February 17, 2026
Popcount popcorn, anyone?
Hamming Distance for Hybrid Search in SQLite
DIY “smart search” lands in SQLite; crowd splits between hacks and plug‑ins
TLDR: A developer built “smart” meaning-based search inside SQLite using compact bit vectors and Hamming distance, skipping a separate vector database. Commenters split between DIY speed hacks, plug‑and‑play tools like sqlite‑vector, and scrappy ideas like keyword expansion—with a side of “can AI write this for me?” curiosity.
One dev just squeezed “smart search” into tiny SQLite by turning meaning into bit‑fingerprints and measuring how many bits differ—aka Hamming distance. Translation: faster, smaller, no extra database needed. The catch? Some accuracy gets tossed—and that’s exactly where the comments went full reality TV.
On Team Hacker, users cheered the lean approach and pitched even leaner tricks. One standout suggested checking just the first 64 bits as a quick screening, then doing the full compare only for close calls—“approximate, but worth it” for speed demons. Meanwhile, the convenience crowd rolled in with receipts: “Why roll your own?” cried the plug‑in brigade, pointing to sqlite-vector and a USearch SQLite extension that “gets similar performance and is very convenient.” DIY pride vs off‑the‑shelf sanity—fight!
Then the philosophers entered: one commenter proposed a “poor man’s embeddings” using keyword expansion—make documents match through related terms even without shared words. It’s the thrift-store version of semantic search, and honestly? Kinda brilliant. Rounding out the thread, someone asked if today’s AI models can help write SQLite or Postgres extensions—cue nervous laughter from anyone who’s segfaulted in C.
Bottom line: the post proved you can bolt “meaning” onto SQLite with clever bit math, and the crowd is gloriously torn between handcrafted speed hacks, plug‑and‑play tools, and scrappy alternatives. Popcount bros vs plugin pros—place your bets.
Key Points
- •The article implements semantic search in SQLite using binary embeddings and Hamming distance to enable hybrid search with FTS5/BM25.
- •Binary embeddings reduce a 1024-dimensional vector from ~4 KiB (float32) to 128 bytes, trading some accuracy for speed and storage benefits.
- •Hamming distance is computed by XORing two binary vectors and applying a popcount; modern CPUs have dedicated popcount instructions.
- •A C-based SQLite extension registers a hamming_distance SQL function that accepts two equal-length BLOBs and returns an integer distance.
- •The implementation processes data in 64-bit chunks and assumes unaligned 64-bit access on x86_64 and ARMv8-A (e.g., Apple Silicon, Raspberry Pi 4), with shared library loading on Linux (.so) and macOS (.dylib).