AI Digest is a daily AI industry news briefing — closer to a wire service than a magazine. Every piece of content is algorithmically curated, AI-written, and human-supervised. It's a personal project by Nate Lee.
Why this exists
If you follow AI, you already know the problem: there's too much to keep up with.
Every day brings a flood of announcements, papers, product launches, and hot takes spread across dozens of newsletters, blogs, and Hacker News discussions. Keeping up means spending an hour or more just triaging what's worth reading — before you even start reading it.
Most AI coverage adds to the noise rather than cutting through it. Headlines are optimized for clicks, not clarity. Hype cycles inflate minor updates into paradigm shifts. And when publications have financial ties to the companies they cover, it's hard to tell which stories reflect genuine importance versus commercial interest.
AI Digest takes a different approach: pull from 20+ primary sources, score every item algorithmically, and let AI write the final digest — with zero manual content intervention. Published daily by 9:00 AM UTC, each edition contains 3 feature articles and a handful of short news items. Total reading time: about 5 minutes.
It won't be deeper than a well-researched human analysis. But it will be more systematic, more transparent, and free from anyone's agenda.
Who this is for
This might be useful to you if:
- You value factual reporting over opinion and commentary
- You want to know "what happened" before "what it means"
- You prefer traceable sourcing — every story links back to the original
- You're tired of the AI hype cycle distorting what actually matters
If you're looking for deep analysis, hot takes, or strong editorial voices — this isn't the right place. AI Digest gives you the facts, organized and compressed, so you can form your own conclusions.
Sources
The current monitoring list includes:
Company blogs: OpenAI, Anthropic, Google AI, DeepMind, Meta Engineering, NVIDIA
Tech media: MIT Technology Review, The Verge, TechCrunch, Ars Technica, Wired, 404 Media
Community & research: Hacker News (AI-related discussions), HuggingFace daily trending papers
Industry newsletters: Import AI (Jack Clark), Ben's Bites, Latent Space, Interconnects (Nathan Lambert), AI Snake Oil (Arvind Narayanan), One Useful Thing (Ethan Mollick)
Independent blogs: Simon Willison's Weblog
Over 20 sources in total, covering the full spectrum from cutting-edge research to product launches to industry analysis. Each source has its own fetch frequency and filtering rules, configured to match its characteristics.
Coverage spans: model releases, AI agents, safety & alignment, industry moves, developer tools, research frontiers, product applications, open-source ecosystem, policy & regulation, and workforce impact. The editorial doesn't try to cover everything — it picks the 3 stories most worth telling each day, preferring to skip rather than pad.
How selection works
Raw fetches produce far more items than what makes it into the final digest. Selection happens in two stages: algorithmic scoring and AI editorial.
Algorithmic scoring
Every incoming item is automatically scored across several dimensions:
- Cross-source corroboration: When multiple independent sources cover the same story, it gets a score boost. This is the strongest importance signal — if only one source mentions it, it might only be important to that source
- Community engagement: Upvotes and comment counts on Hacker News, vote counts on HuggingFace papers. Community consensus is another form of validation
- Source authority: First-party company blogs (e.g., OpenAI announcing its own product) and top-tier tech outlets carry more weight than aggregator newsletters
- Recency: Items from the last 24 hours get a boost, preventing stale stories from taking up space
Items scoring above a threshold enter the "highlight" pool; the rest go into the "notable" pool. All items are then considered in the AI editorial stage.
AI editorial
Algorithmic scoring answers "what's important." AI editorial answers "what stories do we tell today."
AI selects 3 stories worth covering in depth from the day's candidates. A few principles guide the selection:
- Stories span different domains, and narrative approaches are deliberately varied to keep each edition fresh
- Each story draws on multiple related sources rather than echoing a single report
- Recent editions are checked to avoid repeating what's already been covered
Items that don't make the feature stories but are still worth knowing about appear as short news briefs.
On neutrality
This is the project's core principle: humans tune the algorithm, never touch the content.
Specifically:
- Source weighting, selection rules, editorial instructions — these are set by a human
- What topics to cover each day, how to write them, what headlines to use — these are determined by AI following the rules
- I monitor daily output quality and adjust rules and algorithms when I spot problems, but I never directly edit an article's content or override a topic selection
The goal is to create a credible separation layer: what you read is not shaped by anyone's personal preferences, moods, or interests. If something is off on a given day, it's a problem with the rules or the algorithm — and the fix is to improve the rules, not to rewrite the content.
On commercial interests: This site accepts no sponsored content, advertorials, or paid placements of any kind. All topic selection and ranking is fully algorithmic — no item gets visibility because of a business relationship. If advertising is ever introduced, it will be clearly labeled as such and never disguised as editorial content.
Known limitations
Being upfront about where this project falls short:
Inherent limits of AI generation: Articles are written by AI, which can misinterpret source material. Every article includes source links for verification, but AI is not a journalist — it doesn't make phone calls, follow up on leads, or press for details.
Scoring bias: The current scoring system favors community engagement and institutional authority. This means breakthrough work from small teams, or important but niche research directions, may be systematically underweighted.
No follow-up tracking: Each story reflects the information available at the time of publication. If a story is later corrected or reversed, it won't be tracked unless the correction itself becomes a new news event.
Solo project: This is a one-person project, not a newsroom. There's no fact-checking team and no multi-person editorial review. What I can do is design good rules, monitor the results, and fix problems quickly.
FAQ
Is AI biased? Can AI-written content be trusted?
Yes, AI is biased. Models carry biases from their training data and design choices — there's no such thing as a truly unbiased AI. What I can do is: use editorial instructions that are as neutral as possible, require the AI to report facts rather than offer opinions, and include source links at the bottom of every article. You can always click through to verify against the original. This site's role is to help you discover what's worth paying attention to, not to draw conclusions for you.
What AI model do you use?
Currently Claude by Anthropic, chosen after testing multiple models for writing quality. The model is used for both editorial selection and article writing. I treat model quality as a non-negotiable part of this project: as stronger models emerge, the system upgrades to them. Output quality is the priority; compute cost is not a factor in model selection.
How is this different from asking ChatGPT to summarize AI news?
When you ask an AI to "summarize today's AI news," it can only work with its training data or a quick web search. The value of this site is upstream: carefully selected and configured sources, a multi-dimensional scoring algorithm, deduplication and anti-repetition logic, and iteratively refined writing standards. The 5-minute read you see is the output of a system that runs and improves continuously. That's not something a single prompt can replicate.
Why not just read the sources directly?
If you have time to check Hacker News, TechCrunch, The Verge, half a dozen company blogs, and several newsletters every day — you probably don't need this site. AI Digest solves the "I know the information is out there, but I don't have time to sift through it myself" problem. 20+ sources, cross-validated, algorithmically ranked, compressed into 3 articles and a few short items, readable in 5 minutes.
What if I spot an error?
Feedback is welcome. If it's a factual error, I'll investigate whether the issue came from the source data or from AI misinterpretation, then fix the corresponding rules. If it's a selection problem (an important story was missed), I'll check whether the scoring rules need adjustment. The fix is always to improve the rules, never to patch the content.