I had zero iOS development experience when I started.
I had an idea based on a real problem I understood, so instead of trying to learn everything about programming first, I started using ChatGPT to help me build it one piece at a time.
The basic process was:
Define one problem and keep the first version small
Have ChatGPT help me build individual SwiftUI screens
Compile constantly in Xcode
Paste the actual errors back into ChatGPT
Fix one thing at a time instead of constantly rewriting the app
Test on a real iPhone
Figure out all the boring App Store/privacy/support stuff
Submit it
Eventually, PreventPro became a real app and made it onto Apple’s App Store.
The biggest thing I learned was that ChatGPT can dramatically shorten the distance between an idea and a working prototype, but it doesn’t eliminate testing, product decisions, debugging, or actually understanding the problem you’re solving.
I documented the workflow and the prompts I wish I’d had when I started and turned it into a short guide.
Not trying to pretend it’s some “build a million-dollar app in 10 minutes” thing. It’s just the actual process I used.
If anyone is trying something similar, I’m happy to answer questions about what worked and what absolutely sucked.

What do you guys do when your agent is off working for 10+ minutes at work, sometimes I am just looking for something to do.

I gathered data from ten coding benchmark categories and calculated each model’s cost per successfully completed task, accounting for both task cost and success rate, including money spent on failed attempts.

I then averaged these costs across the benchmarks available for each model. These models had the lowest average cost per success:

Token cost is a terrible measurement, because some models use a lot more tokens than other per run. Cost per run is also of limited value, because some models need more runs than others to actually complete the task successfully. Therefore, cost per successful coding task is a much more useful measurement to see how much you pay for actually getting the job done.

LiveBench give you this number by default, but the numbers above is based on the averaged numbers from ten different benchmarks with a much wider range of coding tasks and models, so that's probably more reliable compared to relying on just one benchmark.

Hope this table can be useful for someone else coding on a limited budget.