#MLInfrastructure

Live, measured metrics for the hashtag #MLInfrastructure 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 #mlinfrastructure

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 · 16-character #name
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card via hashtag.org · tokens via hashtag.space
3
Uses / 7 days
Mastodon
2
Accounts / 7 days
Mastodon
8
Recent posts
Mastodon
~0/hr
Recent pace
Mastodon · last 8
0
Avg reactions / post
Mastodon · last 8

Day-by-day usage

measured · fosstodon.org (Mastodon public tags API) · fetched 2026-07-28 12:09 UTC
0
07-22
0
07-23
2
07-24
0
07-25
0
07-26
0
07-27
1
07-28

3 uses by 2 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 12:09 UTC

No related tags with measured usage found for #mlinfrastructure.

Live pulse

measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-07-28 12:09 UTC

Everything below is measured over the latest 8 public posts (spanning ~1456 hours).

Posting hours (UTC)

00:0012:0023:00

Languages: English (8)

Avg boosts / post: 0.3

Top of the latest posts

  • Akshay (@akshay_pachaar) AI 모델 배포 위치를 서버리스, 온프레미스, 엣지로 구분하고 각 방식의 비용·운영상 트레이드오프를 설명하는 스레드입니다. 모델 실행 환경 선택은 지연시간, 인프라 제어권, 확장성, 비용 구조에 직접 영향을 주므로 배포 전 워크로드 요구사항을 검토해야 한다는 내용입니다. https://x.com/akshay_pachaar/status/2082003469175763423 #modeld

    ainews@[email protected]♥ 0↻ 02026-07-28 08:53 UTCView post →

  • techAU (@techAU) 프런티어급 모델의 학습 실행이 2주 만에 끝나는 수준으로 짧아졌다는 관찰입니다. 대규모 학습 인프라·병렬화·데이터 파이프라인 효율이 개선되면서 모델 업데이트와 실험 주기가 빨라질 수 있음을 시사합니다. https://x.com/techAU/status/2080757860674519239 #llm #modeltraining #mlinfrastructure #frontiermodels

    ainews@[email protected]♥ 0↻ 12026-07-24 22:55 UTCView post →

  • Jaana Dogan ヤナ ドガン (@rakyll) AI 시스템 경쟁에서 폐쇄형 생태계만으로는 승리하기 어렵고, 향후 10년의 핵심 과제는 모델을 넘어선 개방형 AI 스택 구축이라는 견해입니다. 모델 자체 외에도 데이터, 도구, 에이전트, 배포·운영 등 스택 전반에 미해결 공백이 있어 개발자·인프라 기업의 기회가 크다는 주장입니다. https://x.com/rakyll/status/2080715189910147266 #open

    ainews@[email protected]♥ 0↻ 02026-07-24 19:54 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/mlinfrastructure