Biggest difference is Atomic leverages an LLM to auto-tag and a text embedding pipeline to drive semantic search - so the knowledge base is self-organizing. The bet here is that having an agent grep the filesystem is fine for a carefully curated, relatively small set of markdown files. It starts to degrade if you approach your knowledge base as a place to put everything: personal notes, articles you find interesting, entire textbooks if you want to. Having a vector database in this context is pretty much required past a certain scale; a filesystem-based approach is just an incredibly inefficient way to do retrieval in this context, and your agent is bound to miss important data points.