The market is saturated for new apps.

AI is useless when developing with Maui, as it's too new of a development framework for any AI's knowledgebase to have a clue what to do with it.

The app itself is fairly straightforward, but it included some intermediate complexity in terms of audio capture and calling local models. Both something I'd never done, and as not-a-mac dev something I probably wouldn't have attempted for a side-project while I'm meant to be bootstrapping my own thing.

I didn't touch a line of code, and I was blown away. I'm so impressed in fact that I'm predicting we'll see a resurgence in native apps in the near future. By far the worst (and slowest) part of the process is having to deal with the App Store, and the ridiculous hoops you have to jump through to get past review.

Mostly for fun/scratch my own itch, and using AI as a companion/helper device.

React made simple webapps a case of just gluing dependencies together and much more approachable to a generalist developer than the previous generation of web development was. But native apps weren’t affected in the same way. With AI I suspect we’ll see a lot of simple apps, the ones that really aren’t doing much other than CRUD operations on a remote API, become very heavily AI generated by generalist developers.

But there will still remain a healthy market for working on considerably more complex apps.

The hard part of this app is great design, requiring intentionally designed workflows and lots of real world testing. The code isn't the interesting part and now code isn't taking most of my time. It's great!

Once the design is nailed down and workflows tightened up, I don't expect much active development and can focus on distribution and marketing.

As a solo dev, this feels totally doable but ask me in 6 months.

It's not as dire as you might think. To software developers, the "AI revolution" is largely what the "desktop publishing revolution" was to designers. Yes, it meant the "riff raff" could theoretically play with the pros. Some percentage of the riff raff became pros. Most of the pros eventually adopted the tools and techniques used by the riff raff. Some of the pros didn't survive the transition and happily retired, taking their rubylith, Letraset type, and rubber cement with them.

The silver lining is that most of a software engineer's job isn't coding, it's thinking. LLMs can't do that, and we're not getting to AGI with current AI architectures. LLMs can amplify thinking, and an LLM in the hands of a software engineer or architect is at least two orders of magnitude more effective than it can be in the hands of a vibe coder. As LLMs get better for vibe coders, they also get better for pros.

One can argue that, by the end of the decade, hand-coded [your language of choice] may be considered as unnecessary as hand-coded assembly has been for decades. But coding in modern languages is already 7-8 levels of abstraction above the metal. One more level of abstraction is not the death of software engineering, IMO.

We built iOS first because that's what most of our users have, though the team is mostly on Android.

We built our logic in Rust, so that compiles cross platform. It's really just the UI layer that is per platform.

Though I had initially written our first demo in React-Native, the team wanted to go with native UI.

Lately we've been talking about moving the timeline for our Android app up because we can get AI to help using the iOS app as a foundation.

I haven't seen anything really new and compelling in iOS/Android land. The promise of right once run anywhere continues to be a promise, and the bluetooth libraries across different devices (particularly in Android land) has me concerned.

Anytime I let lose AI (Both Codex and Claude), they end up producing slop that is overcomplicated, and that looks like a generic tutorial interface. Great for toy apps but not for serious one.

When I do most of the architecture, and use AI to 'fill in the blanks' or just finish some functionality here and there, then AI is great.

The main reason is that it is trained by public code that is mostly tutorials, and simple apps, so AI can't build a great looking one by itself (or it doesn't how to do large apps). But for specific features, it is great.

TLDR: It is still a 'fill in the funcionality here' type of help. Folks that are successful with letting 'multiple agents' running in the background are mostly for backend work where the results are easier to verify.

Also, there is a lot of LARPING in twitter sphere exaggerating what AI can realistically do right now. But I can see how it can affect entry level jobs. AI is still like an CS intern, or very very junior engineer.