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Comment by n4r9 | Hacker News Reader

The reason stack overflow is such a useful resource is that people actually ask questions on it and receive high quality answers. I wonder, how long until the website becomes obsolete because people are asking LLMs instead, and how long after that do LLMs themselves consequently become less useful?

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How long? It started months ago.

That's the fundamental reason why there was such a falling out between Stack Overflow executives and the moderators.

People will say it was just the way they handled the interactions, which was part of it, but I don't believe there was any way to do it in a way that the users wouldn't hate.

Because the basic task for Stack Overflow IS to replace the users answering the questions with AI. They don't have a choice. Because now they are already competing with ChatGPT etc. doing exactly that.

It's true that taking away human answers from Stack Overflow will make it impossible for AI to answer many of those types of questions. But it will still retain fundamental knowledge and skills from other sources. And can be trained on its own debugging experiences as it gets deployed for building software. So although that presents a challenge, it's not insurmountable at all.

> The basic task for Stack Overflow IS to replace the users answering the questions with AI. They don't have a choice.

By going in that direction, I feel like they put the nail in the coffin. Why would I use their website when I can just ask ChatGPT directly?

I think they had one other option, which was to identify the fundamental limitations of LLMs, and find a way to demonstrate value over and above.

People were already just asking ChatGPT.

I don't believe there is any such fundamental limitation, especially when you look say two years down the road and don't limit yourself to language-only models or the AI models of today. Already GPT-4 has audio and image input.

Good human programmers today have better abilities and more current knowledge overall than GPT-4. But it's a matter of degree, and a matter of time.

What types of limitations do you suppose? And again, we have to anticipate that these technologies evolve rapidly. LLM is a misleading term in my opinion for a fundamentally multimodal model. And totally new types of models or model integrations will be likely be coming out over the next few years that will make current systems obsolete. Such as combining AlphaZero-type systems with language models.

> What types of limitations do you suppose?

I don't see the capacity for LLMs to perform novel and creative reasoning tasks. They're limited by virtue of being a pattern-matching system without the ability to make genuine causal or logical inferences.

I wrote here about my recent experiments with ChatGPT - https://news.ycombinator.com/item?id=37583333

I'm vaugely interested to know how GPT4 deals with questions like these, but I'm not willing to pay to find out.

Lol. So even the dumb version actually did your task, just not in a very convincing or "genuine" way. Pattern matching can be used for inference and GPT-4 does it very effectively. 3.5 turbo is relatively brittle and maybe 20 IQ points lower. But still, it did your task.

Anyway, it will only be a year or two before things like tree search over problems spaces are explicitly integrated into models or systems similar to this. Deep Mind may have already done something like that.

Plus where will chatgpt take new stuff from?

Whatever is new will probably be designed for chatgpt to understand first. Maybe people won't bother using your tool if it can't be used without a AI chat interface helping. The same way I won't learn a programming language if the documentation is bad. Tool makers knowing this will make chatgpt specific technical documents that can be easily parsed by an LLM.

Stackoverflow is about expert programmer answering questions from novice programmers (it can be novice on a specific tech, not necessarily junior devs). How will chatgpt learn from expert programmers, who don't need chatgpt to code something up.

My theory is that experts will be hired to train specific models. Documentation will be written to be understood my an LLM chat interface. Edge cases and tutorials will be pre-made and then fed to the model. Technical writing might be skill that will be in huge demand.

> that people actually ask questions on it and receive high quality answers

That really hasn't been my experience with StackOverflow for the last 10 years.

Fair enough. Regardless, as I understand it LLMs can only be as good as their training data. Therefore they are vulnerable to that data losing its freshness.

Wouldn't it seem reasonable to assume that any new technology will train an LLM with extensive documentation and tutorials before being released. GPT4 can browse the web now too and open source LLMa can be trained on custom data.

That is reasonable and I believe it would greatly help. However, docs and tutorials rarely anticipate all the ways in which people need to use that technology.

But LLMs are getting the "upvote" as feedback...more the usage, more they improve as well?

I don't know how LLM upvotes work, but I'm guessing it's typically only the direct user that will upvote or downvote. The value of a public forum voting system is that a pool of experienced users can verify whether an answer is of good quality.

If an inexperienced engineer asks for code and the LLM produces something that has sub-par performance, is overly complex, and botches various edge-cases, the engineer may not realise until years later if at all that they could have produced something much better.

Combined with the fact that there are plenty of things that mostly only junior engineers will ask about, and I think you have a problem.

LLMs can read the docs too

That helps but may not be not enough imo. You can follow the docs to the tee while still producing overly complex, badly performing code which botches many edge cases.

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