I wish the scene was more collaborative - instead of everyone writing their own. But I guess this is the llm curse - too easy to start. I am afraid it will all go in the LangChain direction with VC funding designs that are not yet ready solidifying choices that would normally be superseded.
I feel like most of these applications all boil down to "Obsidian but with AI integration baked in up front". It'd be interesting to see approaches that actually rethink commonplaces of the experience (graph view etc) rather than just reproduce the same thing but "with ai"
They keep adding this “cloud of dots” where each dot represents a concept or something you wrote, and they are linked to other dots… sure it’s pretty the first time you see it, but it’s not useful at all beyond that
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've been tinkering with my own version of this idea off and on for months, and it's great to see someone finally make the thing that I've been wanting since LLMs hit the scene. Congrats on everything you've shipped!
Killer feature: add audio transcription. Record that meeting; just tell the app what you want to remember. It gets transcribed and then processed like any other note.
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.
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')
> Not x — but y.
Am I the only one who feels a bit betrayed after reading LLM text? I am not even willing to try out the app after I notice… which is a shame.
At least polishing the obvious parts would help a lot and is not that much work.
Reviewed: https://zby.github.io/commonplace/agent-memory-systems/revie...
It is the second llm wiki on frontpage today!
I wish the scene was more collaborative - instead of everyone writing their own. But I guess this is the llm curse - too easy to start. I am afraid it will all go in the LangChain direction with VC funding designs that are not yet ready solidifying choices that would normally be superseded.
Lots of good ideas and divergent methods and sources here cheers for the link
What's the other one? I cannot find it.
Probably this one (Tolaria) https://news.ycombinator.com/item?id=47882697
I feel like most of these applications all boil down to "Obsidian but with AI integration baked in up front". It'd be interesting to see approaches that actually rethink commonplaces of the experience (graph view etc) rather than just reproduce the same thing but "with ai"
They keep adding this “cloud of dots” where each dot represents a concept or something you wrote, and they are linked to other dots… sure it’s pretty the first time you see it, but it’s not useful at all beyond that
but there are a lot of them
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've been tinkering with my own version of this idea off and on for months, and it's great to see someone finally make the thing that I've been wanting since LLMs hit the scene. Congrats on everything you've shipped!
Killer feature: add audio transcription. Record that meeting; just tell the app what you want to remember. It gets transcribed and then processed like any other note.
Generally curious, how is this different from pointing Claude Cowork at an 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.
Does the LLM auto-tagging and embedding pipeline run on the device, or are they remote calls?
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')
"Runs everywhere"
...except my Android phone LOL
working on it https://github.com/kenforthewin/atomic/tree/main/mobile/andr...