RAG

You’re building a RAG pipeline with a million documents. Each vector is 1536 floats — OpenAI ada-002 style. And that’s about 6 KB per vector in float32. Do the math: 10 million vectors = 31 GB of RAM just for the index, before your application code even starts. That’s the wall a lot of self-hosted RAG projects hit. But Pinecone costs a fortune. FAISS needs a training phase and still takes ~8 GB. I’ve been tracking tools that tackle these memory bottlenecks — my Headroom review covers LLM context compression from a different angle. So when I saw turbovec hit #2 on GitHub Trending with 10.2k★ in its first week, I had to try it. ...

Google’s Notebook LM is pretty great on the surface. Upload a PDF, get a summary. Throw in a YouTube link, get a podcast. But here’s the thing — your data lives on Google’s servers, you’re locked into Gemini, and you can’t even access it programmatically through an API. That’s where Open Notebook comes in. And it’s an open-source, self-hosted alternative that replicates Notebook LM’s core features and then some. Still, 24,600+ GitHub stars, 739 commits, 51 contributors, and a thriving community aren’t everything. I spent a full afternoon installing it, poking around, and stress-testing it against my own research docs. Here’s what I found. ...