#representations
Live, measured metrics for the hashtag #representations from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #representations
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.
Day-by-day usage
measured · fosstodon.org (Mastodon public tags API) · fetched 2026-07-27 06:37 UTC0 uses by 0 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 06:37 UTCLive pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-07-27 06:37 UTCEverything below is measured over the latest 40 public posts (spanning ~26133 hours).
Posting hours (UTC) — busiest: 09:00
Languages: English (28) · French (8) · German (4)
Avg boosts / post: 0.7
Top of the latest posts
Avant : « L’intelligence artificielle qui génère du contenu façonne les #représentations culturelles, façonne la #langue, façonne également l’#éthique. C’est-à-dire que les modèles qu’on fait, ce sont des modèles qui ont une certaine #polit
fly51fly (@fly51fly) 문맥 구조(context structure)가 언어모델의 표현 공간(representational geometry)을 재구성(reshapes)한다는 내용의 연구가 Google DeepMind 연구진에 의해 arXiv에 공개되었습니다. 문맥 정보의 구조적 특성이 모델 내부 표현과 거리/기하 구조에 미치는 영향을 분석하며, 표현학습과 프롬프트·컨텍스트 설계에 대한 이론적·실험적 통찰을 제공합니
fly51fly (@fly51fly) 2026년 Google DeepMind 연구진(A K Lampinen, Y Li, E Hosseini, S Bhardwaj 등)의 arXiv 논문은 대화 진행 중 언어모델의 선형 표현(linear representations)이 대화 맥락에 따라 극적으로 변화할 수 있음을 보여줍니다. 표현의 불안정성이 모델 해석, 디버깅, 지속적 문맥 처리에 주는 영향을 분석합니다. https://x.co
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/representations