As a solo developer behind MoodTrend, a mood tracker for overworked individuals prone to burnout, I've had my fair share of challenges. With 340 MAU and $9/mo in ad revenue, the app has been a success. However, my legacy codebase was holding me back from truly iterating and adding new features.

The core mission of MoodTrend is to help people stay functional and avoid burnout. But with 0% test coverage and scattered logic, my old code made it a terrifying risk every time I tried to add new features. It was time to break free and start anew.

Why AI Rebuild Was the Way to Go

As someone who can't stop once they've started, I've had my share of burnout collapses twice. That's why I built MoodTrend – to help others like me stay functional. But my codebase was holding me back from achieving this goal. Old Riverpod patterns, outdated Flutter versions, massive linting violations, no test coverage, and logic scattered everywhere made every attempt at adding features a high-risk venture.

I've been experimenting with AI code editors for a while now, testing different tools every few months. When I discovered Codex AI, I knew it was the right tool for the job. With its ability to generate high-quality code, I saw no reason to switch from Antigravity and started rebuilding my entire codebase in just 17 days.

The Rebuild Process

Constant supervision was key during the rebuild process. From November 15th to December 1st, I invested over 100 hours of active monitoring and iteration, ensuring every commit mattered. To ensure quality control, I documented linting rules and architectural patterns in an AGENTS.md file, allowing Codex AI to self-correct. Additionally, I let Codex handle PR reviews.

Another crucial aspect was mapping out the entire dependency tree using DeepWiki. This wasn't just cleanup – it allowed me to finally write meaningful test code. Now, I know what breaks when something changes, giving me a sense of control and confidence in my codebase.

The Before-and-After Comparison

Before the rebuild, my codebase was in a terrible state:

  • Linting warnings: Uncountable
  • Test coverage: 0%
  • Flutter version: Stuck on outdated
  • Code responsibility: Scattered (multiple domain models and logic crammed into single files)
  • Testability: Impossible

Fast-forward to after the rebuild, and the results are stunning:

  • Linting warnings: Zero (even with stricter DCM linting rules enabled)
  • Test coverage: 67%
  • Flutter version: Now on stable
  • Architecture: Clean separation of concerns
  • Testability: Built-in from the start

The Real Moment of Truth

I recently shipped a subscription feature, and while I don't have any paid users yet (fresh release), the $9/mo is purely from interstitial ads. What matters most, however, is that I can now iterate without fear. I can add features. I can maintain this long-term.

For fellow developers interested in AI rebuilds, what tools are you using? What surprised you? For MoodTrend users, what mood patterns do you see? What habits actually help?

App link here. Feedback appreciated!