#sptodos
Live, measured metrics for the hashtag #sptodos from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
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Day-by-day usage
measured · fosstodon.org (Mastodon public tags API) · fetched 2026-07-28 16:18 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 2026-07-28 16:18 UTCNo related tags with measured usage found for #sptodos.
Live pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-07-28 16:18 UTCEverything below is measured over the latest 5 public posts (spanning ~336 hours).
Posting hours (UTC)
Languages: English (5)
Avg boosts / post: 0.8
Used together with
No co-used tags in the sample.
Top of the latest posts
A thought for me to return to: Sequential Monte Carlo methods might be conceptually more straightforward than Markov Chain Monte Carlo methods, on the grounds that large-N asymptotics can be easier to intuit than large-T asymptotics #sptodo
Something to develop a bit, eventually: the task of finding an optimal (stationary) dynamics for sampling in continuous time might be thematically more like an eigenvalue problem than a 'conventional' minimisation or even control problem, s
A related thought: try to describe an adaptive gradient method for (convex) minimisation by viewing the step-size as a control variable, and adapting it via PID control. #sptodos
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/sptodos