alt Hacker NewsYou just install it on your phone and use the app.
Maintainability concerns are entirely overblown by people who don't use agentic AI to develop large mobile apps, but anyway give their opinion as if they had that experience.
I put in a few hundred hours, and I reached the same conclusion as Shopify. With reviews from other models and then a manual QA pass the result is fully usable.
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Same result here just using plain Opus 4.8+. I had a web ap with a PWA approach. Now I have an iOS app written in Swift/SwiftUI and an Android app in Kotlin in the appstores. I do not know how to code a single line of Swift or Kotlin. You just test the app and iterate with the AI over it until it is stable and does what it should.
> You just install it on your phone and use the app.
Some people on the cybersecurity side are starting to cry....
> You just install it on your phone and use the app.
OP says they don't have an android phone...
That's a recipe for regressions as the amount of surface you have to cover with "just...use the app" gets bigger and bigger.
You can write more automation to test it. But that's also how you end up with ever-growing test run times.
There are much better ways that aren't just "throw out the LLM" either. You just need to be more focused on throughput. Requiring manual validation can pretty rapidly require more hours than just sanity-checking code by hand, even (and I'm not advocating that for every use case, either.)
I can't afford manual QA passes if I'm gonna go as quickly as I want to.
I am using Gemini as well as Opus on a somewhat small project in React Native and I can not imagine this thing being able to build the whole thing on its own without it being a dumbpster fire.
Can you share some details of how you work? What models? What harness?
Damn, we really gotta get rid of the vibe coders. Bad things are on the horizon if we keep encouraging these naive habits.
> You just install it on your phone and use the app.
That‘s how you check functionality but that’s not how you get the bugs in the code.
I work as a professional app developer. And I find this take to be naive.
Most of the time when I review code from AI, there is always something to improve.
It’s either a maintenance issue. e.g., Opus recommended and implemented a fix for a database corruption crash. This was ~400 lines of code with many moving parts. I reviewed, and found out Android Room library already handles this recovery case, and all I needed was a 10 liner PR that catches this exception and ignores it.
The maintenance is not only the burden on the human and LLM. With too many moving parts, it becomes harder and harder to build and verify the correctness of future features. Yes you can write test for this and that, but it didn’t need to exist in the first place.
The second problem is correctness issues. Especially the edge cases. You cannot just manually test out a race condition on a phone! Sometimes it happens! Sometimes it doesn’t! If it leads to a visible signal like a crash, then yes, you can try to reproduce it. But there are a lot of these that are “silent” and would just lead to bad experiences.
We already had a software quality crisis! And I think such views only exacerbate the situation! Quality matters!
And this is not an anti-AI stance. I vibe code personal projects where I don’t even look at the code. But when I use AI as a professional engineer, I act like a professional. Because these products do have an impact on people’s lives.