Developer builds offline natural-language search for GitHub starred repos using on-device AI

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A developer has built a fully offline semantic search system that lets users query their GitHub starred repositories using plain English sentences. The system uses Google's EmbeddingGemma 300M model — a 308-million-parameter embedding model built on Gemma 3 — to convert search queries into 768-dimensional vectors entirely on the user's device. Stored repository vectors are ranked against the query vector using PGlite and pgvector, returning the top 50 matches without any data leaving the machine. The search interface is built with TanStack Start, featuring a 600ms debounce and URL-based query state, while the Elysia backend handles input validation and routes requests to the local Deno process. The project builds on earlier components covering GitHub authentication, a background embedding worker, and a real-time UI update bus.

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