What's holding me back from AI repos and agents isn't running it locally though. Its the lack of granular control. I'm not even sure what I want. I certainly don't want to approve every request, but the idea of large amounts of personal data being accessible, unchecked, to an AI is concerning.
I think perhaps an agent that focuses just on security, that learns about your personal preferences, is what might be needed.
Agreed regarding the privacy/security hesitations. Running the models locally with ollama is an option, but of course there's the hardware requirements and limitations of open source models to contend with. ultimately it's a balance between privacy and ease of use, and I'm not sure that there's a good one-size-fits-all for that balance.
Right now I use a skill on every commit (or when the auto compact warning starts showing up) that forces Claude to update its "memory" . It is a flat markdown file that gets stuffed into conversations, not v smart. Claude forgets things I've told it dozens of times.
Your MCP server approach makes total sense. The create_atom tool alongside semantic_search makes it read/write from day one. I would love to wire a stop hook to automatically atomize session insights (the write side). That's the dream: I work on something in my code, Claude learns why, and that knowledge flows into Atomic without me saying "remember this."
Not 100% sure what are the ingestions methods available ? Browser extension clipper and RSS are two. I guess I can manually create a node/atom ? Can it scan a local folder for markdown notes ? Or ocr some pdf -> markdown/frontmatter sidecar files -> atomic node ? That would be the dream.
Yes, regarding ingestion methods the following are available:
- RSS feed
- Web clipper browser extension (working on publishing the extension to the chrome store)
- Import a folder of markdown files (desktop app only)
- Manually ingest a one-off URL
- Manually create an atom
The iOS app is a more recent addition, also not yet published to the app store, but the idea is to add a share target so that during mobile browsing you can quickly add articles to your KB.
PDF support would be great, and I'd love to hear more ideas about ingestion methods if anyone has them!
I saw sqlite-vec for semantic search so I assume notes are stored in sqlite.
- What considerations did you have for the storage layer?
- Also does storage on disk increase linearly as notes/atoms grow?
Yeah, the data layer is sqlite - not just the vectors but the notes themselves, wiki and chat data, etc. The project started out as solely a desktop app, so sqlite seemed a natural fit. Since then I've pivoted to a server/client model, but the purpose is still very much for personal use so I still feel like sqlite fits that niche.
