#appliedml
Live, measured metrics for the hashtag #appliedml from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #appliedml
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 · mastodon.online (Mastodon public tags API) · fetched 2026-07-27 14:20 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · mastodon.online (Mastodon public search API) · fetched 2026-07-27 14:20 UTCLive pulse
measured · mastodon.online (Mastodon tag timeline) · fetched 2026-07-27 14:20 UTCEverything below is measured over the latest 10 public posts (spanning ~26624 hours).
Posting hours (UTC)
Languages: English (10)
Avg boosts / post: 1
Top of the latest posts
Anyway, I keep meaning to write up a blog post on “falsehoods I have believed about measuring model performance” touching on #AppliedML issues related to #modelEvaluation, #metrics, #monitoring, #observability, and #experiments (#RCTs). The
You have a problem: you currently pick thresholds for model-based actions using some arbitrary heuristic. Your solution: pick the threshold that maximizes expected utility (e.g. revenue, profit, ROI, …) instead. That’s the definition of the
Swiss AI Days 2026 Peer-Reviewed Papers Track Submissions are Open! And as the publication chair this year, I am really excited about it. https://ai-days.swiss-ai-center.ch/en/call-for-contributions/papers 1/4 #AI #ML #AppliedML #Conference
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/appliedml