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Comment by tayo42 | Hacker News Reader

> don't know how more people don't experience this, but staring at an llms output be confidently wrong eventually to get there in the process, or looking at the commands its running and see some really stupid things its doing drives me crazy to the point i will just stop it before it answers, i cannot handle something being that confidently wrong (even though it'll probably still get there in the end)

They do? Internet is filled with people complaining about it

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> i cannot handle something being that confidently wrong

I don’t know how these people have stayed so oblivious to it happening all around them for decades. LLMs didn’t invent it.

My work pays me the same if I kick off a Codex session and go practice guitar. My workflow now is like, prompt LLM, answer some emails or review some code, get a coffee, see what it barfed out.

Must be nice to not have been forced back into the office. Might as well watch paint dry on the walls while I wait for the LLM barf.

I've seen people in the office watching live streams while coding. It seems fine to me if the work is getting done, I like some background noise anyways.

Except in that case, your employer eventually catches up with that and forces you to supervise 10x agents, as you're gradually RLHFing your complete replacement.

Second order effects exist!

I don't know who these people are who are supervising 10 agents, but to me there are diminishing returns. There are people on my team who are producing more PRs than me but they're honestly a huge pain in the ass because half of them are nonsense.

The point isn't that. I personally find all LLM generated code a devil's bargain.

The point is by uncritically accepting this "technology" and not standing up to put any limits around its use, you're at best turning your job into a stressful data-labeling gig, and at worst helping your boss finally replace you.

> (even though it'll probably still get there in the end)

Burning up tokens/credits needlessly as it does so. That might be a non-issue for people using LLMs through their work but as someone doing AI on a limited budget that's an issue that drives me up a tree!

Two local models I've tried were dead sure that "How Much Is the Fish" was a 1956 song by Dave Brubeck. When asked why they thought so, both just kinda shrugged. At least that was sort of free but seriously — that's the basic knowledge recall scenario, how did it get this garbled? What else gets so horrendously garbled? How can we even know?

i highly recommend asking for terse or concise responses in whatever your chosen LLM calls its system prompt. you can give a brief explanation of your areas of expertise and say that you don't need those things explained to you. there are limits to how much this stuff works, but it's worth a shot

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