For those who do local AI or want a rag system that doesn't eat up all their RAM, I released Skeg a few months ago. I'm currently finishing up the optimizations for x86 and Linux.

I saw @Qdrant Edge + Nvidia, and I thought... #Skeg can already do that! So thanks, @Nvidia, for the recognition models, and enjoy the demo. running on my old MacBook Pro M1 ~9.5mb in RAM…

28 ephemeral browser microVMs, in parallel, in 37 seconds. Each forks in ms, browses a different site, and self-destructs on disconnect. 555 docs → 1,267 vectors → semantic recall across all of them.…

What am I doing? It's simple. One morning I woke up and decided to create a vector database that's different from the rest. Why? Because I think it’s good to try different approaches every now and…

I'm starting to publish the Skeg benchmarks. This includes the case with Llama 3.2, which uses various databases to perform RAG. ⭐️ 📊

Tired of vector DBs killing your RAM? Skeg is made for big LLMs on the same machine. - 1M vectors → ~400 MB RAM - 1-4 bit quantization + reranking - Fast Rust Long-term memory for your agent without…

I'm terrible at “selling” my work. I'm trying to get better at it, but I'm not sure I'll succeed.

apparently @antirez and I have been independently obsessing over RAM efficiency for AI workloads. he published first. i took the scenic route.

1M vectors. 419 MiB RSS. recall@10 ≥ 0.998. p99 < 4ms. qdrant-hnsw needs 4162 MiB for the same corpus. skeg does it in one tenth. disk-resident Vamana index, SIMD NEON kernels, PQ + int8 + TurboQuant…