Trending in data, tech and AI

Terms210Days72

210 terms · 72 days

Sources and method

Where the signals come from (188,007 items read so far) and how a day's ranking is made.

Sources

  • Hacker NewsFront-page stories via the Algolia API, weighted by points plus half the comments.weight 1 · up to 300 a day · 6,184 items
  • RedditTop posts of the day in a seed list of AI, data and programming subreddits (r/MachineLearning, r/LocalLLaMA, r/ClaudeAI, r/dataengineering, r/programming and more) plus subreddits found since and checked, a few a day in turn (public RSS, one feed every few seconds), weighted by rank.weight 0.8 · up to 250 a day · 5,771 items
  • YouTubeNew videos from tech and AI channels via their public RSS feeds, weighted by views: a seed list (Fireship, Two Minute Papers, AI Explained, Theo, Google DeepMind and others) plus the official channels of companies on companies.fru.dev, found through socials.fru.dev and checked before they count. The list grows. Titles only.weight 0.8 · up to 80 a day · 2,753 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 · 919 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 · 19,199 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 · 2,043 items
  • Google NewsGoogle News RSS: generic queries (artificial intelligence, AI model, chatbot, AI chip, open source, cloud, cybersecurity and a few more), then the same by-name query for each of the day's 40 leading terms (up to 25 stories each), stories published that day; each story counts once.weight 0.6 · up to 1400 a day · 99,442 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 · 6,928 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 · 1,247 items
  • Product HuntLaunches in the Product Hunt feed, weighted by position.weight 0.5 · up to 50 a day · 2,495 items
  • MastodonPublic tag timelines on mastodon.social (#ai, #llm, #machinelearning, #opensource, #programming and more, plus tech tags found trending or used alongside them), weighted by boosts and favourites.weight 0.4 · up to 200 a day · 10,966 items
  • Hugging Face papersDaily papers, weighted by upvotes.weight 0.4 · up to 100 a day · 2,207 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 · 24,490 items
  • Blogs and tech news feedsPosts published that day on company, engineering and research blogs and tech news feeds (Techmeme, Lobsters and others; their own RSS or Atom feeds), found through blogs.fru.dev, socials.fru.dev and research and checked before they count. The list grows. Each post counts once.weight 0.3 · up to 150 a day · 3,363 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

Feeds, and how the list grows

599 feeds are read so far, 81 added in the last 30 days, and 367 found ones wait for a check. Each daily run looks for new ones: the official YouTube, Reddit and RSS channels of companies on companies.fru.dev (through socials.fru.dev), company and research blogs (through blogs.fru.dev), Mastodon tags trending or used alongside the ones already read, and subreddits and channels that the day's posts link to. A found feed is read only after a check gets recent items from it (and, unless it is a company's own channel, when enough of them are about tech); three failed checks drop it. Official channels of 322 of the 559 companies found with one are read (58%). Every change is kept.

  • Sites and APIsFrom starting list9 read
  • YouTube channelsFrom research, starting list, socials.fru.dev233 read · 252 to check
  • SubredditsFrom research, starting list, socials.fru.dev13 read · 92 to check
  • Mastodon tagsFrom linked from items, Mastodon trends, research, starting list70 read · 2 to check
  • BlogsFrom research, blogs.fru.dev, socials.fru.dev270 read · 21 to check
  • arXiv listsFrom starting list4 read

Not covered

  • 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; a growing list of channels is read through free RSS instead, so channels nobody has found yet are missed.
  • TikTok, Instagram, DiscordNo free public API.

How a day is ranked

  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, in English only, and at most two posts from any one Mastodon or Bluesky account a day. Through the day the same run ranks today so far; a read that finds less than half of the last accepted read is refused and the day keeps its ranking.
  2. Find terms. Each title is matched against a curated list of known 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. A capitalised word the same day also writes in lower case ("center", "social") is a common word, not a name, and never counts.
  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.

Movements and stages

A movement is a technology wave (RAG, vibe coding, MCP, reasoning models) rather than one product. Each has a hand-checked origin (the paper, post or product that named it) and a list of names it goes by. Once a month its curve is measured back to 2007: Hacker News stories whose title uses one of its names (Algolia search), Wikipedia pageviews of its article and the pages that redirect to it (from July 2015), and arXiv papers that use its names in the title or abstract (counted in DataCite, where arXiv registers every paper with its abstract, because the arXiv API refuses this server). Names that are also ordinary words or older acronyms count only from the month they took the new meaning.

Index. Each source is divided by its own highest month, and the sources with data that month are averaged, so the index runs from 0 to 100. Emerged is the first month whose three-month average of Hacker News stories and arXiv papers reaches a tenth of its high (and at least three), or the origin month if that is earlier; the curve starts there. Buzzing now uses Trending's own daily items: the share that name a movement (or one of its products) over the last 14 days against the 14 before.

The stage comes from the three-month average of the index, checked in this order:

  • EmergingLess than 12 months since the first dated evidence.
  • RisingThe last three months are at least 20% above the three before.
  • PeakNot rising, and the last three months are within 80% of the highest three-month stretch.
  • DecliningNot rising, at or below half of its high, and not growing.
  • PlateauEverything else: below its high, neither growing nor falling away.

History

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.