#mlx

Live, measured metrics for the hashtag #mlx 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 #mlx

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.

$5,313.74/ year · 3-character #name
Claim #mlx$5,313.74/yr
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card via hashtag.org · tokens via hashtag.space
6
Uses / 7 days
Mastodon
6
Accounts / 7 days
Mastodon
40
Recent posts
Mastodon
~0/hr
Recent pace
Mastodon · last 40
0
Avg reactions / post
Mastodon · last 40

Day-by-day usage

measured · fosstodon.org (Mastodon public tags API) · fetched 2026-07-27 06:52 UTC
2
07-21
1
07-22
2
07-23
0
07-24
0
07-25
1
07-26
0
07-27

6 uses by 6 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 2026-07-27 06:52 UTC

Live pulse

measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-07-27 06:52 UTC

Everything below is measured over the latest 40 public posts (spanning ~1117 hours).

Posting hours (UTC) — busiest: 21:00

00:0012:0023:00

Languages: English (33) · Russian (7)

Avg boosts / post: 0.2

Top of the latest posts

  • Ivan Fioravanti ᯅ (@ivanfioravanti) MLX 기반 비전언어모델 프레임워크인 mlx-vlm에 Mage Flow가 추가됐다. Apple Silicon 환경에서 MLX/VLM 워크플로를 사용하는 개발자에게 관련 기능 확장 소식이다. https://x.com/ivanfioravanti/status/2080965873397838276 #mlx #vlm #applesilicon #multimodal #opens

    ainews@[email protected]002026-07-26 05:48 UTCView post →
  • 56 минут созвона → текст за 5 минут на M4 без OBS и облака У меня от трёх до пяти созвонов в день, почти все в браузере: Meet, Яндекс Телемост, ktalk, реже всего Zoom. Детали встреч мне нужны в тексте, иначе договорённости расползаются. Пис

    Habr@[email protected]012026-07-23 06:02 UTCView post →
  • 13scoobie (@13scoobie) M5 Max 128GB 환경에서 MLX/MLX-LM 서빙 성능을 공유했다. S 2.1은 디코드 44 tok/s·프리필 약 1.5k, SX 2.1은 디코드 93 tok/s·프리필 약 4.3k를 기록했다. Apple Silicon 기반 로컬 LLM 서빙 시 모델 변형별 처리량 차이를 가늠할 수 있는 참고 수치다. https://x.com/13scoobie/status/207995153284

    ainews@[email protected]012026-07-23 03:56 UTCView post →

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/mlx