#embeddings

Live, measured metrics for the hashtag #embeddings 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 #embeddings

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.

$23.52/ year · 10-character #name
Claim #embeddings — $23.52/yr→Buy on hashtag.space (web3)
card via hashtag.org · tokens via hashtag.space
8
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
—
Reddit posts / month
Reddit search
—
Open-web mentions
hashtag.org Firehose

Day-by-day usage

measured · mastodon.online (Mastodon public tags API) · fetched 2026-11-02 14:54 UTC
2
10-27
2
10-28
1
10-29
3
10-30
0
10-31
0
11-01
0
11-02

8 uses by 6 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.

Related hashtags

measured · mastodon.social (Mastodon public search API) · fetched 2026-11-02 15:13 UTC

No related tags with measured usage found for #embeddings.

Live pulse

measured · mastodon.online (Mastodon tag timeline) · fetched 2026-11-02 14:54 UTC

Everything below is measured over the latest 40 public posts (spanning ~1328 hours).

Posting hours (UTC) — busiest: 08:00

00:0012:0023:00

Languages: English (28) · Russian (11) · German (1)

Avg boosts / post: 0.1

Top of the latest posts

  • Sentence Transformers가 mergekit을 이용해 호환되는 모델의 가중치를 추가 학습 없이 병합하는 API를 제공한다. 선형 가중 평균, SLERP, TIES 등을 지원하며 Dense 임베딩 외에 Cross Encoder, Sparse Encoder, Multi-Vector Encoder에도 적용할 수 있다. 아키텍처와 토크나이저 등이 일치해야 하며 공통 기반 모델에서 파인튜닝한 체크포인트를 권장한다. 병합이

    ainews@[email protected]♥ 0↻ 02026-10-30 13:47 UTCView post →
  • 🚀 Vector Databases Explained: The Complete Guide to Embeddings, Indexing, and RAG Vector databases power semantic search, recommendations, and RAG. This guide walks through the nine building blocks — embeddings, similarity search, metadata

    zyvop@[email protected]♥ 0↻ 02026-10-30 11:00 UTCView post →
  • Шум в эмбеддингах не защищает данные: проверил 8 схем, и при 100 известных парах узнаётся ~90% документов Коротко. Если хранить эмбеддинги с добавленным шумом, текст по ним восстановить трудно, но это не значит, что данные защищены. Я измер

    Habr@[email protected]♥ 0↻ 02026-10-30 08:22 UTCView post →

#embeddings across platforms

every network with a public tag surface

Follow #embeddings 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/embeddings