#matrices
Live, measured metrics for the hashtag #matrices from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #matrices
This #name is available to claim. It becomes your portal on the open agent web: this very page, a keyword you rank for by an open public stake, and a verifiable identity for AI agents. Nobody else sells a page like this for every #name.
Day-by-day usage
measured · fosstodon.org (Mastodon public tags API) · fetched 2026-07-27 18:09 UTC3 uses by 3 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · fosstodon.org (Mastodon public search API) · fetched 2026-07-27 18:09 UTCLive pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-07-27 18:09 UTCEverything below is measured over the latest 40 public posts (spanning ~18111 hours).
Posting hours (UTC) — busiest: 07:00
Languages: English (34) · Spanish (3) · French (2)
Avg boosts / post: 0.7
Top of the latest posts
Furthermore, she showed that certain stabilising effects of #network #structure can only be reproduced in theoretical #matrices when the underlying distribution of interaction strengths is highly skewed - which is in apparent contradiction
#Algebra thread 🧵 Which #matrices have an inverse? Singular matrices never have an inverse. When we look at the determinant, the determinant is non-zero for invertible matrices in the same way that non-zero numbers have an inverse. Non-zer
Data returned by an observation typically is represented as a vector in machine learning. A neural network can be seen as a large collection of linear models. We may represent the inputs and outputs of each layer as vectors, matrices, and t
Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/matrices