Ai-Assisted-Development

Everything up to here was built from imagination: what I thought logging a workout should feel like. Then I trained with it for real, phone in hand, chalk on fingers, rest timer running, and the roadmap got humbled. This part is about the highest-signal planning input the project ever had: actual use. The friction list After the first real gym sessions I dumped every annoyance into a list. Nine items, all of them things no amount of desk-testing had surfaced, because they only exist under gym conditions: tired, hurried, one-thumbed. The highlights: ...

Around the middle of the project something flipped: the agent was no longer the bottleneck. I was. One session built a feature faster than I could specify the next one. Part 2 warned that multiplying agents multiplies cost. This part is about the places where parallelism and delegation genuinely paid, and the structures that kept it from becoming chaos. Parallel phases in worktrees The roadmap had independent phases queued up. Daily check-ins and a settings/themes page touched almost disjoint code. So they ran as separate sessions, simultaneously, each in its own git worktree, launched from pre-written, self-contained prompts. ...

By the end of Part 3 the app worked great, on my desk. This is the part where it had to run somewhere else, and where “AI writes the code” met “computers are still computers.” Spoiler: the app-side work was almost eerily smooth, and every single problem lived in the last mile of Linux plumbing. As the guy whose day job is networks, I found this both humbling and deeply familiar. ...

Part 1 and Part 2 covered the setup: docs as memory, cost controls in config. This is the part where the app actually got built, a core logging loop, a plans system, and a PR detection engine, and where the working rhythm emerged that carried the whole project: One phase per session. Review at every phase boundary. The human tests between phases. That is the entire method. The rest of this post is what it looks like in practice and what it caught. ...

The dirty secret of AI-assisted development is that the naive way to do it, one enormous chat session, the biggest model, all day, is expensive out of proportion to what you get. Before writing much code for RepRepo, I set up cost controls. The interesting part is that almost none of them rely on me remembering to do anything, which turns out to be the whole trick. If you missed it, Part 1 covered the docs-as-memory setup this all builds on. ...

This is a different kind of post for this blog. Less BGP, more JavaScript. I built and shipped a real app, RepRepo, a workout tracker, essentially over one weekend, working with an AI coding agent (Claude Code, in my case, though most of this translates to any of them). Not a todo list demo either. It has a plans system, PR detection with unit tests, multi-user auth, security hardening, nightly backups, and tagged releases deployed by CI, and it is running on the public internet where I actually use it at the gym. ...