Also, sharing a link would be great if you're comfortable with the idea.
I'm still in the process of launching the app, it went live just two weeks ago. So far only 30 users, I have not yet started the ad campaign.
Basically I don't look at the codebase at all, I have long stopped using the IDE or doing code reviews.
From a product perspective, there have been no big surprises. Only once I was surprised to see a feature I thought I implemented only on a specific screen being available on a different screen too, hidden behind a drag gesture, spawning an UI component I totally did not expect.
While that seems like a loss of control, it's not too different from enterprise work with many contributors where I am also not aware of all changes.
About maintenance cost, no, I do not think so. It generally became easier over time since Opus 5.5 delivers better results than GPT-5.5 did.
From a token perspective, a large part of the resources go into translating to 30 languages and checking the screenshots for layout regressions.
Sorry, but at this point you have no idea what the maintenance looks like.
I've heard this argument a few times on here, I think it's nonsense.
At 30 users you have no idea what maintenance looks like. That's literally what maintenance is - responding to real world usage.
Come back with your experience when you have lots of users that stick with your app, you're putting out fires and implementing new features. Your experience will be meaningful after 6 months to a year.
I'll wager that right now you have a PoC that needs a lot of work to be something that retains users. That's not a criticism, that's just how it works for every startup.
I have a pretty good idea about what maintenance looks like. Particularly so about the technical aspects of how effective the models are at refactoring or fixing bugs.
Even if I were to accept the argument that users would have entirely different expectations for my app than I do, it hardly changes the fact that I already know I can easily implement large changes in the code base.
Come back after running your mobile app for at least 6 months in the real world with real users and then we will talk about maintenance.
Codebase has never been better and healthier, and any tech debt that has been accumulated after a few releases can be removed in a few prompts when you are out of product ideas and have some free tokens to spare.
(I added US mountains and trails btw: https://fescims.com )
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Yes, a modern model will happily work on the most unmaintainable slop (doesn’t even need to be LLM derived slop, we have all seen bad Human written code) and that is great if you have an old code base to maintain. But if one can avoid it, even if one uses only models and never looks at the code, being efficient has value. Heck, rewrites are doable and affordable now, I have restarted projects and the gains were quickly apparent, even if we just look at model usage efficiency gains.
Even if I ended up with a project of 50k lines, that would take multiple times the effort it took me to get this app ready for the initial release.
Realistically, I wouldn't have finished the project at all.
It doesn't happen often to me. If it does, I find it often handles extra edge cases I only would have found by using the customer as a guinea pig.
Where else did you think they came up with the "10x developer" thing?
(Yes I know LoC is a useless productivity measure, that's just a joke. Obviously it's number of PRs..)
But in doing so, you will also 10x the features delivered. Maybe 5x, who knows?
But you definitely won't have to go through and decimate the generated code as a rule.
Let me tell you the story of the developer trying to reduce the number of tokens they use...
At the current pace, the very idea of maintaining software will become as relevant as the idea of trimming wicks, stropping razors - or making every frame you take with a camera count.
When the marginal cost of replacement drops to a sufficiently low level, the entire paradigm changes.
Digital cameras are another interesting one. Since capturing images is so cheap we just snap a bunch of frames without a lot of thought towards composing them carefully, with a side effect that we get an overwhelming amount of mediocre frames and a few good ones, but we have to sift through.
I’m still not convinced I want to make this tradeoff in programming. But I work in scientific computing where I think it’s (still) really important to understand what modeling assumptions you are making through code
This reminds me of Moore's law in the 1990s.
If you program run slowly, or used too much RAM, you could spend six months optimising it (or removing features). Or you could just sit on it for six months and release it as it is - hardware moved on so fast that, by then, it probably ran successfully.
Your suggestion is a bit like this: don't worry, the pace of model understanding will catch up faster than the code will get disorganised.
I'm not saying I agree but it's an interesting idea.
Disclaimer: I have no SaaS platform, it's a mobile app.
In my experience maintenance does not suffer long term with codebase size, if anything it became easier with model advances.
Most people talking about suboptimal implementation, fragility, and maintenance burden seem to theorize and extrapolate with no actual experience in implementing a large project with AI.
I can't blame them, I see the same in my day job, but the results after 500h+ on a separate project show that the concerns do not manifest in practice.