You beat me to it by a day! But well done Luca. The tool looks excellent and I'm trying it out now.
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.
if "git versioned" means the .md files themselves, I'm sold. I am actually processing files using a git based workflow in order to tell claude what to look at.
I'll definitely give this a spin.
Yes, the .md's are in their own repo, locally. The entire UI is a layer on top of that repo. The UI has some underlying mechanisms that abstract the git operations away from the user, but that doesn't stop a power user from jumping in the shell and accessing the repo directly.
The "magic" starts when Sig contributes to another, remote repo - a central knowledge base that all teammates' local Sig can pull from, and contribute toward.
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the distinction you're drawing is real, totally agree that tolaria feels like a library, sig feels like a field recorder. both necessary, just different moments in the workflow.