Hey! I just shared a write-up on something I learned while rebuilding a product with AI agents:) ...Hey! I just shared a write-up on something I learned while rebuilding a product with AI agents:) ...
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The giant agent file problem is real, I hit context limits constantly before splitting docs by domain. Curious how you decide what counts as core context versus something the agent should look up on demand.
Yes! I'mcurrently a lead tech at a stratup and im migrating the old app into the new stack :)
So I'm doing the job of a product owner and also building the whole app!
The table-of-contents-in-the-main-file pattern is the key move here - it keeps the agent's default context small while still giving it a map to go fetch the architecture or naming-convention doc only when the task actually touches that domain.
I also really like the feature-spec skill I made for the repo. Having AI ask you further questions to get into the edge cases and fill better the documentation files.
This maps to how I think about client documentation too, keep the source of truth thin and let people drill into details only when they need them. Curious if there was any part of the product where the thin main file approach broke down and you had to load more context up front.
Great to hear @Maty Sandoval ! I'm thinking to do a template repo or a sort of framework so others can use it also!
Hmm I don't think so, as the main file always gets updated if there is a new doc added to the repo. So if I add a new document, the main file will add it too, so the ai knows it...
This makes a lot of sense. Keeping the main context small and letting the AI pull in the right docs when needed seems way easier to manage as the product grows.
Voted Dark. On a dashboard watching trucks move from gate to dock all day, dark is easier on the eyes, and the 3D bars and route lines glow against it. Light is clean, but dark makes the live movement the focus.