#Splade
Live, measured metrics for the hashtag #Splade from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #splade
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 2026-09-13 14:03 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 2026-09-13 14:03 UTCLive pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-09-13 14:03 UTCEverything below is measured over the latest 5 public posts (spanning ~26151 hours).
Posting hours (UTC)
Languages: English (3) · Japanese (2)
Avg boosts / post: 0.8
Top of the latest posts
"The State of Information Retrieval in 2026" This is the best survey article I have seen in a long time in this niche. The dominant retriever in 2026 is an 8-billion-parameter decoder-only language model fine-tuned on synthetic data, condit
"Neural Lexical Search with Learned Sparse Retrieval" - SIGIR tutorial with slides, demo, COLAB notebooks Learned Sparse Retrieval (LSR) techniques use neural machinery to represent queries & documents as learned bags of words. In contrast
Qdrant 1.7.0 https://qdrant.tech/articles/qdrant-1.7.x/ #ycombinator #vector_search #new_features #sparse_vectors #discovery #exploration #custom_sharding #snapshot_based_shard_transfer #hybrid_search #bm25 #tfidf #splade #qdrant
What “splade” means
WikipediaLearned sparse retrieval (LSR) or sparse neural search is an approach to Information Retrieval which uses a sparse vector representation of queries and documents. It borrows techniques both from lexical bag-of-words and vector embedding algorithms, and is claimed to perform better than either alone. The best-known sparse neural search systems are SPLADE and its successor SPLADE v2. Others include DeepCT, uniCOIL, EPIC, DeepImpact, TILDE and TILDEv2, Sparta, SPLADE-max, and DistilSPLADE-max.
“Learned sparse retrieval” on Wikipedia (CC BY-SA) →#splade across platforms
every network with a public tag surfaceFollow #splade 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/splade