Fast State-of-the-Art Static Embeddings
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Updated
May 6, 2026 - Python
Fast State-of-the-Art Static Embeddings
Fast Multimodal Semantic Deduplication & Filtering
Official Rust Implementation of Model2Vec
Pre-train Static Word Embeddings
A unified web extraction and stateful automation engine for AI. Replaces heavy testing frameworks with token-optimized browser control, deep research, and HITL.
CLI for semantic search on your computer. Searches text files and identifies the most relevant chunks to your query.
Drag and drop your Excel or CSV file with a text column for batch calculating semantic similarity for your queries.
Fast hybrid code search for agents. Pure Go, drop-in MCP-compatible with semble.
Fast, local semantic search over web content for AI agents. Hybrid BM25 + potion-retrieval-32M embeddings, cross-page dedup, token-budget mode, MCP server, SearXNG bridge. ~90% fewer tokens than raw web_fetch.
Blazingly fast word embeddings with Tokenlearn
Fast hybrid (BM25 + semantic) local code search for AI agents - pure Rust, persistent index, MCP/gRPC servers, tree-sitter symbols
Local-first cross-corpus retrieval MCP server — model2vec embeddings + LanceDB (Tantivy BM25) + SQLite. One binary; hybrid (dense + BM25) search across markdown, code, and Claude Code sessions.
An effort to build a holistic matching engine that supports regex, cucumber expressions, semantic phrases, and combinations of that. Could be useful for Guardrails, Caching systems etc. Can eventually be a symbolic AI engine
One install, three MCPs: graph chat memory + semantic code search + backend. Benchmarked with claude-haiku-4-5: -19% tokens & 23× faster on memory-recall questions.
Describe a concept and instantly find the perfect word. A free reverse dictionary powered by AI that runs entirely in your browser — no sign-up, no tracking.
Narnium's/H2CO3's Rust Implementation of Model2Vec
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