A Karpathy-style LLM wiki your agents maintain (Markdown and Git)
Markdown + git beats pgvector for agent memory — refreshingly simple architecture.
Chat with the just released ufo files. Vector RAG setup with pre-built embeddings database using sqlite. Ready to chat.
Yet another chat-with-docs tool, just with UFO files pre-loaded instead of your own.
UFO enthusiasts, developers testing RAG setups
ChatPDF · DocuChat · PrivateGPT
Contains sqlite database with all files pre-loaded along with embeddings. Has good looking UI you can directly chat with.
Demo(for just next 15 minutes or I run out of 5$ credits). Also, concurrency is limited by openrouter to just 10... so, some requests might fail:
https://open-proxy.space
^ Im running this on my machine begind cloudflare proxy.. and this is the domain I generally use for all dev purposes..
Markdown + git beats pgvector for agent memory — refreshingly simple architecture.
Using a single-file .pardus format with CREATE/INSERT/SELECT + SIMILARITY queries gives a very familiar developer UX for embedding storage. The combination of graph-based ANN, full transactions, thread-safety, and zero external dependencies is an uncommon and useful engineering combo for local-first AI work; it would win more attention with benchmark comparisons and richer ecosystem integrations (connectors/clients).
Breaks down hidden RAG costs like vector storage overhead and HNSW indexing fees.
Columnar storage inside SQLite delivers 130,000x speedup on aggregation scans.
Graph RAG without Neo4j — pure vector search beats HippoRAG on multi-hop benchmarks.
Graph-vector-FTS in one database, but Weaviate and Neo4j already offer hybrid search.