The Setup I had an idea I'd been sitting on a simple habit tracker, nothing novel, but I wanted something with a specific feature set I'd never found in any existing app: habits grouped by life domain (health, work, relationships, personal growth) with a weekly rhythm instead of a daily one. Weekly habits, not daily. Sounds simple. Apparently, nobody has built this exact thing in a way I like. The First Four Hours: Surprisingly Good The architecture planning conversation with Claude was genuinely useful. I described what I wanted, Claude suggested a state management approach (Riverpod, which I agreed with), a data model, and a folder structure. Good suggestions, well-reasoned. I'd done this myself before and the AI version was roughly comparable in quality to what I'd have come up with, maybe 20% better because it caught a data modeling edge case I'd have probably hit later. Hours Four Through Twelve: The Grind This is where the honeymoon ended. The Part That Actually Impressed Me AI is genuinely exceptional at some specific things. Writing boilerplate. Generating test cases once I described the behaviors I wanted to test. Explaining what a piece of code was doing when I couldn't immediately parse it. Suggesting fixes to compiler errors instantly. Finishing at Hour 24 I shipped something at 9 PM the next day. It worked. The core habit tracking flow was complete. Weekly rhythm, domain grouping, basic stats screen. Running on my phone. What I'd Do Differently Start earlier in the day. Use AI for architecture and data layer planning where it's genuinely good. Write the UI code myself rather than generating it. Use AI for test generation and debugging assistance. Not as a "write my app for me" tool but as a "be a really fast, always-available pair programmer" tool. mobile app development company India - Mittal Technologies has been navigating this in real projects, happy to share what we've seen.