I wanted to see whether AI coding tools could help one person ship an actual native Linux app in a day, not just generate a demo. The result was Disciple, a GTK4/Libadwaita organizer with notes, habits, projects, calendar events and an optional AI assistant. The biggest lesson was that generating code was the easy part. Most of the time went into catching things the tests missed: broken window behavior, duplicate actions, weird layouts, unsafe context sharing, model-selection problems and packaging. A few choices that made the AI side more reliable:
- Dates, recurrence, database writes, matching and Undo are handled locally instead of trusting the model.
- The model only receives the actions relevant to the current request.
- Conversation history and personal context are bounded to avoid wasting tokens.
- Reversible changes happen immediately with Undo.
- Permanent deletion still requires confirmation.
- Personal context is visible and editable instead of hidden memory.
- The organizer still works without an AI provider. It ended up with 180 tests and working Flatpak, RPM and DEB packages. AI wrote a lot of the code, but it also repeatedly produced things that technically worked and felt terrible until I tested the actual app and pushed it to fix them. I made a short video showing the finished workflow: https://youtu.be/IWDRk7Hc2vY Source: https://github.com/justlinuxnoob/disciple I’m curious whether people think this architecture makes sense for AI-connected desktop apps, especially keeping deterministic actions outside the model. submitted by /u/PoleTV
Originally posted by u/PoleTV on r/ArtificialInteligence
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