#swib23
Live, measured metrics for the hashtag #swib23 from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #swib23
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Day-by-day usage
measured · fosstodon.org (Mastodon public tags API) · fetched 2026-07-27 08:26 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 2026-07-27 08:26 UTCNo related tags with measured usage found for #swib23.
Live pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-07-27 08:26 UTCEverything below is measured over the latest 40 public posts (spanning ~17110 hours).
Posting hours (UTC) — busiest: 09:00
Languages: English (24) · German (15) · Dutch (1)
Avg boosts / post: 3.8
Top of the latest posts
@pietercolpaert @hvdsomp @nichtich At #swib23, Steven Folsom introduced it in the talk "Supporting sustainable lookup services", see https://swib.org/swib23/programme.html#supporting-sustainable-lookup-services for abstract, slides and reco
@hauschke @SemAntiKast Hmm, now I'm not sure what discussion you're referring to. We've mainly tried LLMs for metadata extraction, not classification as such. I gave a talk about it at #SWIB24 with Pierre Beauguitte from Norway (and also a
@osma At #SWIB23 we discussed with @SemAntiKast about some tests you did with LLM based classification, if I remember correctly. Do I remember correctly? If yes, did you document your tests, and did you try different approaches (reasoning m
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/swib23