Category
AI Engineering & Vector Search
Expert engineering insights, practical implementation guides, and technical breakdowns.
01
AI Engineering & Vector Search
Product Quantization (PQ) for Vector Databases: Compress 1,536-D Embeddings by 95% with Asymmetric Distance Computation
Master Product Quantization (PQ) for high-dimensional vector search. Learn sub-space decomposition, Voronoi codebook training, and Asymmetric Distance Computation (ADC) with an interactive visualizer.
02
AI Engineering & Vector Search
Embedded Vector Search Compared: SQLite-vec vs USearch vs LanceDB vs HNSWLib
A technical comparison of embedded vector search engines. Compare HNSW memory overhead, disk-native Apache Arrow columnar storage, and SIMD brute-force query latency.
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