I would love to be able to do the clustering from a CSV instead of a collection of Markdown files. I know I can easily generate the files, but I used to do this directly for very short text inputs (just titles or words) on nomic.ai (before they pivoted to 'Enterprise')
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.
Hey HN - I first posted about my knowledge base product, Atomic, here around a month ago; since then, a viral tweet by Karpathy has produced a torrent of AI powered knowledge base projects. meanwhile I've been shipping like crazy, here are some of the new features shipped in the last month:
- Rebuilt the iOS app with an Android app on the way
- expanded both the MCP and internal agent chat toolkit immensely
- A custom, CodeMirror6-based markdown editor with obsidian-style rendering
- A dashboard view that provides a daily summary of atoms created or updated in the last day
And many bug fixes and improvements across the board. Atomic is MIT licensed. You can download the desktop app, but the true power is unlocked by self hosting an atomic server, which any client (web, mobile, or desktop) can connect to from anywhere. You can add content to your knowledge base directly, or via RSS feed, web clipper, mobile share capture, obsidian sync, or REST api.
I would love to be able to do the clustering from a CSV instead of a collection of Markdown files. I know I can easily generate the files, but I used to do this directly for very short text inputs (just titles or words) on nomic.ai (before they pivoted to 'Enterprise')
Generally curious, how is this different from pointing Claude Cowork at a Obsidian Vault?
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.
Hey HN - I first posted about my knowledge base product, Atomic, here around a month ago; since then, a viral tweet by Karpathy has produced a torrent of AI powered knowledge base projects. meanwhile I've been shipping like crazy, here are some of the new features shipped in the last month:
- Rebuilt the iOS app with an Android app on the way
- expanded both the MCP and internal agent chat toolkit immensely
- A custom, CodeMirror6-based markdown editor with obsidian-style rendering
- A dashboard view that provides a daily summary of atoms created or updated in the last day
And many bug fixes and improvements across the board. Atomic is MIT licensed. You can download the desktop app, but the true power is unlocked by self hosting an atomic server, which any client (web, mobile, or desktop) can connect to from anywhere. You can add content to your knowledge base directly, or via RSS feed, web clipper, mobile share capture, obsidian sync, or REST api.
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