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Clark Hash, 32x smaller searchable sketches for embeddings
7 starsRust
Clark Hash, 32x smaller searchable sketches for embeddings
by stan_kirdey·May 27, 2026·1 point·0 comments
AI Analysis
●●●BangerBig BrainWizardry
32x embedding compression without calibration beats product quantization's training overhead.
Strengths
- •Stateless sparse-JL projection means zero corpus-wide calibration before storing vectors
- •Asymmetric query sketches let database vectors stay bit-packed while queries remain floating-point
- •Published paper sources and reproducible benchmarks on crates.io with docs.rs API docs
Weaknesses
- •Only includes flat compressed-scan index; no HNSW or ANN integration for large-scale deployments
- •Quality tradeoffs depend heavily on embedding model and sketch configuration parameters
Category
Target Audience
ML engineers, vector database builders, edge AI developers
Similar To
Faiss product quantization · ScaNN · DiskANN
Post Description
made a small library using GPT5.5-Pro and autoresearch
you can convert 384-dim f32 vectors go from 1536 bytes to 48 bytes without calibration. works for petabyte scale processing of text in pure online manner.
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