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Reflections on AI at the End of 2025

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Re: Reflections on AI at the End of 2025

#211

Post by heliumtera »

AYBABTME wrote: post #169
candiddevmike wrote: post #158

Earlier quoted context omitted.

Skeptic here: I do think LLMs are a fad for software development . They're an interesting phenomen that people have convinced themselves MUST BE USEFUL in the context of software development, either through ignorance or a sense of desperation. I do not believe LLMs will be used long term for any kind of serious software development use cases, as the maintenance cost of the code they produce will run development teams…

In this year of 2025, in December, I find it untenable for anyone to hold this position unless they have not yet given LLMs a good enough try. They're undeniably useful in software development, particularly on tasks that are amenable to structured software development methodologies. I've fixed countless bugs in a tiny fraction of the time, entirely accelerated by the use of LLM agents. I get the most reliable results…

"high quality results". Yeah, sure. Then I wanted to check this high quality stuff by myself, it feels way worse than the overall experience in 2020. Or even 2024.

Go to docs, fast page load. Than blank, wait a full second, page loads again. This does not feel like high quality. You think it does because LLM go brrrrrrrr, never complains, says your smart. The resulting product is frustrating.

Re: Reflections on AI at the End of 2025

#212

Post by mvkel »

HarHarVeryFunny wrote: post #138
mvkel wrote: post #130

Earlier quoted context omitted.

One only has to go read the original vibe coding thread[0] from ...ten months ago(!) to see the resistance and skepticism loud and clear. The very first comment couldn't be more loud about it. It was possible to create things in gpt-3.5. The difference now is it aligns with the -taste- of discerning programmers, which has a little, but not everything, to do with technological capability. [0] https://news.ycombinator.…

"Look Ma, no hands!" vibe coding, as described by Karpathy, where you never look at the code being generated, was never a good idea, and still isn't. Some people are now misusing "vibe coding" to describe any use of LLMs for coding, but there is a world of difference between using LLMs in an intelligent considered way as part of the software development process, and taking a hit on the bong and "vibe coding" another…

Karpathy himself has used "vibe coding" to describe "usage of LLMs for coding," so it's fair to say the definition has expanded.

https://karpathy.bearblog.dev/year-in-review-2025/

Re: Reflections on AI at the End of 2025

#213

Post by heliumtera »

emp17344 wrote: post #144
mjr00 wrote: post #135

Earlier quoted context omitted.

> No, that doesn’t make you a skeptic in this context. That's good to hear, but I have been called an AI skeptic a lot on hn, so not everyone agrees with you! I agree though, there's a certain class of "AI denialism" which pretends that LLMs don't do anything useful, which in almost-2026 is pretty hard to argue.

On the other hand, ever since LLMs came on the scene, there’s been a vocal group claiming that AI will become intelligent and rapidly bring about human extinction - think the r/singularity crowd. This seems just as untenable a position to hold at this point. It’s becoming clear that these things are simply tools. Useful in many cases, but that’s it.

Lots of money to be made and power to be grabed on this safety and alignment moat.

Re: Reflections on AI at the End of 2025

#214

Post by pixl97 »

etra0 wrote: post #165
santadays wrote: post #141

Earlier quoted context omitted.

I get this take, but given the state of the world (the US anyways), I find it hard to trust anyone with any kind of profit motive. I feel like any information can’t be taken as fact, it can just be rolled into your world view and discarded if useful or not. If you need to make a decision that can’t be backed out of that has real world consequences I think/hope most people are learning to do as much due diligence as r…

> I find it hard to trust anyone with any kind of profit motive. As much as this is true, and i.e. doctors for sure can profit (here in my country they don't get any type of sponsor money AFAIK, other than having very high rates), there is still accountability. We have built a society based on rules and laws, if someone does something that can harm you, you can follow the path to at least hold someone accountable (or…

>there is still accountability

I mean there is some if they go wildly off the rails, but in general if the doctor gives a prognosis based on a tiny amount of the total corpus of evidence they are covered. Works well if you have the common issue, but can quickly go wrong if you have the uncommon one.

Re: Reflections on AI at the End of 2025

#215

Post by jcelerier »

mjr00 wrote: post #151
nl wrote: post #129

Earlier quoted context omitted.

There is some limited truth in this but we still see claims that LLMs are "just next token predictors" and "just regurgitate code they read online". These are just uninformed and wrong views. It's fair to say that these people were (are!) wrong.

> we still see claims that LLMs are "just next token predictors" and "just regurgitate code they read online". These are just uninformed and wrong views. It's fair to say that these people were (are!) wrong. I don't think it's fair to say that at all. How are LLMs not statistical models that predict tokens? It's a big oversimplification but it doesn't seem wrong , the same way that "computers are electricity running…

> How are LLMs not statistical models that predict tokens?

there's LLMs as in "the blob of coefficients and graph operations that runs on a gpu whenever there's an inference" which is absolutely "a statistical model that predict tokens" and LLMs as in "the online apps that iterates and have access to an entire automated linux environment that can run $LANGUAGE scripts and do web queries when an intermediary statistical output contains too much maybes and use the result to drive further inference.".

Re: Reflections on AI at the End of 2025

#216

Post by piker »

NitpickLawyer wrote: post #198
piker wrote: post #190

Earlier quoted context omitted.

The second example has nothing to do with the first. I am optimistic that LLMs are great for translations with good testing frameworks. “Optimize” in a vacuum is a tarpit for an LLM agent today, in my view. The Google case is interesting but 1% while significant at Google scale doesn’t move the needle much in terms of statistical significance. It would be more interesting to see the exact operation and the speed up a…

Why do you think it's not relevant to the "optimise in a loop" thing? The way I think of it, it's using LLMs "in a loop" to move something from arch A (that costs x$) to arch B (that costs y$), where y is cheaper than x. It's still an autonomous optimisation done by LLMs, no?

Did the LLM suggest moving to the new architecture? If not that’s not what’s under discussion. That’s just following an order to translate.

Re: Reflections on AI at the End of 2025

#217

Post by timmytokyo »

XorNot wrote: post #30
ajoseps wrote: post #15

Earlier quoted context omitted.

he’s not a “random internet person”, he created Redis. Despite that, I don’t know how authoritative of a figure he is with respect to AI research. He’s definitely a prolific programmer though.

There are plenty of Nobel laureates who well, do rest on their laurels and dive deep into pseudoscience after that. Accomplishment in one field does not make one an expert, nor even particularly worth listening to, in any other. Certainly it doesn't remove the burden of proof or necessity to make an actual argument based on more then simply insisting something is true.

Not sure why you're being downvoted. It's such a common phenomenon that it has its own name: Nobelitis.

[0] https://en.wikipedia.org/wiki/Nobel_disease

Re: Reflections on AI at the End of 2025

#218

Post by HarHarVeryFunny »

zahlman wrote: post #188
HarHarVeryFunny wrote: post #173

Earlier quoted context omitted.

RLVR "reinforcement learning for verifiable rewards" refers to RL used to encourage reasoning towards achieving long-horizon goals in areas such as math and programming, where the correctness/desirability of a generated response (or perhaps an individual reasoning step) can be verified in some way. For example generated code can be verified by compiling and running it, or math results verified by comparing to known c…

> The difficulty of using RL more generally to promote reasoning is that in the general case it's hard to define correctness and therefore quantify a reward for the RL training to use. Ah, hence the "HF" angle.

RLHF really has a different goal - it's not about rewarding/encouraging reasoning, but rather rewarding outputs that match human preferences for whatever reason (responses that are more on-point, or politer, or longer form, etc, etc).

The way RLHF works is that a smallish amount of feedback data of A/B preferences from actual humans is used to train a preference model, and this preference model is then used to generate RL rewards for the actual RLHF training.

RLHF has been around for a while and is what tamed base models like GPT 3 into GPT 3.5 that was used for the initial ChatGPT, making it behave in more of an acceptable way!

RLVR is much more recent, the basis of the models that do great at math and programming. If you talk about reasoning models being RL trained then it's normally going to imply RLVR, but it seems there's a recent trend of people calling it RLVR to be more explcit.

Re: Reflections on AI at the End of 2025

#219

Post by timmytokyo »

lm28469 wrote: post #61
dkdcio wrote: post #25

Earlier quoted context omitted.

fear mongering science fiction, you may as well cite Dune or Terminator

Lesswrong looks like a forum full of terminally online neckbeards who discovered philosophy 48 hours ago, you can dismiss most of what you read there don't worry

If only they had discovered philosophy. Instead they NIH their own philosophy, falling into the same ditches real philosophers climbed out of centuries ago.

Re: Reflections on AI at the End of 2025

#220

Post by NitpickLawyer »

piker wrote: post #216
NitpickLawyer wrote: post #198

Earlier quoted context omitted.

Why do you think it's not relevant to the "optimise in a loop" thing? The way I think of it, it's using LLMs "in a loop" to move something from arch A (that costs x$) to arch B (that costs y$), where y is cheaper than x. It's still an autonomous optimisation done by LLMs, no?

Did the LLM suggest moving to the new architecture? If not that’s not what’s under discussion. That’s just following an order to translate.

Ah, I see your point.
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