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Comment by abm53 | Hacker News Reader

And I would go further… these “common tasks” cover 80% of the work in even the most demanding engineering or research positions.

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That’s absolutely not my experience. I struggle to find tasks in my day to day work where LLMs are saving me time. One reason is that the systems and domains I work with are hardly represented at all on the internet.

I have the same experience. I'm in gamesdev and we've been encouraged to test out LLM tooling. Most of us at/above the senior level report the same experience: it sucks, it doesn't grasp the broader context of the systems that these problems exist inside of, even when you prompt it as best as you can, and it makes a lot of wild assed, incorrect assumptions about what it doesn't know and which are often hard to detect.

But it's also utterly failed to handle mundane tasks, like porting legacy code from one language and ecosystem to another, which is frankly surprising to me because I'd have assumed it would be perfectly suited for that task.

In my experience, AI for coding is having a rather stupid very junior dev at your beck and call but who can produce the results instantly. It's just often very mediocre and getting it fixed often takes longer than writing it on your own.

My experience is that it varies a lot by model, dev, and field — I've seen juniors (and indeed people with a decade of experience) keeping thousands of lines of unused code around for reference, or not understanding how optionals work, or leaving the FAQ full of placeholder values in English when the app is only on the German market, and so on. Good LLMs don't make those mistakes.

But the worst LLMs? One of my personal tests is "write Tetris as a web app", and the worst local LLM I've tried, started bad and then half way through switched to "write a toy ML project in python".

I think this illustrates the biggest failure mode when people start using LLMs: asking it to do too much in one step.

It’s a very useful tool, not magic.

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