I'd also love the ability to take back control over my rollouts, but with Apple's recent crackdown on remote controlled feature flags in response to fraudulent nudification apps, I'd be very careful about this.

Rumors say Apple now uses heuristics to detect feature flags in the binary. While that's not directly relevant to remotely delivered JS, I fear that bypassing App Review in this way could become a quick ticket to account termination.

Good luck with parsing that one, Apple.

I'm joking, but in seriousness, I'm not sure they would gain much from that which they couldn't already analyze from the binary.

Just last week we saw a critical CVE in BetterAuth integrations for mobile app sessions. Being able to put out a patch without being gatekept on review is just so important in a world where crazy security vulnerabilities are being disclosed daily.

[0]: https://support.google.com/googleplay/android-developer/answ...

[1]: https://developer.apple.com/app-store/review/guidelines/

[2]: https://www.sec.gov/Archives/edgar/data/1581760/000119312522...

People are already used to being able to do things like phased rollouts, automatic rollbacks, and hotfix deployments for webapps and API services. OTA updates let you do something similar for mobile.

It's against the ToS from the stores to do this for notable feature changes that impact what the user sees, but for things like fixing crashes, bugs, or applying security updates, it's a game-changer.

So I don't see any reason to stick to RN, when we can just use any OS native APIs to our heart's content and our apps automatically feel more native.

For the exact same reasons you chose in it the first place: experience with React to build a single codebase for different platforms.

If AI allows you to build your native apps and keep 2 different, growing code bases in parity without needing native dev experience, then use AI. But my point of the original comment is that those decisions are different at smaller companies than Shopify. There is no free lunch.

And that can work for a company of any size.

Abstractions are for humans.

I disagree with this. The mistake rate goes up rapidly as context grows. Current frontier models get noticeably more stupid at context sizes of only 100k tokens or so, and inference becomes slower as the context grows, leading to more rework and more token usage.

I foresee a trend of having _smaller_ code units, more modularity, more abstraction, because agent work on smaller units leads to smaller contexts and a better chance of the agent getting the change right first time. Or a higher likelihood of a lower skill, lower cost model getting the change right.

It may be domain/task/prompting dependent etc, but my experience is different. I haven't subjectively felt any degradation up to like 500-600k tokens with current Opus/Fable models. After which I usually do a manual hand-off to a fresh session anyway. I don't use context compaction though.

The first attempts I made to one shot improvements to the recompiler before doing the refactoring and test framework were basically all wrong and introduced lots of bugs, a per turn success rate in the single digit percentages. Each of the non-functional changes I made improved the performance substantially as you might expect. The real eye opener though was the refactoring into smaller units - that bumped up the success rate much more than I was expecting.

Don't get me wrong, the success rate of human changes is abysmal on these tasks, which is why the recompiler was in such a sorry state already. There are edge cases and gotchas everywhere. Opus, Astra and Gemini 4 all reason for hours on individual changes, and lesser models can churn for much longer and still fail.

Interestingly the code itself isn't really all that big - about 45000 lines of C all told.