The problem
What was broken before AI
AI coding can get messy when everything happens in one branch or one long chat. A model may make changes that overlap, lose the original plan, or mix experiments with real implementation work. For larger tasks, the human can also lose track of what is being tried, which branch matters, and why a particular change exists. The result is speed without enough structure.
What changed
What the use case made possible
Alex’s workflow adds engineering structure around Codex. Plans.md gives the model and human a shared written target. Git worktrees let multiple branches or experiments happen side by side without overwriting each other. GitHub review keeps generated work inside a familiar review process. The human can compare approaches, inspect diffs, and decide which path deserves to move forward.
Why this matters
Why this use case is worth studying
This use case is valuable because it treats AI coding as orchestration, not just generation. Once models can write useful code, the bottleneck becomes managing parallel work, preserving intent, and reviewing output. Alex’s workflow gives those pieces a shape: plan, isolate, run, compare, review, merge.
Use this when
When this pattern applies
Use this pattern when AI coding tasks are getting too large or too parallel for one branch and one chat. It works especially well when you want to test several approaches, split a feature into smaller pieces, or keep agent-generated work isolated until it has been reviewed.
