The signal, not the noise, in AI news

How it works

• 01

  Every story is scored

  Each morning's stories are rated for how structurally important they are: a major model release or landmark ruling scores high; routine updates and minor commentary score low.

• 02

  The layout follows the score

  The single most important story leads the homepage, the next handful run as full cards, and everything more routine collects in a compact “In Brief” list, so what mattered is obvious at a glance.

• 03

  Read here, verify anywhere

  Every headline opens on this site first, with our own summary and a photo from the original source. The link to the full original reporting is always at the bottom.

How this content is made

Every story's summary and longer write-up is researched and drafted by AI, starting from the sourcing standards below, then run through an automated daily pipeline that scores, categorizes, and publishes it. No person individually reads, edits, or signs off on each story before it goes live. That's different from a newsroom where a reporter or editor reviews each article, and we want that distinction to be clear rather than implied.

This also means occasional errors are possible: a misstated number, a misread quote, a story that turns out to be less significant than its score suggested. If you spot one, we want to know and fix it — see the correction contact in the footer of every page.

Categories, and what they mean

• Products

  A new tool, model, or feature shipping or updating: a new GPT/Gemini/Claude/Llama version, a product launch, a UI feature, or a change to how an existing product is priced or accessed.

• Society

  Government and regulatory action, lawsuits, and the broader societal debate around AI that isn't itself a research publication, including infrastructure stories (chips, data centers) when the angle is their societal or environmental impact.

• Industry

  Funding rounds, valuations, mergers and acquisitions, IPOs, and other market or competitive moves among AI companies, including infrastructure and compute deals (chips, data centers, cloud capacity) themselves.

• Research

  Genuine published or preprint research: a paper on arXiv, a journal or conference publication, or a study from a lab or research group (MIT Media Lab, Stanford HAI, DeepMind, Nature, Anthropic's own research team, and similar). A company shipping a new model does not count as research on its own; there has to be an actual paper or study behind the story.

Sourcing standards

Official/primary sources come first: company newsrooms and research blogs, government and regulator sites, court filings. Otherwise, only major wire services and established outlets:

• Reuters
• Bloomberg
• AP
• Wall Street Journal
• Financial Times
• TechCrunch
• VentureBeat
• The Verge
• Ars Technica
• MIT Technology Review
• Nature
• Science
• IEEE Spectrum

Low-effort aggregator blogs and unverified social posts are never used as a story's source.

About the creator

EurekaRaven AI is built by Namig Abbasov, AI & Technology Initiatives Librarian at Stanford Law School, where his work centers on AI governance and discovery tools for legal and academic research: building RAG-based research platforms, establishing institutional AI auditing frameworks, and studying how AI is actually adopted and governed inside universities and law schools. His path ran through a Ph.D. in political science and an M.S. in data science before landing here, bringing that same computational rigor to a newer question: how institutions built on trust and precedent should govern the AI tools reshaping their own research.

More of his work at namigabbasov.com →