I recently shipped a mobile IDE on Google Play.
~90% of the code was generated with AI tools. I handled architecture, product decisions, testing, and iteration.
From the outside, this sounds like it should be fast.
But in reality, it still took around 6 months of continuous work.
What AI actually helped with
AI significantly accelerated:
- boilerplate code
- UI implementation
- feature scaffolding
- refactoring suggestions
- debugging assistance
It reduced the time spent on writing code line by line.
But what still took time
Even with heavy AI usage, a large amount of time was still spent on:
- system design and architecture decisions
- ensuring correctness across features
- testing UI behavior across edge cases
- fixing inconsistent AI-generated logic
- integrating complex workflows (SSH, Git, remote execution)
The key reality
For complex applications, AI doesn’t remove the hard parts of development.
It shifts the workload.
Instead of writing code, you spend more time:
- validating outputs
- correcting assumptions
- defining constraints clearly
- repeatedly testing real-world behavior
What I learned
AI today is extremely good at speeding up implementation.
But it is still far from replacing:
- system-level thinking
- product decisions
- correctness guarantees
- end-to-end testing
In practice, it feels less like “AI builds software for you”
and more like AI increases your throughput as a developer.
The app
The result of this process is a mobile IDE:
- SSH remote development environments
- Git workflow support
- mobile code editing
- AI-assisted coding features
It’s now live on Google Play:
https://play.google.com/store/apps/details?id=com.nimote.nimotecode&pcampaignid=web_share
Final thought
AI didn’t reduce the need for developers.
It changed what developers spend time on.
Top comments (2)
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