There’s been infinitely times where I was stuck at some problem and every single solution was complex, messy, over-engineered and somehow wrong, until I went on a walk or moved to a different issue and suddenly it would hit me that I was looking at it all wrong, and there’s a simple solution there but my tunnel vision didn’t let me see it. LLM rob you of that, everything is instant, there’s no time to reflect, there’s no time to realise it’s a dead end, or that it won’t work with the next problem on your todo list.

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lordnacho 2 hours ago | parent | next [-]

I would say that the mechanism for this is actually due to the nature of working with LLMs: they free your attention.

Say you're working on a project, and it does an ok job like what the GP says. Each step looks fine, but put together it looks off. You give it some instructions, and then you let it work.

But what are you doing in the freed up time? You context switch into another project and give that some instructions.

Now you have two or three or ten projects that superficially look fine, but you yourself have context switched so much you don't have the overview anymore of why exactly each project needs fixing.

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mmcnl 3 hours ago | parent | prev | next [-]

I recognize this, LLMs never stop, they pursue the dead end at all costs.

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cyanydeez 2 hours ago | parent [-]

some of the problems ya'llk encountering would be solved with dumber and slower local models.

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x______________ 2 hours ago | parent [-]

Some of the problems I solved after a few attempts where the llm couldn't solve the issue was by dumbing down the prompts.. ...

	▲	ab71e5 2 hours ago \| parent \| next [-]
		Grok why program no work? Make work please
	▲	cyanydeez 37 minutes ago \| parent \| prev [-]
		I've been successfully using local LLMs for the past few months on equipment ranging from AMD 395+ strix halo, to dual nvidia gpus with 96GB ampere, to 24GB dual 3060, etc. They all produce text gen at almost readable rates. You can read the thinking traces, the editting of code in the opencode harness, etc. Along that same use, I've never touched any cloud service for code gen, and looked at docs or arena LLM. From my POV, it seems like a lot of people arn't even paying attention to the LLMs output when they generate their projects. So I don't find it hard to believe there's seemingly intelligent people being swindled by LLMs, big or small, because they do make bizarre assumptions and go into code edits that don't make sense even when they appeared to be running. Then there's people that say they're in the ballpark of 500k context before they even get to work on something where these local models struggle when they get up to 100k both from generating and keeping scope on what they're doing. This is managed by opencode plugin dynamic-context-pruning that has the LLM rewrite sections of the context into summaries, while keeping the working context. It works pretty wel where a session then gets into ~500k where the working increments from 30k-80k of useful life. So, I think we all underestimate how easily people can be swindled into poor development behaviors and _one_ reason, is the large cloud models just dump way more changes along a scope and can easily poison a whole chain of a feature or app implementation. So the OP above who got 3 months into an app they think is hitting a dead end is recognizable as having one idea in their head of what's being built and the LLM building something subtly different because they're inevitably only reviewing the things that confirm the specs in their head and don't look for the edge cases that break it.

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lukan 3 hours ago | parent | prev [-]

That's a choice (unless your management thinks otherwise). I don't need my agents running non stop. I tell them to start running once my walk is completed and I have a clear vision of what I want them to do.