Building Apps with AI: Deep Dive into beads Workflow

Building Apps with AI: Deep Dive into beads Workflow Part 2 of 2: JSONL Memory, Real Examples, and Honest Drawbacks Recap In Part 1, I introduced beads — a git-native issue tracker designed for AI-assisted development. We looked at the Mission House app and the basic workflow. Now let’s go deeper. Where the Tasks Came From Although beads is task-first, I didn’t start from a blank slate. I began with a lightweight requirements.md that described: core user flows data sources (e.g. NAPLAN, Google Maps) output expectations (comparison metrics, charts) I then asked Claude Code (with beads installed) to: Read requirements.md Propose epics, features, and tasks Encode them directly into beads issues with explicit dependencies In other words, requirements existed, but they were treated as input, not as a continuously consulted execution artifact. Once the task graph existed, beads became the primary source of truth. Scope and As