If you’ve been doing Android development for a while, you know the drill. You start a new project, wait for Gradle to sync (and maybe grab a coffee...
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Yeah this is basically boilerplate elimination on steroids. Compose + MVVM setup is usually where most time gets wasted anyway.
It’s great for scaffolding, but I doubt it replaces real design decisions once state, scaling, and edge cases kick in.
Feels more like fast first draft than production architecture.
Have you tried pushing a generated project beyond the default 3–4 screens yet?
chech my profile what i build u will be amazed
totally agree that the new prompt-to-android-app feature could save so much time on the boilerplate. it’s exciting to see how AI is reshaping app development. speaking of building quickly, at Moonshift, you can get a full next.js + postgres + auth app deployed in about 7 minutes. if you want to give it a shot, I can set you up with a free run.
"Awesome layout structure here! The UI transitions are incredibly fluid. I’m currently wrapping up a major overhaul of my own portfolio site using a Next.js core with a Supabase vector database for a custom RAG architecture. It’s always inspiring to see how other developers handle responsive modular layouts and asset caching. Thanks for sharing the blueprint!"
Tried this — clean Compose scaffold, MVVM split is solid, real time-saver on greenfield UI work.
The interesting tension I hit building Background Camera RemoteStream (play.google.com/store/apps/details?id=com.superfunicular.digicam) over 75 AI sessions: prompt-to-Android nails the cold-start scaffold, but the work that ate >80% of my hours was Camera2 lifecycle + Doze/WorkManager scheduling under screen-off — exactly the stuff with no canonical example in training data, so model output drifts and you have to baby it with logcat traces.
Anyone else seeing prompt-to-Android work beautifully for the first emulator boot but stall hard on OEM/platform-edge stuff (foreground services, Camera2, AudioFocus, Doze)? Curious whether AI Studio's grounding extends to Pixel-only quirks or if it's still mostly textbook MAD.
The Gradle coffee break is so real it hurts 😂 But honestly, the more interesting question to me is: once AI generates the app, who's responsible for testing it? We run Hermes for automated testing and I've seen a pattern where AI-generated mobile code looks right on the surface but has subtle lifecycle bugs (rotation, process kill, back navigation) that a human would catch because they understand Android's state management. The prompt-to-app pipeline is amazing for speed — but I feel like we're 80% of the way there and the last 20% (testing, edge cases, production hardening) is where the real work still lives.
Curious — have you seen any patterns in what the AI consistently gets wrong in the generated Android apps? Followed you, would love to see more thoughts on this! 🙌
Thanks for sharing. I tuned into Google I/O as well, and I’m amazed at how fast Google AI Studio is evolving. This feature looks incredibly cool — definitely something I want to try out.