#recommendations
Live, measured metrics for the hashtag #recommendations from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #recommendations
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 2026-09-09 13:59 UTC7 uses by 7 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 2026-09-09 13:59 UTCLive pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-09-09 13:59 UTCEverything below is measured over the latest 40 public posts (spanning ~723 hours).
Posting hours (UTC) — busiest: 19:00
Languages: English (38) · German (1)
Avg boosts / post: 2.5
Top of the latest posts
I've been a full-time #linux user for about 18 months now. First #ubuntu, then #mint, now #fedora (yes, it is very likely mastodon's fault). Very happy with it all. And I have learnt plenty. Seeking #recommendations for (e)books or courses
UPDATE: I'm still very open to recommendations, but I am giving clip.space a try. Looking through the lists of peertube instances I get a somewhat grim picture: It doesn't seem to be a widely used platform, but in a specific way. There are
Hey Fedi, any #recommendations for #Email and #CloudStorage as a replacement/exit strategy for Google mail and drive? I'm looking at mailbox.org at the moment, looks pretty good and seems to include a docs replacement too. But anything I sh
What “recommendations” means
Wiktionary · WikipediaA recommender system, also called a recommendation algorithm, recommendation engine, recommendation platform, or in the context of social media, simply algorithm is a type of information filtering system that suggests items most relevant to a particular user. The value of these systems becomes particularly evident in scenarios where users must select from a large number of options, such as products, media, or content. Major social media platforms, streaming services and e-commerce websites rely
“Recommender system” on Wikipedia (CC BY-SA) →#recommendations across platforms
every network with a public tag surfaceFollow #recommendations 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/recommendations