The main thing that sets it apart: built-in hybrid semantic memory search (vector similarity + full-text) using a bundled SQLite with vector extensions. The index is just a local .sqlite file — no separate vector DB to run. Drop the binary on any machine and memory search just works.
13pixels•1h ago
Are you using `sqlite-vec` under the hood for the embeddings or a custom extension? Also, curious how you handle the 'hybrid' search part—is it just a linear combination of FTS5 bm25 and vector cosine similarity, or do you have a re-ranking step?