#lapack
Live, measured metrics for the hashtag #lapack from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #lapack
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 2026-10-09 09:57 UTC0 uses by 0 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 2026-10-09 09:57 UTCNo related tags with measured usage found for #lapack.
Live pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-10-09 09:57 UTCEverything below is measured over the latest 33 public posts (spanning ~57149 hours).
Posting hours (UTC) — busiest: 15:00
Languages: English (24) · Polish (3) · Russian (3) · German (2)
Avg boosts / post: 1
Top of the latest posts
Good news especially for Debian scientific computing users: BLAS/LAPACK Ecosys massive update https://lists.debian.org/debian-science/2019/10/msg00038.html #LAPACK #BLAS #LinearAlgebra
The plot thickens #BLAS #rstats #lapack (When one is about to rip through 10s of millions of medical records, one must profile the tools if the project is to finish before one's retirement) FlexiBLAS makes this benchmarks a breeze
The plot thickens #BLAS #rstats #lapack (When one is about to rip through 10s of millions of medical records, one must profile the tools if the project is to finish before one's retirement) FlexiBLAS makes this benchmarks a breeze
What “lapack” means
WikipediaLAPACK is a standard software library for numerical linear algebra. It provides routines for solving systems of linear equations and linear least squares, eigenvalue problems, and singular value decomposition. It also includes routines to implement the associated matrix factorizations such as LU, QR, Cholesky and Schur decomposition. The routines handle both real and complex matrices in both single and double precision. LAPACK relies on an underlying BLAS implementation to provide efficient and
“LAPACK” on Wikipedia (CC BY-SA) →#lapack across platforms
every network with a public tag surfaceFollow #lapack straight to each platform’s own tag page. Where a platform publishes open data we measure it above; the rest lock their numbers behind paid APIs, so we link rather than guess.
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/lapack