Hey there! I am Luca, I write
https://refactoring.fm/ and I built Tolaria for myself to manage my own knowledge base (10K notes, 300+ articles written in over 6 years of newslettering) and work well with AI.
Tolaria is offline-first, file-based, has first-class support for git, and has strong opinions about how you should organize notes (types, relationships, etc).
Let me know your thoughts!
I'm building Sig <https://github.com/adamjramirez/sig-releases> and the architecture overlap is obvious: macOS, plain markdown, git-versioned, designed as context for AI agents.
The difference is where in the workflow we start. Tolaria seems to excel at organizing knowledge that already exists. Sig is trying to solve what happens before that - how to get the knowledge out of your head and into files in the first place. Most of what actually determines the quality of your AI output was never written down: the decision made in the last five minutes of a meeting, the verbal commitment with no follow-up, your actual read on what a conversation meant (not the surface version).
Sig's capture is two layers: 1) factual record first, 2) your personal interpretation on top. Both stored as markdown on your machine. When you're ready to share to a team knowledge base/open brain, it's an explicit decision to do so and opt-in — private by default, team-readable only when you choose.
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