#Vectorization

Live, measured metrics for the hashtag #Vectorization from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.

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$5.00/ year · 13-character #name
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0
Uses / 7 days
Mastodon
0
Accounts / 7 days
Mastodon
40
Recent posts
Mastodon
~0/hr
Recent pace
Mastodon · last 40
0.4
Avg reactions / post
Mastodon · last 40
—
Reddit posts / month
Reddit search
—
Open-web mentions
hashtag.org Firehose

Day-by-day usage

measured · mastodon.online (Mastodon public tags API) · fetched 2026-10-14 10:36 UTC
0
10-08
0
10-09
0
10-10
0
10-11
0
10-12
0
10-13
0
10-14

0 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 2026-10-14 10:36 UTC

No related tags with measured usage found for #vectorization.

Live pulse

measured · mastodon.online (Mastodon tag timeline) · fetched 2026-10-14 10:36 UTC

Everything below is measured over the latest 40 public posts (spanning ~19384 hours).

Posting hours (UTC) — busiest: 16:00

00:0012:0023:00

Languages: English (33) · Russian (4) · French (3)

Avg boosts / post: 0.2

Top of the latest posts

  • I'm working on a Rust library for explicitly *non-portable* SIMD intrinsics, called `npsimd` <https://docs.rs/npsimd>. The idea is that high-performance vectorized code needs to take advantage of platform-specific functionality, and we need

    Arav K.@bal4e♥ 11↻ 42024-07-17 06:30 UTCView post →

  • Version 0.2.0 of `npsimd` is now published, with a new low-level API that supports runtime feature detection (currently only SSE2 is implemented). I'm going to slowly migrate all the existing functionality over to it, and then work on a bet

    Arav K.@bal4e♥ 2↻ 12024-07-17 16:46 UTCView post →

  • vectorized prefix ~sum~ function composition send help #vectorization #simd

    Arav K.@bal4e♥ 1↻ 02024-08-10 07:07 UTCView post →

#vectorization across platforms

every network with a public tag surface

Follow #vectorization 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.

X (Twitter) ↗Instagram ↗TikTok ↗Facebook ↗LinkedIn ↗YouTube ↗Threads ↗Bluesky ↗Mastodon ↗Reddit ↗Pinterest ↗Tumblr ↗

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/vectorization