[klabb3]

Disagree. There is almost no decision making in converting to use i18n APIs that already have example use cases elsewhere. Building a frontend involves many decisions, such as picking a language, build system, dependencies, etc. I’m sure the LLM would finish the task, but it could make many suboptimal decisions along the way. In my experience it also does make very different decisions from what I would have made.

[int_19h]

The AI will pick the most common technologies used for this purpose, which is both "good enough" and also what people generally do at scale (for this exact reason).

[klabb3]

This was the promise of no-code. ”All apps are crud anyway”, ”just build for common use cases” etc. This didn’t turn out to be true as often as predicted. If averaging people’s previous decisions was truly a fruitful way, we’d have seen much stronger results of that before AI.

[int_19h]

On the contrary, it turned out to be exactly as common as predicted, which is why you see so many people going "this AI assistant thing makes me 100% more productive". It's precisely those tasks. And they are handled in precisely this way by humans, too - throw whatever the popular stack of the day is at them. And sure, it results in inefficiencies and crappy code, but that code is "good enough" wrt what the customer wants it to do.