Hacker News Reader

> in general I find GPT 5.x to actually be a huge breakthrough in terms of asserting itself when I’m wrong

That's just a different bias purposefully baked into GPT-5's engineered personality on post-training. It always tries to contradict the user, including the cases where it's confidently wrong, and keeps justifying the wrong result in a funny manner if pressed or argued with (as in, it would have never made that obvious mistake if it wasn't bickering with the user). GPT-5.0 in particular was extremely strongly finetuned to do this. And in longer replies or multiturn convos, it falls into a loop on contradictory behavior far too easily. This is no better than sycophancy. LLMs need an order of magnitude better nuance/calibration/training, this requires human involvement and scales poorly.

Fundamental LLM phenomena (ICL, repetition, serial position biases, consequences of RL-based reasoning etc) haven't really changed, and they're worth studying for a layman to get some intuition. However, they vary a lot model to model due to subtle architectural and training differences, and impossible to keep up because there are so many models and so few benchmarks that measure these phenomena.

By the time I switched to GPT 5 we were already on 5.1, so I can't speak to 5.0. All I can say is that if the answer came down to something like "push the bias in the other direction and hope we land in the right spot"... well, I think they landed somewhere pretty good.

Don't get me wrong, I get a little tired of it ending turns with "if you want me to do X, say the word." But usually X is actually a good or at least reasonable suggestion, so I generally forgive it for that.

To your larger point: I get that a lot of this comes down to choices made about fine tuning and can be easily manipulated. But to me that's fine. I care more about if the resulting model is useful to me than I do about how they got there.

I find both are useful.

Claude is my loyal assistant who tries its best to do what I tell it to.

GPT-5 is the egotistical coworker who loves to argue and point out what I'm doing wrong. Sometimes it's right, sometimes it's confidently wrong. It's useful to be told I'm wrong even when I'm not. But I'm not letting it modify my code, it can look but not touch.

> That's just a different bias purposefully baked into GPT-5's engineered personality on post-training.

I want to highlight this realization! Just because a model says something cool, it doesn't mean it's an emergent behavior/realization, but more likely post-training.

My recent experience with claude code cli was exactly this.

It was so hyped here and elsewhere I gave it a try and I'd say it's almost arrogant/petulant.

When I pointed out bugs in long sessions it tried to gaslight me that everything was alright, faked tests to prove his point.

By the time GPT 5.5 landed we were already on 5.1, honestly they seem to converge on similar limitations around compositional reasoning.

"Still on Claude Code" is a funny statement, given that the industry is agreeing that Anthropic has the lead in software generation while others (OpenAI) are lagging behind or have significant quality issues (Google) in their tooling (not the models). And Anthropic frontier models are generally "You're absolutely right - I apologize. I need to ..." everytime they fuck something up.

Why is it every time anyone has a critique someone has to say “oh but you aren’t using model X, which clearly never has this problem and is far better”?

Yet the data doesn’t show all that much difference between SOTA models. So I have a hard time believing it.

GP here: My problem with a lot of studies and data is that they seem to measure how good LLMs are at a particular task, but often don't account for "how good the LLM is to work with". The latter feels extremely difficult to quantify, but matters a lot when you're having a couple dozen turns of conversation with an LLM over the course of a project.

Like, I think there's definitely value in prompting a dozen LLMs with a detailed description of a CMS you want built with 12 specific features, a unit testing suite and mobile support, and then timing them to see how long they take and grading their results. But that's not how most developers use an LLM in practice.

Until LLMs become reliable one-shot machines, the thing I care most about is how well they augment my problem solving process as I work through a problem with them. I have no earthly idea of how to measure that, and I'm highly skeptical of anyone who claims they do. In the absence of empirical evidence we have to fall back on intuition.

A friend recommended to me having a D&D style roleplay with some different engines, to see which you vibe with. I thought this sounded crazy but I took their advice.

I found this worked suprisingly well, I was certain 'claude' was best, while they like grok and someone else liked ChatGPT. Some AIs just end up fitting best with how you like to chat I think. I do definately also find claude best for coding with as well.

Because they are getting better. They're still far from perfect/AGI/ASI, but when was the last time you saw the word "delve"? So the models are clearly changing, the question is why doesn't the data show That they're actually better?

Thing is, everyone knows the benchmarks are being gamed. Exactly how is besides the point. In practice, anecdotally, Opus 4.5 is noticably better than 4, and GPT 5.2 has also noticably improved. So maybe the real question is why do you believe this data when it seems at odds with observations by humans in the field?

> Jeff Bezos: When the data and the anecdotes disagree, the anecdotes are usually right.

https://articles.data.blog/2024/03/30/jeff-bezos-when-the-da...

"They don't use delve anymore" is not really a testament that they became better.

Most of what I can do now with them I could do half a year to a year ago. And all the mistakes and fail loops are still there, across all models.

What changed is the number of magical incantations we throw at these models in the form of "skills" and "plugins" and "tools" hoping that this will solve the issue at hand before the context window overflows.

"They dont say X as often anymore" is just a distraction, it has nothing to do with actual capability of the model.

Unfortunately, I think that the overlap between actual model improvements and what people perceive as "better" is quite small. Combine this with the fact that most people desperately want to have a strong opinion on stuff even though the factual basis is very weak.. "But I can SEE it is X now".

The type of person who outsources their thinking to their social media feed news stories and isn't intellectually curious enough to deeply explore the models themselves in order for the models to display their increase in strength, isn't going to be able to tell this themselves.

I would think this also correlates with the type of person who hasn't done enough data analysis themselves to understand all the lies and misleading half-truths "data" often tells. In the reverse also, that experience with data inoculates one to some degree against the bullshitting LLM so it is probably easier to get value from the model.

I would imagine there are all kinds of factors like this that multiple so some people are really having vastly different experiences with the models than others.

Because the answer to the question, “Does this model work for my use case?” is subjective.

People desperately want 'the plateau' to be true because it means our jobs would be safe and we could call ourselves experts again. If the ground is keep moving then no one is truly an expert. There is just no enough time to achieve expertise when the paradigm shifts every six months.

That statement is only true if you're ignoring higher order patterns. I called the orchestration trend and the analytic hurdle trends back in April of last year.

Claude is still just like that once you’re deep enough in the valley of the conversation. not exactly that phrase but things like that’s the smoking gun or so. nothing has changed.

> Claude is still just like that once you’re deep enough in the valley of the conversation

My experience is claude (but probably other models as well) indeed resort to all sorts of hacks once the conversation has gone for too long.

Not sure if it's an emergent behavior or something done in later stages of training to prevent it from wasting too many tokens when things are clearly not going well.

> I haven’t heard an LLM say anything like that since October, and in general I find GPT 5.x

It said precisely that to me 3 or 4 days ago when I questioned its labelling of algebraic terms (even though it was actually correct).

I don't see a reason to think we're not going to hit a plateua sooner or later (and probably sooner). You can't scale your way out of hallucinations, and you can't keep raising tens of billions to train these things without investors wanting a return. Once you use up the entire internets worth of stack overflow responses and public github repositories you run into the fact that these things aren't good at doing things outside their training dataset.

Long story short, predicting perpetual growth is also a trap.

> You can't scale your way out of hallucinations

You scale your way only out in verifiable domains, like code, math, optimizations, games and simulations. In all the other domains the AI developers still got billions (trillions) of tokens daily, which are validated by follow up messages, minutes or even days later. If you can study longitudinally you can get feedback signals, such as when people apply the LLM idea in practice and came back to iterate later.

> Once you use up the entire internets worth of stack overflow responses and public github repositories you run into the fact that these things aren't good at doing things outside their training dataset.

I think the models have reached that human training data limitation a few generations ago, yet they stil clearly improve by various other techniques.

On balance, there’s far more evidence to support the conclusion that language models have reached a plateau.

I’m not sure I agree, it doesn’t feel like we’re getting super linear growth year over year, but Claude opus 4.5 is able to do useful work over meaningful timescales without supervision. Is the code perfect? No, but that was certainly not true of model generations a year or two ago.

To me this seems like a classic LLM defense.

A doesn't work. You must frontier model 4.

A works on 4, but B doesn't work on 4. You doing it wrong, you must use frontier model 5.

Ok, now I use 5, A and B work, but C doesn't work. Fool, you must use frontier model 6.

Ok, I'm on 6, but now A is not working as it good as it did on A. Only fools are still trying to do A.

Opus 4.5 seems to be better than GPT 5.2 or 5.2 Codex at using tools and working for long stretches on complex tasks.

I agree with a lot of what you've said, but I completely disagree that LLM's are no longer sycophantic. GPT-5 is definitely still very sycophantic, 'You're absolutely right!' still happens, etc. It's true it happens far less in a pure coding context (Claude Code / Codex) but I suspect only because of the system prompts, and those tools are by far in the minority of LLM usage.

I think it's enlightening to open up ChatGPT on the web with no custom instructions and just send a regular request and see the way it responds.