2026-04-20T16:50:37.482Z","durationMinutes":130}
+{"id":"OpenAI API-1776708336881","service":"OpenAI API","provider":"OpenAI","severity":"minor","title":"OpenAI API degraded performance","startedAt":"2026-04-20T18:05:36.881Z","resolvedAt":"2026-04-20T18:25:36.774Z","durationMinutes":20}
+{"id":"Cohere-1776723636429","service":"Cohere","provider":"Cohere","severity":"major","title":"Cohere outage","startedAt":"2026-04-20T22:20:36.429Z","resolvedAt":"2026-04-20T22:30:36.567Z","durationMinutes":10}
+{"id":"OpenAI API-1776738946142","service":"OpenAI API","provider":"OpenAI","severity":"minor","title":"OpenAI API degraded performance","startedAt":"2026-04-21T02:35:46.142Z","resolvedAt":"2026-04-21T04:35:48.607Z","durationMinutes":120}
+{"id":"OpenAI API-1776819662083","service":"OpenAI API","provider":"OpenAI","severity":"major","title":"OpenAI API outage","startedAt":"2026-04-22T01:01:02.083Z","resolvedAt":"2026-04-22T03:35:54.809Z","durationMinutes":155}
+{"id":"OpenAI API-1776866144696","service":"OpenAI API","provider":"OpenAI","severity":"minor","title":"OpenAI API degraded performance","startedAt":"2026-04-22T13:55:44.696Z","resolvedAt":"2026-04-22T19:25:35.825Z","durationMinutes":330}
+{"id":"Claude API-1776905140734","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-23T00:45:40.734Z","resolvedAt":"2026-04-23T01:00:35.370Z","durationMinutes":15}
+{"id":"Claude API-1776958220752","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-23T15:30:20.752Z","resolvedAt":"2026-04-23T16:00:21.051Z","durationMinutes":30}
+{"id":"OpenAI API-1776962720958","service":"OpenAI API","provider":"OpenAI","severity":"minor","title":"OpenAI API degraded performance","startedAt":"2026-04-23T16:45:20.958Z","resolvedAt":"2026-04-23T18:00:27.444Z","durationMinutes":75}
+{"id":"OpenAI API-1776968722182","service":"OpenAI API","provider":"OpenAI","severity":"minor","title":"OpenAI API degraded performance","startedAt":"2026-04-23T18:25:22.182Z","resolvedAt":"2026-04-23T20:30:22.408Z","durationMinutes":125}
+{"id":"Claude API-1776999622359","service":"Claude API","provider":"Anthropic","severity":"major","title":"Claude API outage","startedAt":"2026-04-24T03:00:22.359Z","resolvedAt":"2026-04-24T03:35:22.438Z","durationMinutes":35}
+{"id":"Claude API-1777025433793","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-24T10:10:33.793Z","resolvedAt":"2026-04-24T10:25:33.444Z","durationMinutes":15}
+{"id":"Claude API-1777027233849","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-24T10:40:33.849Z","resolvedAt":"2026-04-24T10:55:32.867Z","durationMinutes":15}
+{"id":"Claude API-1777050332753","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-24T17:05:32.753Z","resolvedAt":"2026-04-24T17:35:32.721Z","durationMinutes":30}
+{"id":"OpenAI API-1777065918869","service":"OpenAI API","provider":"OpenAI","severity":"minor","title":"OpenAI API degraded performance","startedAt":"2026-04-24T21:25:18.869Z","resolvedAt":"2026-04-24T22:15:18.822Z","durationMinutes":50}
+{"id":"Claude API-1777081218536","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-25T01:40:18.536Z","resolvedAt":"2026-04-25T02:40:18.684Z","durationMinutes":60}
+{"id":"Claude API-1777103430297","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-25T07:50:30.297Z","resolvedAt":"2026-04-25T08:30:32.936Z","durationMinutes":40}
+{"id":"Claude API-1777106730774","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-25T08:45:30.774Z","resolvedAt":"2026-04-25T09:00:34.609Z","durationMinutes":15}
+{"id":"Claude API-1777379129468","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-28T12:25:29.468Z","resolvedAt":"2026-04-28T12:40:29.554Z","durationMinutes":15}
+{"id":"Claude API-1777383030948","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-28T13:30:30.948Z","resolvedAt":"2026-04-28T13:50:29.393Z","durationMinutes":20}
+{"id":"OpenAI API-1777384529221","service":"OpenAI API","provider":"OpenAI","severity":"minor","title":"OpenAI API degraded performance","startedAt":"2026-04-28T13:55:29.221Z","resolvedAt":"2026-04-28T16:15:28.994Z","durationMinutes":140}
+{"id":"Claude API-1777398331009","service":"Claude API","provider":"Anthropic","severity":"major","title":"Claude API outage","startedAt":"2026-04-28T17:45:31.009Z","resolvedAt":"2026-04-28T19:00:32.960Z","durationMinutes":75}
+{"id":"OpenAI API-1777416630011","service":"OpenAI API","provider":"OpenAI","severity":"minor","title":"OpenAI API degraded performance","startedAt":"2026-04-28T22:50:30.011Z","resolvedAt":"2026-04-28T23:15:29.861Z","durationMinutes":25}
+{"id":"Claude API-1777419330005","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-28T23:35:30.005Z","resolvedAt":"2026-04-29T00:05:30.041Z","durationMinutes":30}
+{"id":"OpenAI API-1777439745560","service":"OpenAI API","provider":"OpenAI","severity":"minor","title":"OpenAI API degraded performance","startedAt":"2026-04-29T05:15:45.560Z","resolvedAt":"2026-04-29T05:45:45.640Z","durationMinutes":30}
+{"id":"Claude API-1777470656278","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-29T13:50:56.278Z","resolvedAt":"2026-04-29T14:06:00.955Z","durationMinutes":15}
+{"id":"OpenAI API-1777496716309","service":"OpenAI API","provider":"OpenAI","severity":"minor","title":"OpenAI API degraded performance","startedAt":"2026-04-29T21:05:16.309Z","resolvedAt":null,"durationMinutes":null}
+{"id":"Claude API-1777512316106","service":"Claude API","provider":"Anthropic","severity":"major","title":"Claude API outage","startedAt":"2026-04-30T01:25:16.106Z","resolvedAt":"2026-04-30T01:55:16.090Z","durationMinutes":30}
+{"id":"Claude API-1777554627450","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-04-30T13:10:27.450Z","resolvedAt":"2026-04-30T14:05:19.657Z","durationMinutes":55}
+{"id":"Claude API-1777882203725","service":"Claude API","provider":"Anthropic","severity":"minor","title":"Claude API degraded performance","startedAt":"2026-05-04T08:10:03.725Z","resolvedAt":"2026-05-04T09:25:03.078Z","durationMinutes":75}

2026-05-04/manifest.json

@@ -1,13 +1,13 @@
 {
   "date": "2026-05-04",
-  "captured_at": "2026-05-04T05:25:46.621945+00:00",
+  "captured_at": "2026-05-04T10:09:56.641803+00:00",
   "source": "https://tensorfeed.ai",
   "feeds": {
     "news": {
       "status": "ok",
       "endpoint": "/api/news?limit=200",
-      "records": 122,
-      "bytes": 67947
+      "records": 107,
+      "bytes": 60099
     },
     "models": {
       "status": "ok",
@@ -43,13 +43,13 @@
       "status": "ok",
       "endpoint": "/api/agents/activity",
       "records": 50,
-      "bytes": 4894
+      "bytes": 4875
     },
     "podcasts": {
       "status": "ok",
       "endpoint": "/api/podcasts",
       "records": 50,
-      "bytes": 45544
+      "bytes": 45660
     },
     "trending-repos": {
       "status": "ok",
@@ -61,19 +61,19 @@
       "status": "ok",
       "endpoint": "/api/mcp/registry/snapshot",
       "records": 1,
-      "bytes": 3167
+      "bytes": 3209
     },
     "probe": {
       "status": "ok",
       "endpoint": "/api/probe/latest",
       "records": 1,
-      "bytes": 1330
+      "bytes": 1334
     },
     "gpu-pricing": {
       "status": "ok",
       "endpoint": "/api/gpu/pricing",
       "records": 1,
-      "bytes": 251033
+      "bytes": 239280
     },
     "afta-adopters": {
       "status": "ok",
@@ -169,7 +169,7 @@
       "status": "ok",
       "endpoint": "/api/benchmark-registry",
       "records": 24,
-      "bytes": 14939
+      "bytes": 17185
     },
     "public-leaderboards": {
       "status": "ok",
@@ -218,6 +218,54 @@
       "endpoint": "/api/agent-provisioning",
       "records": 18,
       "bytes": 6486
+    },
+    "training-datasets": {
+      "status": "ok",
+      "endpoint": "/api/training-datasets",
+      "records": 19,
+      "bytes": 8878
+    },
+    "mcp-servers": {
+      "status": "ok",
+      "endpoint": "/api/mcp-servers",
+      "records": 30,
+      "bytes": 11590
+    },
+    "attention": {
+      "status": "ok",
+      "endpoint": "/api/attention",
+      "records": 12,
+      "bytes": 4907
+    },
+    "incidents": {
+      "status": "ok",
+      "endpoint": "/api/incidents",
+      "records": 70,
+      "bytes": 16543
+    },
+    "harnesses": {
+      "status": "ok",
+      "endpoint": "/api/harnesses",
+      "records": 4,
+      "bytes": 1253
+    },
+    "embodied-ai": {
+      "status": "ok",
+      "endpoint": "/api/embodied-ai",
+      "records": 25,
+      "bytes": 11115
+    },
+    "ai-lawsuits": {
+      "status": "ok",
+      "endpoint": "/api/ai-lawsuits",
+      "records": 19,
+      "bytes": 15999
+    },
+    "x402-adopters": {
+      "status": "ok",
+      "endpoint": "/api/x402-adopters",
+      "records": 9,
+      "bytes": 5516
     }
   }
 }

2026-05-04/mcp-registry.jsonl

@@ -1 +1 @@
-{"ok":true,"summary":{"date":"2026-05-03","capturedAt":"2026-05-03T09:30:57.312Z","total_servers":7442,"total_versions":22049,"by_status":{"active":7419,"deprecated":23},"top_namespaces":[{"namespace":"io.github.pipeworx-io","count":252},{"namespace":"ai.smithery","count":214},{"namespace":"eu.ansvar","count":105},{"namespace":"io.github.Br0ski777","count":101},{"namespace":"io.github.codespar","count":57},{"namespace":"io.github.srotzin","count":53},{"namespace":"io.github.ryudi84","count":49},{"namespace":"io.github.theYahia","count":49},{"namespace":"io.github.daedalus","count":48},{"namespace":"com.olyport","count":38},{"namespace":"io.github.rog0x","count":33},{"namespace":"com.thenextgennexus","count":27},{"namespace":"io.github.Dave-London","count":26},{"namespace":"io.github.geraservicesuk","count":25},{"namespace":"io.github.junct-bot","count":24},{"namespace":"io.github.cyanheads","count":23},{"namespace":"io.github.nirholas","count":23},{"namespace":"io.github.markswendsen-code","count":19},{"namespace":"io.github.domdomegg","count":18},{"namespace":"io.github.Ansvar-Systems","count":17}],"new_today":{"count":54,"names":["app.readwithleaf/leaf","bot.mailbox/mailbox","cc.wealthos/mcp","com.cymetica/event-trader-mcp","com.phasefolio/phasefolio","com.providersignal/dental-market-intelligence","com.tradallo/reputation","dev.urantia/urantia-papers","io.github.AAAA-Nexus/aaaa-nexus-mcp","io.github.BlackMount-ai/blackmount-construction-mcp","io.github.Cmoen11/redm-mcp","io.github.Davison-Francis/deliveriq-mcp","io.github.Ipayx-stellar/fx-audit","io.github.JuhongPark/pronunciation","io.github.Neo1228/swagger-mcp-bridge","io.github.abenassi/argentina-data","io.github.adebayojuwon200-pixel/layergen-mcp","io.github.alpineflow-io/swiss-whale-intelligence","io.github.atmflow55/datafood-mcp","io.github.atomadictech/atomadic-forge","io.github.aurekai/aurekai-mcp","io.github.ayo-nci/bulkrender","io.github.bitgett/q402-mcp","io.github.byimprint/intelligence","io.github.capratesignals-cpu/cap-rate-signals","io.github.emergencescience/mcp","io.github.eranimore/surf-session-finder","io.github.ironflowsh/mcp","io.github.jackgillen15-dev/scope-mcp","io.github.jayvee6/apple-mail-mcp","io.github.jonathanpchapman/inferventis","io.github.kapruka/reviewguru","io.github.kiwoongeom/gmc-mcp","io.github.kunwar-shah/claudex","io.github.kushknows/weather-mcp-server","io.github.nowork-studio/notfair-meta-ads","io.github.openpulsechain/analytics","io.github.opjhabuilds/designpin-mcp-server","io.github.pduggusa/dugganusa-threat-intel","io.github.pquattro/memoraeu","io.github.printmoneylab/x402watch","io.github.scope-bid/scope-aec-mcp","io.github.scope-bid/scope-claims-mcp","io.github.scope-bid/scope-mcp","io.github.simul813/kdp-niche-research","io.github.smartmemory/stratum-mcp","io.github.snenenenenenene/transita-mcp-server","io.github.spencerpauly/querybear","io.github.spuentesp/lain","io.github.srotzin/hive-mcp-attest"]},"reactivated_today":{"count":0,"names":[]},"deprecated_today":{"count":1,"names":["io.github.mefengl/jail-mcp"]},"delta_vs_yesterday":{"added":54,"removed":1,"net":53},"pages_fetched":221,"fetch_truncated":false}}
+{"ok":true,"summary":{"date":"2026-05-04","capturedAt":"2026-05-04T09:30:01.895Z","total_servers":7511,"total_versions":22295,"by_status":{"active":7487,"deprecated":24},"top_namespaces":[{"namespace":"io.github.pipeworx-io","count":252},{"namespace":"ai.smithery","count":214},{"namespace":"eu.ansvar","count":105},{"namespace":"io.github.Br0ski777","count":101},{"namespace":"io.github.srotzin","count":60},{"namespace":"io.github.codespar","count":57},{"namespace":"io.github.ryudi84","count":49},{"namespace":"io.github.theYahia","count":49},{"namespace":"io.github.daedalus","count":48},{"namespace":"com.olyport","count":38},{"namespace":"io.github.rog0x","count":33},{"namespace":"com.thenextgennexus","count":27},{"namespace":"io.github.Dave-London","count":26},{"namespace":"io.github.geraservicesuk","count":25},{"namespace":"io.github.junct-bot","count":24},{"namespace":"io.github.cyanheads","count":23},{"namespace":"io.github.nirholas","count":23},{"namespace":"io.github.markswendsen-code","count":19},{"namespace":"io.github.domdomegg","count":18},{"namespace":"io.github.Ansvar-Systems","count":17}],"new_today":{"count":69,"names":["ai.buyersense/buyersense","ai.law/lawyer-search","com.keenethics-labs/mindmap","com.markifact/mcp","com.revensi/revensi","com.zahabprice/gold-prices","gg.renewly/contracts","io.github.BuyWhere/buywhere-mcp","io.github.BuyWhere/buywhere-mcp-server","io.github.Decodo/mcp-server","io.github.Ivanolegvak/sonar-mcp","io.github.MatchupLabs/nycfhv","io.github.NotHarshhaa/devops-mcp","io.github.OjasKord/quantum-suitability-validator-mcp-server","io.github.Ooak21/cortex402","io.github.Quant-M2M/API-Quant-M2M","io.github.RetroRobAI/mcp-memory-graph","io.github.Semiotronika/NOUZ-MCP","io.github.TelcharFromNogrod/keen","io.github.TradeSystemsNique/mt5-mcp-by-leo","io.github.World-999-Labs/proof-layer","io.github.ai-elara/code-context-engine","io.github.bbb-build/mascope-mcp","io.github.bbb-build/touchgrass-mcp","io.github.burakaydinofficial/searxng-deepdive","io.github.caseSHY/aicoreutils","io.github.chrischall/canvas-parent-mcp","io.github.chrischall/creditkarma-mcp","io.github.chrischall/ofw-mcp","io.github.chrischall/splitwise-mcp","io.github.chrischall/tempo-api-mcp","io.github.chrischall/zola-mcp","io.github.dame-senders/senders-mcp","io.github.groupdocs-comparison/groupdocs-comparison-mcp","io.github.groupdocs-viewer/groupdocs-viewer-mcp","io.github.jpazvd/unicefstats-mcp","io.github.l-agence/agence","io.github.lordbasilaiassistant-sudo/thryx-portfolio","io.github.mesutoezdil/mcp-gpu-server","io.github.myrouteonline/myrouteonline","io.github.revolutionarybukhari/psx-mcp","io.github.robert19001-cmyk/mcp-lint","io.github.sanjana-pier39/negotiate-mcp","io.github.shemshallah/qtcl-mcp-server","io.github.srgplus/srgplus-mcp","io.github.srotzin/hive-mcp-address-screen","io.github.srotzin/hive-mcp-credit-broker","io.github.srotzin/hive-mcp-keet-bridge","io.github.srotzin/hive-mcp-ledger-bridge","io.github.srotzin/hive-mcp-mpp"]},"reactivated_today":{"count":0,"names":[]},"deprecated_today":{"count":1,"names":["io.studiomeyer/mcp-suite"]},"delta_vs_yesterday":{"added":69,"removed":0,"net":69},"pages_fetched":223,"fetch_truncated":false}}

2026-05-04/mcp-servers.jsonl

@@ -0,0 +1,30 @@
+{"id":"filesystem","name":"Filesystem","vendor":"Anthropic","capabilities":["filesystem"],"transports":["stdio"],"firstParty":true,"language":"typescript","license":"MIT","install":"npx -y @modelcontextprotocol/server-filesystem /path/to/dir","url":"https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem","notes":"Read/write/list files in a sandboxed directory. The most-installed MCP server. Default for any agent that needs local file access."}
+{"id":"fetch","name":"Fetch","vendor":"Anthropic","capabilities":["web-fetch"],"transports":["stdio"],"firstParty":true,"language":"python","license":"MIT","install":"pip install mcp-server-fetch","url":"https://github.com/modelcontextprotocol/servers/tree/main/src/fetch","notes":"Convert URLs to markdown for LLM consumption. Lightweight web reader; pair with Brave or Tavily for search."}
+{"id":"memory","name":"Memory","vendor":"Anthropic","capabilities":["memory"],"transports":["stdio"],"firstParty":true,"language":"typescript","license":"MIT","install":"npx -y @modelcontextprotocol/server-memory","url":"https://github.com/modelcontextprotocol/servers/tree/main/src/memory","notes":"Knowledge graph persisted across sessions. The reference long-term-memory implementation; many agents fork this as their starting point."}
+{"id":"sequential-thinking","name":"Sequential Thinking","vendor":"Anthropic","capabilities":["memory"],"transports":["stdio"],"firstParty":true,"language":"typescript","license":"MIT","install":"npx -y @modelcontextprotocol/server-sequential-thinking","url":"https://github.com/modelcontextprotocol/servers/tree/main/src/sequentialthinking","notes":"Structured chain-of-thought tool. Surprisingly useful as a poke for models that under-plan."}
+{"id":"brave-search","name":"Brave Search","vendor":"Brave","capabilities":["web-search"],"transports":["stdio"],"firstParty":true,"language":"typescript","license":"MIT","install":"npx -y @modelcontextprotocol/server-brave-search","url":"https://github.com/modelcontextprotocol/servers/tree/main/src/brave-search","notes":"Web search via Brave Search API. 2k free queries/month. Cheaper than Google CSE for agent workloads."}
+{"id":"tavily","name":"Tavily","vendor":"Tavily","capabilities":["web-search"],"transports":["stdio","http"],"firstParty":true,"language":"typescript","license":"MIT","install":"npx -y @tavily/mcp","url":"https://github.com/tavily-ai/tavily-mcp","notes":"AI-optimized web search. Returns structured snippets and full-page extraction. Standard search MCP for research agents."}
+{"id":"exa","name":"Exa","vendor":"Exa Labs","capabilities":["web-search"],"transports":["stdio"],"firstParty":true,"language":"typescript","license":"MIT","install":"npx -y exa-mcp-server","url":"https://github.com/exa-labs/exa-mcp-server","notes":"Embedding-based search. Better than keyword for \"find me content like X\" queries."}
+{"id":"playwright","name":"Playwright MCP","vendor":"Microsoft","capabilities":["browser","puppeteer"],"transports":["stdio"],"firstParty":true,"language":"typescript","license":"Apache-2.0","install":"npx -y @playwright/mcp","url":"https://github.com/microsoft/playwright-mcp","notes":"Microsoft's official Playwright MCP. Accessibility-tree based, not screenshot. Faster and more reliable than vision-based browser agents."}
+{"id":"browser-use-mcp","name":"Browser Use MCP","vendor":"Browser Use","capabilities":["browser"],"transports":["stdio"],"firstParty":true,"language":"python","license":"MIT","install":"pip install browser-use-mcp","url":"https://github.com/browser-use/browser-use-mcp","notes":"Wraps the browser-use library. Vision-based browser agent with strong WebArena performance."}
+{"id":"puppeteer","name":"Puppeteer","vendor":"Anthropic","capabilities":["browser","puppeteer"],"transports":["stdio"],"firstParty":true,"language":"typescript","license":"MIT","install":"npx -y @modelcontextprotocol/server-puppeteer","url":"https://github.com/modelcontextprotocol/servers/tree/main/src/puppeteer","notes":"Reference Puppeteer MCP. Headless Chrome with screenshot + click + form-fill. Older than Playwright MCP but well-tested."}
+{"id":"github","name":"GitHub","vendor":"GitHub","capabilities":["github"],"transports":["stdio","http"],"firstParty":true,"language":"typescript","license":"MIT","install":"npx -y @github/github-mcp-server","url":"https://github.com/github/github-mcp-server","notes":"GitHub's official MCP. Issues, PRs, files, search, actions. The most-installed third-party MCP after the Anthropic reference servers."}
+{"id":"gitlab","name":"GitLab","vendor":"GitLab","capabilities":["gitlab"],"transports":["stdio"],"firstParty":true,"language":"typescript","license":"MIT","install":"npx -y @modelcontextprotocol/server-gitlab","url":"https://github.com/modelcontextprotocol/servers/tree/main/src/gitlab","notes":"GitLab API access. Issues, MRs, pipelines."}
+{"id":"slack","name":"Slack","vendor":"Anthropic","capabilities":["slack"],"transports":["stdio"],"firstParty":true,"language":"typescript","license":"MIT","install":"npx -y @modelcontextprotocol/server-slack","url":"https://github.com/modelcontextprotocol/servers/tree/main/src/slack","notes":"Read channels, post messages, search. Bot-token auth."}
+{"id":"gmail","name":"Gmail","vendor":"Google","capabilities":["gmail"],"transports":["stdio","http"],"firstParty":false,"language":"typescript","license":"MIT","install":"npx -y @gongrzhe/server-gmail-autoauth-mcp","url":"https://github.com/GongRzhe/Gmail-MCP-Server","notes":"Read and send Gmail. OAuth flow built in. Most-deployed Gmail MCP."}
+{"id":"notion","name":"Notion","vendor":"Notion","capabilities":["notion"],"transports":["stdio","http"],"firstParty":true,"language":"typescript","license":"MIT","install":"npx -y @notionhq/notion-mcp-server","url":"https://github.com/makenotion/notion-mcp-server","notes":"Notion's official MCP. Pages, databases, search. Use for any agent working with Notion as a knowledge base."}
+{"id":"linear","name":"Linear","vendor":"Linear","capabilities":["linear"],"transports":["stdio","http"],"firstParty":true,"language":"typescript","license":"MIT","install":"npx -y @linear/mcp-server","url":"https://github.com/linear/linear-mcp-server","notes":"Linear's official MCP. Issues, projects, comments. Workflow-grade integration for engineering agents."}
+{"id":"gdrive","name":"Google Drive","vendor":"Anthropic","capabilities":["gdrive"],"transports":["stdio"],"firstParty":true,"language":"typescript","license":"MIT","install":"npx -y @modelcontextprotocol/server-gdrive","url":"https://github.com/modelcontextprotocol/servers/tree/main/src/gdrive","notes":"Read Google Drive files. OAuth required."}
+{"id":"gcal","name":"Google Calendar","vendor":"community","capabilities":["gcal"],"transports":["stdio"],"firstParty":false,"language":"typescript","license":"MIT","install":"npx -y @cocal/google-calendar-mcp","url":"https://github.com/nspady/google-calendar-mcp","notes":"Read/create calendar events. Most-installed community Gcal MCP."}
+{"id":"postgres","name":"PostgreSQL","vendor":"Anthropic","capabilities":["database"],"transports":["stdio"],"firstParty":true,"language":"typescript","license":"MIT","install":"npx -y @modelcontextprotocol/server-postgres postgres://user:pass@host:5432/db","url":"https://github.com/modelcontextprotocol/servers/tree/main/src/postgres","notes":"Read-only Postgres queries. Schema introspection + ad-hoc SQL. Defaults safe (read-only)."}
+{"id":"sqlite","name":"SQLite","vendor":"Anthropic","capabilities":["database"],"transports":["stdio"],"firstParty":true,"language":"typescript","license":"MIT","install":"npx -y @modelcontextprotocol/server-sqlite path/to/db.sqlite","url":"https://github.com/modelcontextprotocol/servers/tree/main/src/sqlite","notes":"Local SQLite access. Useful for agent dev, data exploration, lightweight memory."}
+{"id":"supabase","name":"Supabase","vendor":"Supabase","capabilities":["database"],"transports":["stdio"],"firstParty":true,"language":"typescript","license":"Apache-2.0","install":"npx -y @supabase/mcp-server-supabase","url":"https://github.com/supabase-community/supabase-mcp","notes":"Supabase's official MCP. SQL, storage, auth. Project management for agents that own a Supabase project."}
+{"id":"aws","name":"AWS","vendor":"AWS","capabilities":["aws"],"transports":["stdio"],"firstParty":true,"language":"python","license":"Apache-2.0","install":"pipx install awslabs.aws-mcp-server","url":"https://github.com/awslabs/mcp","notes":"AWS Labs official MCP collection. Covers core AWS services + Bedrock + DynamoDB + S3."}
+{"id":"cloudflare","name":"Cloudflare","vendor":"Cloudflare","capabilities":["cloudflare"],"transports":["stdio","http"],"firstParty":true,"language":"typescript","license":"MIT","install":"npx -y @cloudflare/mcp-server-cloudflare","url":"https://github.com/cloudflare/mcp-server-cloudflare","notes":"Cloudflare's official MCP. Workers, KV, R2, D1, Pages. Manage account from agent."}
+{"id":"vercel","name":"Vercel","vendor":"community","capabilities":["vercel"],"transports":["stdio"],"firstParty":false,"language":"typescript","license":"MIT","install":"npx -y vercel-mcp","url":"https://github.com/Quegenx/vercel-mcp-server","notes":"Deploy, list, inspect Vercel projects. Most-installed community Vercel MCP."}
+{"id":"sentry","name":"Sentry","vendor":"Sentry","capabilities":["sentry","observability"],"transports":["stdio","http"],"firstParty":true,"language":"python","license":"BUSL-1.1","install":"uvx mcp-server-sentry","url":"https://github.com/getsentry/sentry-mcp","notes":"Read Sentry issues + events. Useful for agent-driven incident triage."}
+{"id":"datadog","name":"Datadog","vendor":"community","capabilities":["datadog","observability"],"transports":["stdio"],"firstParty":false,"language":"typescript","license":"MIT","install":"npx -y datadog-mcp","url":"https://github.com/datadog-mcp/datadog-mcp","notes":"Query Datadog metrics, logs, monitors. Read-only by default. Pairs with Sentry for full observability access."}
+{"id":"shell","name":"Shell","vendor":"community","capabilities":["shell"],"transports":["stdio"],"firstParty":false,"language":"typescript","license":"MIT","install":"npx -y mcp-shell-server","url":"https://github.com/tumf/mcp-shell-server","notes":"Run shell commands from MCP. Default-deny safelist; explicit per-command approval. Use with care."}
+{"id":"tensorfeed","name":"TensorFeed","vendor":"TensorFeed.ai","capabilities":["analytics","web-search"],"transports":["stdio"],"firstParty":true,"language":"typescript","license":"MIT","install":"npx -y @tensorfeed/mcp-server","url":"https://github.com/RipperMercs/tensorfeed/tree/main/mcp-server","notes":"AI ecosystem data tools: news, status, models, benchmarks, harnesses, attention index, embeddings, vector DBs, frameworks, usage rankings. Free tier; paid premium tools require a USDC credit token."}
+{"id":"stripe","name":"Stripe","vendor":"Stripe","capabilities":["fetch-payment"],"transports":["stdio"],"firstParty":true,"language":"typescript","license":"MIT","install":"npx -y @stripe/mcp","url":"https://github.com/stripe/agent-toolkit","notes":"Stripe's official MCP. Customers, subscriptions, products. Restricted-key auth recommended."}
+{"id":"elevenlabs","name":"ElevenLabs","vendor":"ElevenLabs","capabilities":["voice"],"transports":["stdio"],"firstParty":true,"language":"python","license":"MIT","install":"pip install elevenlabs-mcp","url":"https://github.com/elevenlabs/elevenlabs-mcp","notes":"Generate speech, manage voices, cloning. Useful for agents that need to synthesize audio output."}

2026-05-04/podcasts.jsonl

@@ -1,50 +1,50 @@
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2026-05-04/probe.jsonl

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2026-05-04/status.jsonl

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2026-05-04/training-datasets.jsonl

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+{"id":"fineweb-edu","name":"FineWeb-Edu","publisher":"Hugging Face","stage":"pretraining","contentType":"educational web text","tokens":"1.3T","items":"1.3B documents","license":"ODC-BY-1.0","languages":"English","released":"2024-05","url":"https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu","notes":"FineWeb subset filtered to educational content via Llama-3-70B classifier. Smaller models trained on FineWeb-Edu beat counterparts trained on full FineWeb at the same compute."}
+{"id":"common-crawl","name":"Common Crawl","publisher":"Common Crawl Foundation","stage":"pretraining","contentType":"web text","tokens":"petabyte-scale raw","items":"250B+ webpages","license":"CC-0","languages":"multilingual (200+)","released":"monthly since 2008","url":"https://commoncrawl.org","notes":"The raw web crawl every other web pretraining dataset is built on. Released monthly. Most labs filter it heavily before training."}
+{"id":"redpajama-v2","name":"RedPajama v2","publisher":"Together AI","stage":"pretraining","contentType":"web text","tokens":"30T","items":"100B+ documents","license":"MIT (filters), CC-BY (subsets)","languages":"English, German, French, Spanish, Italian","released":"2023-10","url":"https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2","notes":"Together AI's open reproduction of LLaMA-style pretraining data. v2 ships with 40+ pre-computed quality filters so labs can apply their own thresholds."}
+{"id":"the-pile","name":"The Pile","publisher":"EleutherAI","stage":"pretraining","contentType":"mixed (web + books + papers + code)","tokens":"825GB / 300B","items":"22 sub-corpora","license":"mixed (per-subcorpus)","languages":"English-focused","released":"2020","url":"https://pile.eleuther.ai","notes":"The original open pretraining corpus. Largely superseded by FineWeb / RedPajama for new training runs but still cited. Note: Books3 subset removed for copyright."}
+{"id":"dolma","name":"Dolma","publisher":"Allen AI (AI2)","stage":"pretraining","contentType":"web + code + books + papers","tokens":"3T","items":"mixed","license":"AI2 ImpACT (Medium Risk)","languages":"English","released":"2024-02","url":"https://huggingface.co/datasets/allenai/dolma","notes":"AI2's open pretraining corpus (the dataset behind OLMo). Documents source provenance and offers reproducible filters. The most-academically-cited open pretraining set."}
+{"id":"refinedweb","name":"RefinedWeb","publisher":"TII","stage":"pretraining","contentType":"web text","tokens":"5T","items":"600M documents","license":"ODC-BY-1.0","languages":"English","released":"2023-06","url":"https://huggingface.co/datasets/tiiuae/falcon-refinedweb","notes":"Behind the Falcon series. Aggressive deduplication and filtering. Older but cleaner than CommonCrawl raw."}
+{"id":"the-stack-v2","name":"The Stack v2","publisher":"BigCode (HuggingFace + ServiceNow)","stage":"pretraining","contentType":"source code","tokens":"67TB / 17B files","items":"17B files across 600+ languages","license":"permissive only (filtered)","languages":"600+ programming languages","released":"2024-02","url":"https://huggingface.co/datasets/bigcode/the-stack-v2","notes":"The reference open code-pretraining corpus. v2 is sourced from Software Heritage, dedup'd at file and near-dup level. Behind StarCoder 2 and most open code models."}
+{"id":"starcoderdata","name":"StarCoderData","publisher":"BigCode","stage":"pretraining","contentType":"source code + jupyter + GitHub issues","tokens":"305B","items":"86 languages","license":"mixed permissive","languages":"86 programming languages","released":"2023-05","url":"https://huggingface.co/datasets/bigcode/starcoderdata","notes":"Pre-cleaned subset of The Stack used to train StarCoder. Smaller than The Stack v2 but easier to work with for small-scale code-model experiments."}
+{"id":"tulu-3-sft-mix","name":"Tulu 3 SFT Mixture","publisher":"Allen AI","stage":"instruction-tuning","contentType":"instruction-response pairs","tokens":null,"items":"939K instructions","license":"ODC-BY-1.0","languages":"English","released":"2024-11","url":"https://huggingface.co/datasets/allenai/tulu-3-sft-mixture","notes":"Tulu 3 is the strongest open post-training recipe in 2024-2025. The SFT mixture combines OpenAssistant, ShareGPT, math reasoning, code instructions, and persona-aligned prompts."}
+{"id":"open-hermes-2.5","name":"OpenHermes 2.5","publisher":"Teknium / Nous Research","stage":"instruction-tuning","contentType":"multi-turn conversations","tokens":null,"items":"1M conversations","license":"mixed (per-source)","languages":"English","released":"2023-12","url":"https://huggingface.co/datasets/teknium/OpenHermes-2.5","notes":"Aggregated from GPT-4 outputs, AiroborosLM, ShareGPT, and others. The dataset behind Nous Hermes and many community fine-tunes."}
+{"id":"glaive-function-calling","name":"Glaive Function Calling v2","publisher":"Glaive AI","stage":"instruction-tuning","contentType":"function-calling traces","tokens":null,"items":"113K examples","license":"Apache-2.0","languages":"English","released":"2023-12","url":"https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2","notes":"Synthetic function-calling traces. The most-cited dataset for fine-tuning open models to do reliable JSON tool use."}
+{"id":"open-orca","name":"OpenOrca","publisher":"Open Orca team","stage":"instruction-tuning","contentType":"reasoning traces","tokens":null,"items":"4.2M GPT-4 + 0.7M GPT-3.5 examples","license":"MIT","languages":"English","released":"2023-07","url":"https://huggingface.co/datasets/Open-Orca/OpenOrca","notes":"Reproduction of Microsoft Orca paper. Augments FLAN tasks with GPT-4 chain-of-thought traces. Strong base for math/reasoning fine-tunes."}
+{"id":"agent-instruct","name":"AgentInstruct","publisher":"Microsoft Research","stage":"instruction-tuning","contentType":"agentic task traces","tokens":null,"items":"25M agent-shaped instruction pairs","license":"CDLA-Permissive-2.0","languages":"English","released":"2024-07","url":"https://huggingface.co/datasets/microsoft/AgentInstruct-1M-v1","notes":"Synthetic dataset for agent training. Multi-step tool-use, reading comprehension, code, RAG, brain teasers, content creation. Behind Orca-3 and used in Phi-3.5."}
+{"id":"ultrafeedback","name":"UltraFeedback","publisher":"OpenBMB","stage":"dpo","contentType":"preference pairs","tokens":null,"items":"64K instructions x 4 model responses","license":"MIT","languages":"English","released":"2023-10","url":"https://huggingface.co/datasets/openbmb/UltraFeedback","notes":"Most-used DPO dataset for open models. GPT-4 ranks 4 candidate responses per prompt across helpfulness, honesty, instruction-following, truthfulness."}
+{"id":"tulu-3-pref","name":"Tulu 3 Preference Mixture","publisher":"Allen AI","stage":"dpo","contentType":"preference pairs","tokens":null,"items":"270K preference pairs","license":"ODC-BY-1.0","languages":"English","released":"2024-11","url":"https://huggingface.co/datasets/allenai/llama-3.1-tulu-3-70b-preference-mixture","notes":"Curated preference pairs for the Tulu 3 DPO stage. The strongest open preference data for post-training in late 2024."}
+{"id":"helpsteer-2","name":"HelpSteer 2","publisher":"NVIDIA","stage":"dpo","contentType":"preference pairs (5-attribute)","tokens":null,"items":"21K conversations","license":"CC-BY-4.0","languages":"English","released":"2024-06","url":"https://huggingface.co/datasets/nvidia/HelpSteer2","notes":"NVIDIA preference data labeled across helpfulness, correctness, coherence, complexity, verbosity. Strong for multi-attribute reward modeling."}
+{"id":"laion-5b","name":"LAION-5B","publisher":"LAION","stage":"multimodal","contentType":"image-text pairs","tokens":null,"items":"5.85B image-text pairs","license":"CC-BY-4.0","languages":"multilingual","released":"2022-03","url":"https://laion.ai/blog/laion-5b/","notes":"Largest open image-text dataset. Behind Stable Diffusion and many open image models. Original release was withdrawn for review; v2 reissued 2023."}
+{"id":"datacomp-1b","name":"DataComp-1B","publisher":"DataComp","stage":"multimodal","contentType":"image-text pairs","tokens":null,"items":"1.4B filtered pairs","license":"mixed","languages":"multilingual","released":"2023","url":"https://www.datacomp.ai","notes":"Filtered subset of CommonPool. Reproducible filtering recipes are part of the contribution. Behind several open CLIP-class models trained in 2024."}

2026-05-04/x402-adopters.jsonl

@@ -0,0 +1,9 @@
+{"id":"x402-spec","name":"x402 specification","org":"Coinbase + community","category":"spec","status":"spec","networks":["chain-agnostic"],"tokens":["stablecoin-agnostic"],"x402Methods":["exact","stripe","method-agnostic"],"endpointUrl":null,"websiteUrl":"https://x402.org","repoUrl":"https://github.com/coinbase/x402","docsUrl":"https://x402.org/docs","lastVerified":"2026-05-04","notes":"The HTTP-status-402 protocol that revives the long-dormant \"Payment Required\" status code as a machine-payable handshake. Method-agnostic by design: \"exact\" for direct on-chain transfers, \"stripe\" for the Stripe Link variant, future methods can be added."}
+{"id":"tensorfeed","name":"TensorFeed.ai","org":"Pizza Robot Studios","category":"publisher","status":"live","networks":["base"],"tokens":["USDC"],"x402Methods":["exact"],"endpointUrl":"https://tensorfeed.ai/api/premium","websiteUrl":"https://tensorfeed.ai/developers/agent-payments","repoUrl":"https://github.com/RipperMercs/tensorfeed","docsUrl":"https://tensorfeed.ai/.well-known/x402.json","lastVerified":"2026-05-04","notes":"Real-time AI-ecosystem data with 14 paid premium endpoints. AFTA-certified: code-enforced no-charge guarantees plus Ed25519-signed receipts on every paid call. End-to-end USDC loop verified on Base mainnet 2026-04-27."}
+{"id":"terminalfeed","name":"TerminalFeed.io","org":"Pizza Robot Studios","category":"publisher","status":"live","networks":["base"],"tokens":["USDC"],"x402Methods":["exact"],"endpointUrl":"https://terminalfeed.io/api/premium","websiteUrl":"https://terminalfeed.io","repoUrl":null,"docsUrl":"https://terminalfeed.io/.well-known/x402.json","lastVerified":"2026-05-04","notes":"Real-time data dashboard. AFTA-federated with TensorFeed: a single bearer token works on both sites via the cross-Worker validate + commit rail. Independent Ed25519 receipt keypair, shared credit ledger."}
+{"id":"stripe-link-agents","name":"Stripe Link Agents","org":"Stripe","category":"publisher","status":"announced","networks":["ethereum-mainnet","base"],"tokens":["USDC"],"x402Methods":["stripe"],"endpointUrl":null,"websiteUrl":"https://link.com/agents","repoUrl":null,"docsUrl":"https://docs.stripe.com/link/agents","lastVerified":"2026-05-04","notes":"April 2026 announcement: Stripe Link extended to AI agents using x402 with a `stripe` method for Shared Payment Tokens. Same x402 protocol envelope, different settlement scheme. General availability rolling out across the Stripe payments platform through 2026."}
+{"id":"coinbase-x402-sdk","name":"@coinbase/x402","org":"Coinbase","category":"sdk","status":"sdk","networks":["base","ethereum-mainnet"],"tokens":["USDC"],"x402Methods":["exact"],"endpointUrl":null,"websiteUrl":"https://www.npmjs.com/package/@coinbase/x402","repoUrl":"https://github.com/coinbase/x402","docsUrl":"https://x402.org/docs","lastVerified":"2026-05-04","notes":"Reference TypeScript SDK from Coinbase. Provides client + server middleware for the `exact` x402 method, plus Express/Fastify/Hono adapters. The fastest way to add x402 to an existing Node API."}
+{"id":"tensorfeed-python-sdk","name":"tensorfeed (Python)","org":"Pizza Robot Studios","category":"sdk","status":"sdk","networks":["base"],"tokens":["USDC"],"x402Methods":["exact"],"endpointUrl":null,"websiteUrl":"https://pypi.org/project/tensorfeed/","repoUrl":"https://github.com/RipperMercs/tensorfeed/tree/main/sdk/python","docsUrl":"https://tensorfeed.ai/developers/agent-payments","lastVerified":"2026-05-04","notes":"Python SDK with optional [web3] extra for one-call USDC sign-and-send. Targets the TensorFeed API but the payment helpers are reusable for any AFTA-compliant publisher."}
+{"id":"tensorfeed-js-sdk","name":"tensorfeed (JavaScript)","org":"Pizza Robot Studios","category":"sdk","status":"sdk","networks":["base"],"tokens":["USDC"],"x402Methods":["exact"],"endpointUrl":null,"websiteUrl":"https://www.npmjs.com/package/tensorfeed","repoUrl":"https://github.com/RipperMercs/tensorfeed/tree/main/sdk/javascript","docsUrl":"https://tensorfeed.ai/developers/agent-payments","lastVerified":"2026-05-04","notes":"TypeScript SDK with full coverage of free + premium endpoints and the x402 confirm flow."}
+{"id":"afta-gateway","name":"afta-gateway","org":"Pizza Robot Studios","category":"gateway","status":"gateway","networks":["base"],"tokens":["USDC"],"x402Methods":["exact"],"endpointUrl":null,"websiteUrl":"https://github.com/RipperMercs/afta-gateway","repoUrl":"https://github.com/RipperMercs/afta-gateway","docsUrl":"https://tensorfeed.ai/agent-fair-trade","lastVerified":"2026-05-04","notes":"Drop-in Cloudflare Worker template. Wraps any HTTP API in AFTA primitives: USDC verification, bearer-token credits, Ed25519-signed receipts, code-enforced no-charge ledger. MIT, no protocol fee, no signup. Fork, set 3 secrets, deploy."}
+{"id":"tensorfeed-mcp","name":"tensorfeed-mcp (MCP server)","org":"Pizza Robot Studios","category":"reference","status":"live","networks":["base"],"tokens":["USDC"],"x402Methods":["exact"],"endpointUrl":null,"websiteUrl":"https://github.com/RipperMercs/tensorfeed-mcp","repoUrl":"https://github.com/RipperMercs/tensorfeed-mcp","docsUrl":"https://registry.modelcontextprotocol.io/v0/servers/ai.tensorfeed/mcp-server","lastVerified":"2026-05-04","notes":"MCP server that consumes a TensorFeed bearer token (paid via USDC on Base) and exposes 14 paid tools to any MCP client (Claude Desktop, Claude Code, Cursor, Cline, etc). Reference example of how an MCP server can wrap an x402 publisher."}