#quantization

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

hashtag.org network · sponsored

Own #quantization

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.

$5.00/ year · 12-character #name
Claim #quantization — $5.00/yr→Buy on hashtag.space (web3)
card via hashtag.org · tokens via hashtag.space
7
Uses / 7 days
Mastodon
5
Accounts / 7 days
Mastodon
40
Recent posts
Mastodon
~0/hr
Recent pace
Mastodon · last 40
0
Avg reactions / post
Mastodon · last 40
—
Reddit posts / month
Reddit search
—
Open-web mentions
hashtag.org Firehose

Day-by-day usage

measured · mas.to (Mastodon public tags API) · fetched 2026-10-10 03:48 UTC
0
10-04
0
10-05
4
10-06
1
10-07
0
10-08
2
10-09
0
10-10

7 uses by 5 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.

Related hashtags

measured · mas.to (Mastodon public search API) · fetched 2026-10-10 03:48 UTC

Live pulse

measured · mas.to (Mastodon tag timeline) · fetched 2026-10-10 03:48 UTC

Everything below is measured over the latest 40 public posts (spanning ~1327 hours).

Posting hours (UTC) — busiest: 00:00

00:0012:0023:00

Languages: English (31) · German (4) · Japanese (1) · Chinese (1) · Russian (1)

Avg boosts / post: 0.3

Top of the latest posts

  • Part 4 of 4, How AI Actually Works: what does a language model really cost to run? I measured Qwen3-8B at 16, 8, 4 and 2 bits on an M4 Pro MacBook with llama.cpp: file size, perplexity, reading and writing speed, and the KV cache growing to

    Ready for the Side Quest@R4TSQ@mastodon.social♥ 0↻ 12026-10-09 22:09 UTCView post →
  • Underdog AI가 Qwen3.8-27B를 7.89GB 2비트 모델로 압축한 Saluki 27B를 Apache 2.0으로 공개했다. 표준 llama.cpp 및 이를 사용하는 앱에서 별도 런타임 없이 실행되며, 16GB RAM 노트북을 목표로 한다. 자체 도구 호출 벤치마크에서 120개 중 88개를 통과해 원본(84개)과 비교 모델을 앞섰지만, 작성자는 작은 표본과 실행 변동성 때문에 원본보다 지능적이라는 주장은 하지 않았다

    ainews@sayzard@mastodon.sayzard.org♥ 0↻ 12026-10-09 14:46 UTCView post →
  • Sudo su (@sudoingX) Cloudflare의 Jev 계열로 언급된 의사결정 모델이 RTX 5090 노트북에서 Q4 양자화된 Clef Flash 9B로 완전 로컬 실행되며 Breakout을 플레이하는 데모다. 매 틱마다 공·방향·패들 위치를 한 줄 숫자 입력으로 받고 좌/우 동작을 출력해 공을 계속 받아낸다고 설명했다. 소형 로컬 모델의 실시간 환경 제어 가능성을 보여준다. https://x.com/sudoingX/

    ainews@sayzard@mastodon.sayzard.org♥ 0↻ 22026-10-07 05:53 UTCView post →

#quantization across platforms

every network with a public tag surface

Follow #quantization 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/quantization