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Mindshare · Methodology

How Mindshare Is Measured · Methodology

Every number on the Mindshare pages is a count from a public source, collected once a day. Nothing is estimated or hand-entered.

A watchlist of 96 entities in five lists: model families, coding agents, companies, people and topics. Choosing what to track is an editorial decision; each entry has a fixed search phrase, and, where relevant, a Wikipedia article and a GitHub repository.

  • Hacker News mentions · stories and comments created during the UTC day that contain the exact search phrase (Hacker News Algolia API, exact-phrase matching, no typo tolerance).
  • Wikipedia pageviews · English Wikipedia user pageviews for the entity's article (Wikimedia REST API). Companies and people only.
  • GitHub stars · stargazer count of the agent's repository, recorded daily (GitHub API). Shown alongside agents; not blended into share.
  • Share · an entity's signal over the trailing 7 days divided by the total for its list. Companies and people blend 60% Hacker News share with 40% Wikipedia share. Agents with a public repository blend 50% Hacker News share with 50% share of new GitHub stars over the same 7 days (open-source agents spread through GitHub more than Hacker News); closed agents keep their Hacker News share, and the list is rescaled to 100%. Star growth applies once 7 days of star counts exist for at least 3 agents (tracked since 2026-10-02). Other lists use Hacker News only.
  • Index · share relative to the list leader (leader = 100).
  • 7d / 30d change · difference in share versus 7 or 30 days earlier, in basis points (1 bps = 0.01 percentage points).
  • Mindshare Pulse · total tracked Hacker News mentions over the last 7 days compared with the 90-day weekly average. 50 = usual level, 100 = double.
  • Models · Hacker News
  • Agents · Hacker News, GitHub stars (shown, not blended)
  • Companies · Hacker News (60%), Wikipedia pageviews (40%)
  • People · Hacker News (60%), Wikipedia pageviews (40%)
  • Topics · Hacker News

Hacker News skews toward developers and English-language tech audiences, so these lists measure developer attention, not overall public attention. Common words can over-count (for example, a product named after an everyday word); we pick unambiguous phrases and review them when lists change. Sentiment is not measured.