The AI coding trap

The difference is that there is no point (I know or would encounter) in which a compiler would not actually be able to do the job, and I would need to write manual assembly to fix some parts that the compiler could not compile. Yes a proficient programmer could probably do that to optimise the code, but the code would run and do the job regardless. That is not the case for LLMs, there is a non-zero changeyou get to the point of LLM agents getting stuck and it makes more sense to get hands dirty than iterating with agents.

The other thing that makes a big difference is using a language that has strong typing and a strict compiler. You can allow claude to call the complier, analysers, debuggers and so forth and it works amazing to keep code quality consistent if you tell it always follow your existing patterns.

Lastly, I figured out recently, quite by accident, that claude can call adb/logcat on a connected android device, to ingest real debug logs while my app is running, which can be a firehose of logs. It cuts through it like candyfloss and spots runtime errors significantly faster than a human can read all those log. It found a bug for me that was nearly impossible to find with the IDE & breakpoints, just by ingesting the debug/logcats outputs directly.

So overall the article is great, I do think the way we work can and will fundamentally change in the coming years.

Current AI models, especially small ones, are quite smooth-brain by themselves.

But, if we can refine them to a point where thinking AI systems can run on smart glasses and smartwatches in a usable, stable state, we can assume by then larger AI systems with higher compute are bound to be much more capable.

We can claim AGI milestone then. And, the real race for superintelligence begins.

Why would I want a fuzzy, vague, imprecise, up-to-interpretation programming language? I already have to struggle with that in documentation, specifications, peers and – of course – myself. Why would I take the one precise component and make it suffer from the same?

This contrasts of course with tasks such as search, where I'm not quite able to precisely express what I want. Here I find LLMs to be a fantastic advance. Same for e.g. operations between imprecise domains, like between natural languages.

Does this divide between "physical" and "mental" exist? Programming languages are formal languages that allow you to precisely and unambiguously express your ideas. I would say that "fiddling" with the code (as you say) is a kind of mental activity.

If there is actually someone out there that only dislikes AI coding assistants because they enjoy the physical act of typing and now have to do less of it (I have not seen this blog post yet), then I might understand your point.