My comment was admittedly a little inflammatory, so I’ll try to elaborate on what I find so exasperating about the whole experience of using LLMs for coding (in any form, chat, agent, etc.): at root, it’s their total lack of a model of computation.

This leads to mistakes that are difficult to catch, because while I know the kinds of mistakes another human might make, having been through the process of learning not to make them myself, LLMs produce whole classes of bizarre mistakes that I have no interest in learning to catch. There is no discernible flawed mental model behind these errors—which with a human could be discussed and corrected—just an opaque stochastic process which I can tediously try to set on a better course with ‘incantations’, attempting to dial in to a better part of the training data that avoids the relevant class of error.

It’s honestly amusing how much of ‘prompt engineering’ (if it can be dignified with that term) amounts to a modern-day kind of mysticism. What better can we really do though when these models’ structure and operation is utterly opaque, on the one hand through deliberate, commercially-oriented obfuscation, and on the other because we still just cannot explain how a multi-billion parameter model works.

It’s rewarding to work with human juniors because people actually learn and improve. My learning how to coax these models into producing better than trash just is not, especially on anything either remotely novel, or in a legacy codebase that requires genuine understanding of how an existing system functions at runtime.

Once those two ends of the spectrum are ruled out, I find there’s little left that an LLM can accomplish, without necessitating a loop of prompt refinement that leaves me feeling like a worse developer for not just having thought through the problem myself, and resentful of the time wasted.

Edit: this entire thread about coercing Gemini into behaving is exactly the kind of crap I have zero interest in: https://news.ycombinator.com/item?id=44194061