Hey HN, I built Rememex a semantic search layer for your local files.
The problem: I kept losing files. Not because they were deleted, but because
I couldn't remember the exact filename or keyword. grep needs the exact word.
Everything only searches filenames. I wanted to type what I meant and find
what I needed.
How it works:
- Indexes 120+ file types (code, docs, images, configs)
- Hybrid search: vector embeddings + full-text + JINA cross-encoder reranking
- OCR on images via Windows UWP engine
- Reads EXIF GPS → reverse geocodes to city names ("photos from istanbul" works)
- EXIF dates → human language ("summer morning" finds a July 8am photo)
- Smart chunking per language (Rust at fn/struct, Python at def/class)
- Built-in MCP server so AI agents can use it as a tool
Everything runs locally. Embeddings use a local ONNX model (Multilingual-E5-Base)
by default, though you can optionally plug in OpenAI/Gemini/Cohere.
Named after Vannevar Bush's Memex (1945) his vision of a device that stores
and retrieves all human knowledge.
Stack: Rust (Tauri 2), React/TypeScript, LanceDB, rayon
I benchmarked it against grep for agentic tasks rememex consistently finds
things in 1 step where grep takes 3-5 or fails entirely. The key difference:
grep needs the exact keyword, rememex needs the idea.
Windows-only for now (UWP OCR dependency), but the core engine is portable.
Would love feedback on the search quality and architecture.
MIT licensed, free forever.
Show HN: Rememex – Semantic file search that runs 100% locally (Rust/Tauri) | Heykuki News