Sources and method

• Hacker NewsFront-page stories via the Algolia API, weighted by points plus half the comments.weight 1 · up to 300 a day · 3,171 items
• RedditTop posts of the day in r/MachineLearning, r/LocalLLaMA, r/singularity, r/OpenAI, r/ClaudeAI, r/ChatGPT, r/artificial, r/dataengineering, r/programming and r/technology (public RSS, one feed every few seconds, one slower retry on a 429), weighted by rank.weight 0.8 · up to 250 a day · 415 items
• YouTubeNew videos from about 35 tech and AI channels (Fireship, Two Minute Papers, AI Explained, Matthew Berman, Theo, The PrimeTime, Anthropic, OpenAI, Google DeepMind, MKBHD and others) via their public RSS feeds, weighted by views. Titles only.weight 0.8 · up to 80 a day · 228 items
• GitHubTrending repositories, weighted by stars gained today; for backfilled days, the most-starred repositories created that day.weight 0.8 · up to 40 a day · 481 items
• Wikipedia pageviewsThe 400 most-read English Wikipedia articles of the day (Wikimedia pageviews API), weighted by views; only articles that name a known tech term count.weight 0.7 · up to 400 a day · 6,000 items
• Hugging Face modelsTrending models and Spaces, weighted by trending score; the organisation names the model family.weight 0.7 · up to 45 a day · 92 items
• Google NewsGoogle News RSS search with generic queries (artificial intelligence, AI model, chatbot, AI chip, open source, cloud, cybersecurity and a few more; never a company name, so no company is counted by construction), stories published that day; each story counts once.weight 0.6 · up to 200 a day · 13,642 items
• BlueskyThe public "What's Hot" feed and trending topics (public AppView), weighted by likes and reposts. Search needs a login, so it is not used.weight 0.5 · up to 120 a day · 120 items
• Google TrendsUS daily search trends (RSS), only entries that name a tech or AI term, weighted by approximate traffic.weight 0.6 · up to 30 a day · 30 items
• Product HuntLaunches in the Product Hunt feed, weighted by position.weight 0.5 · up to 50 a day · 102 items
• MastodonPublic tag timelines on mastodon.social (#ai, #llm, #machinelearning, #opensource, #programming and others), weighted by boosts and favourites.weight 0.4 · up to 200 a day · 200 items
• Hugging Face papersDaily papers, weighted by upvotes.weight 0.4 · up to 100 a day · 676 items
• fru.dev sitesNew releases on Releases and new rounds on Funding, each counted once.weight 0.4 · up to 50 a day · 52 items
• arXivNew submissions in cs.AI, cs.LG, cs.CL and cs.DB (RSS listings); each title counts once.weight 0.3 · up to 600 a day · 1,226 items
• Google News (Serper)A news count for the top few terms each day, as a cross-check; it does not change the score.check only

• X (Twitter)No free API for reading or search, and scraping is against its terms. Posts on X reach the score only when news stories, Reddit or Hacker News link to them.
• LinkedInNo public API for posts or trends, and scraping is against its terms.
• Bluesky searchThe public API now refuses unauthenticated search; only the public What's Hot feed and trending topics are read.
• YouTube searchThe Data API needs a Google Cloud key; the fixed channel list is read through free RSS instead, so smaller channels are missed.
• TikTok, Instagram, DiscordNo free public API.

1. Collect. At 05:45 UTC the run reads every source for the UTC day that just ended: titles, links and engagement only, never page text.
2. Find terms. Each title is matched against a curated list of about 230 names (models, tools, databases, chips, companies, people). Model versions are read from the text in any spelling, so "Claude Opus 5.5", "Opus 5.5", "claude opus 5.5" and "claude-opus-5-5" are one term, and a new version is a new term the day it appears. New capitalised names become terms when three items from two sources, or four items, name them on one day.
3. Tidy new names. At most one batched Gemini call a day sees only the day's new names with two example titles each, and merges spellings, fixes the written name, picks a category or drops what is not a tech term. Its decisions are stored and reused; without it, new names are kept by count alone and marked Unverified.
4. Score. For each source, a term's share of the day: 0.7 x its share of engagement (square-rooted, so one viral post cannot own a source) + 0.3 x its share of items. Shares are weighted by source (above) and averaged over the sources that answered. Lift compares that with the term's own mean over the previous 14 days, capped at 6. Trend score = 100 x sqrt(share) x lift^0.35 x (1 + 0.1 for each extra source), capped at 100.
5. Rank. The top 60 are stored for baselines; the top 20 are shown. Risers compare the summed scores of the last seven days with the seven before.

The first 30 days were backfilled from the sources that keep history: Hacker News (Algolia search by date), Hugging Face daily papers, and GitHub (the most-starred repositories created each day, which count for their names but never become terms themselves). On September 24, 2026 YouTube, Wikipedia pageviews, Google News and more Reddit feeds were added and the recent days recomputed with them. Bluesky, Mastodon, Product Hunt, Google Trends, arXiv listings and trending models only have today, so they count from their first live day on.