#agentoptimization

Live, measured metrics for the hashtag #agentoptimization 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 #agentoptimization

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 · 17-character #name
Claim #agentoptimization — $5.00/yr→Buy on hashtag.space (web3)
card via hashtag.org · tokens via hashtag.space
0
Uses / 7 days
Mastodon
0
Accounts / 7 days
Mastodon
10
Recent posts
Mastodon
~0/hr
Recent pace
Mastodon · last 10
0
Avg reactions / post
Mastodon · last 10
—
Reddit posts / month
Reddit search
—
Open-web mentions
hashtag.org Firehose

Day-by-day usage

measured · mastodon.online (Mastodon public tags API) · fetched 2026-11-03 02:00 UTC
0
10-28
0
10-29
0
10-30
0
10-31
0
11-01
0
11-02
0
11-03

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 2026-11-03 02:00 UTC

No related tags with measured usage found for #agentoptimization.

Live pulse

measured · mastodon.online (Mastodon tag timeline) · fetched 2026-11-03 02:00 UTC

Everything below is measured over the latest 10 public posts (spanning ~2956 hours).

Posting hours (UTC)

00:0012:0023:00

Languages: English (10)

Avg boosts / post: 0.1

Top of the latest posts

  • Thomas Wight (@thomaswight) Google이 Harness²를 오픈소스로 공개했다는 소식. 동일 모델을 유지한 채 에이전트가 프롬프트·메모리·스킬 등 자신의 설정을 수정하고, 단일 작업에서 여러 버전을 시험해 성능이 좋았던 구성을 채택하는 방식이다. 트윗에 따르면 한 번의 반복으로 법률·오피스 테스트에서 수작업 구성보다 높은 성능을 보였다. https://x.com/thomaswight/status/2112

    ainews@[email protected]♥ 0↻ 02026-10-20 01:54 UTCView post →

  • fly51fly (@fly51fly) Microsoft Research와 MIT 연구진이 적대적 메타프롬프트 진화를 통해 보상 함수를 반복적으로 개선하는 ‘Recursive Reward Improvement’ 연구를 공개했습니다. LLM의 자기개선·에이전트 최적화에서 사람의 직접 라벨링 의존도를 줄이고, 보상 모델/프롬프트를 자동 탐색·개선하려는 접근으로 볼 수 있습니다. https://x.com/fly51fly/status/

    ainews@[email protected]♥ 0↻ 02026-10-17 18:54 UTCView post →

  • Shubhankar (@_shubhankar) 프로덕션 환경에서 웹사이트를 오가는 반(半)반복 업무용 컴퓨터 사용 에이전트를 운영한다면, 워크플로 성능을 반복적으로 개선하는 hill-climbing 접근을 적용해야 한다는 실무 제안이다. 개인 에이전트처럼 반복 작업이 많은 환경에서 특히 유효하다는 점을 강조했다. https://x.com/_shubhankar/status/2109725380596912605 #computerus

    ainews@[email protected]♥ 0↻ 02026-10-13 06:54 UTCView post →

#agentoptimization across platforms

every network with a public tag surface

Follow #agentoptimization 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/agentoptimization