Ask HN: How should junior programmers use and/or not use AI for programming?

AI is training wheels for programming. Hint: Training wheels are actually counterproductive for learning the dynamic balance skills it takes to ride a bike.

New programmers should learn the relevant skills on their own: choosing the appropriate relevant abstractions, writing the code, testing, debugging. Maybe someday AI will be able to help talk them through the concepts and process, but I wouldn't trust today's LLMs without CLOSE human oversight. They're still just drawing refrigerator poetry out of a magic, statistically weighted bag of holding.

If you're mid-level or senior and you think "mash button, get slop" will help streamline your workflow in some mission noncritical way, go for it. Slop is convenient, and can free up time to focus on what you think is more important -- Hackernews was all in on Soylent because being able to keep your flesh mech topped up with nutrients without having to prepare food really appeals to the SV grindset crowd -- but slop shouldn't be taking on production workloads, again not without human oversight, which would require equivalent effort to just letting the humans write the damn thing themselves.

Well, there's AI for reading source now. In fact, a lot of the time I spend arguing with AI now is the implementation of things like replaceAll('{{$value}}').

If value is foo, then the intent here is {{foo}} would be replaced. But sometimes the $ is a regex character. But because it's a string, is it using the string form of replaceAll or regex form?

AI tends to be multilingual and sometimes it's thinking in the wrong language, so this is the vibe code version of 10 + 10 = 1010.

The docs can be ambiguous on this, so ideally read the source. Or heck, tell AI to read it for you, but someone has to read it, and it becomes another gotcha for engineers to understand, vibe code or not.

The spirit of the original is intact.

However, usually the bad result is not affecting immediately and in the meantime the apprentice undergoes a lot of anti-learning before this becomes apparent. And the learning muscles have atrophied.

Nice work, both to the prompter and the promptee!

LLMs will ultimately prove to be for programming what sugar is for the food industry. It’s a short circuit that appeals to some people more than others resulting in addiction, poor performance, and lost development/growth.

There is already a tremendous gap, like more than an order of magnitude, between high performers and the average participant. LLMs will only serve to grow that performance gap just like added sugars in the food supply.