Lift Tracker
Working notes from a single session of using the app and writing down what was annoying, confusing, or missing.
Most of Lift Tracker's roadmap didn't start as a polished spec. It started as raw notes taken straight from using the app myself: friction points, half-formed ideas, and questions about what to build next. Keeping the input this loose mattered, because the next step was feeding it directly into an AI coding session rather than translating it into a formal ticket first.
User feedback was immediately broken into component parts, with each request systematically addressed in minutes. From a one-line complaint to a shipped feature (and its own UI) in the same session.
This diagram traces one actual feedback cycle end to end: a user's text message about wanting an easier way to see workout history led to a working feature, complete with its own UI, in the same sitting. There was no backlog, no sprint planning, and no separate design pass.
The gap between "this is annoying" and "this is fixed" was minutes, not weeks.
The current list view: lifts grouped into workouts, drag-to-reorder, and a Fast-mode toggle for rapid-fire logging.
That cycle, repeated dozens of times, is how Lift Tracker actually got built. Drag-to-reorder came from not wanting to scroll past finished lifts. Fast-mode's rapid-log rows came from wanting to log a set without opening anything. Workout grouping and pill filters came from training different splits on different days and wanting to see just one. Even the composite score above the lift list started as a simple question: am I actually getting stronger, or does it just feel that way?
Weight tracking and the composite score, expanded. One of many features that started as a one-line ask.
As of writing, this workflow has shipped well over 100 individual features and fixes: weight and waist tracking, per-user accounts, a day-grouped workout history, gamified weekly streaks, full-history export, and dozens of smaller polish passes, with several sometimes shipping in a single day.
The point isn't that AI wrote the code unsupervised. It's that the loop from "I noticed this" to "this is live" got short enough that almost nothing is too small to fix.
Working notes from a single session of using the app and writing down what was annoying, confusing, or missing.
Most of Lift Tracker's roadmap didn't start as a polished spec. It started as raw notes taken straight from using the app myself: friction points, half-formed ideas, and questions about what to build next. Keeping the input this loose mattered, because the next step was feeding it directly into an AI coding session rather than translating it into a formal ticket first.
User feedback was immediately broken into component parts, with each request systematically addressed in minutes. From a one-line complaint to a shipped feature (and its own UI) in the same session.
This diagram traces one actual feedback cycle end to end: a user's text message about wanting an easier way to see workout history led to a working feature, complete with its own UI, in the same sitting. There was no backlog, no sprint planning, and no separate design pass.
The gap between "this is annoying" and "this is fixed" was minutes, not weeks.
The current list view: lifts grouped into workouts, drag-to-reorder, and a Fast-mode toggle for rapid-fire logging.
That cycle, repeated dozens of times, is how Lift Tracker actually got built. Drag-to-reorder came from not wanting to scroll past finished lifts. Fast-mode's rapid-log rows came from wanting to log a set without opening anything. Workout grouping and pill filters came from training different splits on different days and wanting to see just one. Even the composite score above the lift list started as a simple question: am I actually getting stronger, or does it just feel that way?
Weight tracking and the composite score, expanded. One of many features that started as a one-line ask.
As of writing, this workflow has shipped well over 100 individual features and fixes: weight and waist tracking, per-user accounts, a day-grouped workout history, gamified weekly streaks, full-history export, and dozens of smaller polish passes, with several sometimes shipping in a single day.
The point isn't that AI wrote the code unsupervised. It's that the loop from "I noticed this" to "this is live" got short enough that almost nothing is too small to fix.