OpenAI claims gold-medal performance at IMO 2025
twitter.com
twitter.com
https://x.com/polynoamial/status/1946478258968531288
"When you work at a frontier lab, you usually know where frontier capabilities are months before anyone else. But this result is brand new, using recently developed techniques. It was a surprise even to many researchers at OpenAI. Today, everyone gets to see where the frontier is."
and
"This was a small team effort led by @alexwei_ . He took a research idea few believed in and used it to achieve a result fewer thought possible. This also wouldn’t be possible without years of research+engineering from many at @OpenAI and the wider AI community."
They have a parallel effort to corner Ramanujan called https://epoch.ai/frontiermath/tier-4
(& Problem 6, combinatorics, the one class of problems not yet fallen to AI?)
The hope for humanity is that of the big names associated to FrontierMath (starkly opposite to oAI proper) Daniel is the one youngish nonexsoviet guy :)
Why waste time say lot word when few word do trick :)
Also worth pointing out that Alex Wei is himself a gold medalist at IOI.
Terence Tao also called it, that the top LLMs would get gold this year in a recent podcast.
- A 3Blue1Brown video on a particularly nice and unexpectedly difficult IMO problem (2011 IMO, Q2): https://www.youtube.com/watch?v=M64HUIJFTZM
-- And another similar one (though technically Putnam, not IMO): https://www.youtube.com/watch?v=OkmNXy7er84
- Timothy Gowers (Fields Medalist and IMO perfect scorer) solving this year’s IMO problems in “real time”:
x+y=1
xy=1
The incredible thing is the explanation uses almost all reasoning steps that I am familiar with from basic algebra, like factoring, quadratic formula, etc. But it just comes together so beautifully. It gives you the impression that if you thought about it long enough, surely you would have come up with the answer, which is obviously wrong, at least in my case.
It’s as-if we had learned whale song, and then within two years a whale had won a Nobel prize for their research in high pressure aquatic environments. You’d similarly get naysayers debating the finer points of what special advantage whales may have in that particular field, neglecting the stunned shock of the general population — “Whales are publishing research papers now!? Award winning papers at that!?”
Last month, Tao himself said that we can compare humans and AIs at IMO. He even said such AI didn't exist yet and AIs won't beat IMO in 2025. And now that AIs can compete with humans at IMO under the same conditions that Tao mentioned, suddenly it becomes an apples-to-oranges comparison?
Professional mathematicians would not get this level of performance, unless they have a background in IMO themselves.
This doesn’t mean that the model is better than them in math, just that mathematicians specialize in extending the frontier of math.
The answers are not in the training data.
This is not a model specialized to IMO problems.
Basically how you do RL is that you make a set of training examples of input-output pairs, and set aside a smaller validation set, which you never train on, to check if your model's doing well.
What you do is you tweak the architecture and the training set until it does well on the validation set. By doing so, you inadvertedly leak info about the training set. Perhaps you choose an architecture which does well on the validation set. Perhaps you train more on examples more like ones being validated.
Even without the explicit intent to cheat, it's very hard to avoid this contamination, if you chose a different validation set, you'd end up with a different model.
How do you know?
> This is not a model specialized to IMO problems.
Any proof?
There are trillions of dollars at stake in hyping up these products; I take everything these companies write with a cartload of salt.
From the thread:
> just to be clear: the IMO gold LLM is an experimental research model.
The thread tried to muddy the narrative by saying the methodology can generalize, but no one is claiming the actual model is a generalized model.
There'd be a massively different conversation needed if a generalized model that could become the next iteration of ChatGPT had achieved this level of performance.
E.g here: https://pbs.twimg.com/media/GwLtrPeWIAUMDYI.png?name=orig
Frankly it looks to me like it's using an AlphaProof style system, going between natural language and Lean/etc. Of course OpenAI will not tell us any of this.
https://x.com/alexwei_/status/1946477745627934979?s=46&t=Hov...
> 5/N Besides the result itself, I am excited about our approach: We reach this capability level not via narrow, task-specific methodology, but by breaking new ground in general-purpose reinforcement learning and test-time compute scaling.
It's interesting that it didn't solve the problem that was by far the hardest for humans too. China, the #1 team got only 21/42 points on it. In most other teams nobody solved it.
Edit: Fixed P4 -> P3. Thanks.
Day 1: P1 P3 P5 (odds)
Day 2: P2 P4 P6 (evens)
Then the problem # is the difficulty.
There is no reason why machines would do badly on exactly the problem which humans do badly as well - without humans prodding the machine towards a solution.
Also, there is no reason why machines could not produce a partial or wrong answer to problem 6 which seems like survivor bias to me. ie, that only correct solutions were cherrypicked.
An internet connected machine that reasons like humans was by default considered a fraud 5 years ago; it's not unthinkable some researchers would fake it till they made it, but of course you need proof of it before making such an accusation.
Unless the machine is trained to mimic human thought process.
You know IMO questions are not all equally difficult, right? They're specifically designed to vary in difficulty. The reason that problem 6 is hard for both humans and LLM is... it's hard! What a surprise.
There are many things that are hard for AI’s for the same reason they’re hard for humans. There are subtleties in complexity that make challenging things universal.
Obviously the model was trained on human data so its competencies lie in what other humans have provided input for over the years in mathematics, but that isn’t data contamination, that’s how all humans learn. This model, like the contestants, never saw the questions before.
https://x.com/natolambert/status/1946569475396120653
OAI announced early, probably we will hear announcement from Google soon.
The key difference is that they claim to have not used any verifiers.
Big if true. Setting up an RL loop for training on math problems seems significantly easier than many other reasoning domains. Much easier to verify correctness of a proof than to verify correctness (what would this even mean?) for a short story.
There's a comment on this twitter thread saying the Google model was using Lean, while IIUC the OpenAI one was pure LLM reasoning (no tools). Anyone have any corroboration?
In a sense it's kinda irrelevant, I care much more about the concrete things AI can achieve, than the how. But at the same time it's very informative to see the limits of specific techniques expand.
> this isn’t an IMO-specific model. It’s a reasoning LLM that incorporates new experimental general-purpose techniques.
> it’s also more efficient [than o1 or o3] with its thinking. And there’s a lot of room to push the test-time compute and efficiency further.
> As fast as recent AI progress has been, I fully expect the trend to continue. Importantly, I think we’re close to AI substantially contributing to scientific discovery.
I thought progress might be slowing down, but this is clear evidence to the contrary. Not the result itself, but the claims that it is a fully general model and has a clear path to improved efficiency.
"So under his saturate response, he never loses. For her to win, must make him unable at some even -> would need Q_{even-1}>even, i.e. some a_j> sqrt2. but we just showed always a_j<=c< sqrt2. So she can never cause his loss. So against this fixed response of his, she never wins (outcomes: may be infinite or she may lose by sum if she picks badly; but no win). So she does NOT have winning strategy at λ=c. So at equality, neither player has winning strategy."[1]
Why use lot word when few word do trick?
1. https://github.com/aw31/openai-imo-2025-proofs/blob/main/pro...
A) that's not clear
B) now we have "reasoning" models that can be used to analyse the data, create n rollouts for each data piece, and "argue" for / against / neutral on every piece of data going into the model. Imagine having every page of a "short story book" + 10 best "how to write" books, and do n x n on them. Huge compute, but basically infinite data as well.
We went from "a bunch of data" to "even more data" to "basically everything we got" to "ok, maybe use a previous model to sort through everything we got and only keep quality data" to "ok, maybe we can augment some data with synthetic datasets from tools etc" to "RL goes brrr" to (point B from above) "let's mix the data with quality sources on best practices".
The progress has come from all kinds of things. Better distillation of huge models to small ones. Tool use. Synthetic data (which is not leading to model collapse like theorized). Reinforcement learning.
I don't know exactly where the progress over the next year will be coming from, but it seems hard to believe that we'll just suddenly hit a wall on all of these methods at the same time and discover no new techniques. If progress had slowed down over the last year the wall being near would be a reasonable hypothesis, but it hasn't.
The new "Full Self-Driving next year"?
Unlike seemingly most here on HN, I judge people's trustworthiness individually and not solely by the organization they belong to. Noam Brown is a well known researcher in the field and I see no reason to doubt these claims other than a vague distrust of OpenAI or big tech employees generally which I reject.
A sufficiently brilliant and determined human can invent or explain everything armed only with this knowledge.
There's no need to train him on a huge corpus of text, like they do with ChatGPT.
Not sure what this model's like, but I'm quite certain it's not trained on terabytes of Internet and book dumps, but rather is trained for abstract problem solving in some way, and is likely much smaller than these trillion parameter SOTA transformers, hence is much faster.
Wei references scaling up test-time compute, so I have to assume they threw a boatload of money at this. I've heard talk of running models in parallel and comparing results - if OpenAI ran this 10000 times in parallel and cherry-picked the best one, this is a lot less exciting.
If this is legit, then we need to know what tools were used and how the model used them. I'd bet those are the 'techniques to make them better at hard to verify tasks'.
You don’t have to be hyped to be amazed. You can retain the ability to dream while not buying into the snake oil. This is amazing no matter what ensemble of techniques used. In fact - you should be excited if we’ve started to break out of the limitations of forcing NN to be load bearing in literally everything. That’s a sign of maturing technology not of limitations.
Half the internet is convinced that LLMs are a big data cheating machine and if they're right then, yes, boldly cheating where nobody has cheated before is not that exciting.
1) did humans formalize the input 2) did humans prompt the llm towards the solution etc..
I am excited to hear about it, but I remain skeptical.
Because if I have to throw 10000 rocks to get one in the bucket, I am not as good/useful of a rock-into-bucket-thrower as someone who gets it in one shot.
People would probably not be as excited about the prospect of employing me to throw rocks for them.
Certainly the emergent behaviour is exciting but we tend to jump to conclusions as to what it implies.
This means we are far more trusting with software that lacks formal guarantees than we should be. We are used to software being sound by default but otherwise a moron that requires very precise inputs and parameters and testing to act correctly. System 2 thinking.
Now with NN it's inverted: it's a brilliant know-it-all but it bullshits a lot, and falls apart in ways we may gloss over, even with enormous resources spent on training. It's effectively incredible progress on System 1 thinking with questionable but evolving System 2 skills where we don't know the limits.
If you're not familiar with System 1 / System 2, it's googlable .
Claude Code is still only a mildly useful tool because it's horrific beyond a certain breadth of scope. If I asked it to solve the same problem 10,000 times I'm sure I'd get a great answer to significantly more difficult problems, but that doesn't help me as I'm not capable of scaling myself to checking 10,000 answers.
That entirely depends on who did the cherry picking. If the LLM had 10000 attempts and each time a human had to falsify it, this story means absolutely nothing. If the LLM itself did the cherry picking, then this is just akin to a human solving a hard problem. Attempting solutions and falsifying them until the desired result is achieved. Just that the LLM scales with compute, while humans operate only sequentially.
According to the twitter thread, the model was not given access to tools.
This is almost certainly the case, remember the initial o3 ARC benchmark? I could add this is probably multi-agent system as well, so the context length restriction can be bypassed.
Overall, AI good at math problems doesn't make news to me. It is already better than 99.99% of humans, now it is better than 99.999% of us. So ... ?
Waiting for Terry Tao's thoughts, but these kind of things are good use of AI. We need to make science progress faster rather than disrupting our economy without being ready.
> I will not be commenting on any self-reported AI competition performance results for which the methodology was not disclosed in advance of the competition.
You degraded this thread badly by posting so many comments like this.
For those following along but without math specific experience: consider whether your average CS professor could solve a top competitive programming question. Not Leetcode hard, Codeforces hard.
Every time an LLM reaches a new benchmark there’s a scramble to downplay it and move the goalposts for what should be considered impressive.
The International Math Olympiad was used by many people as an example of something that would be too difficult for LLMs. It has been a topic of discussion for some time. The fact that an LLM has achieved this level of performance is very impressive.
You’re downplaying the difficulty of these problems. It’s called international because the best in the entire world are challenged by it.
Here's an example problem 5:
Let a1,a2,…,an be distinct positive integers and let M=max1≤i<j≤n.
Find the maximum number of pairs (i,j) with 1≤i<j≤n for which (ai +aj )(aj −ai )=M.
My very rough napkin math suggests that against the US reference class, imo gold is literally a one in a million talent (very roughly 20 people who make camp could get gold out of very roughly twenty million relevant high schoolers).
I think that the number of students who are even aware of the competition is way lower than the total number of students.
I mean, I don’t think I’d have been a great competitor even if I tried. But I’m pretty sure there are a lot of students that could do well if given the opportunity.
If your school had a math team and you were on it, would be surprised if you didn't hear of it
You may not have heard of the IMO because no one in school district, possibly even state got in. It is extremely selective (like 20 students in the entire country)
And AI changes so quickly that there is a breakthrough every week.
Call my cynical, but I think this is an RLHF/RLVR push in a narrow area--IMO was chosen as a target and they hired specifically to beat this "artificial" target.
Every skill and skill level is specifically assigned and hired in the RLHF world.
Sometime the skill levels are fuzzier, but that’s usually very temporary.
And as been said already, IMO is a specific skill that even PHD math holders aren’t universally trained for.
In 2021 Paul Christiano wrote he would update from 30% to "50% chance of hard takeoff" if we saw an IMO gold by 2025.
He thought there was an 8% chance of this happening.
Eliezer Yudkowsky said "at least 16%".
Source:
https://www.lesswrong.com/posts/sWLLdG6DWJEy3CH7n/imo-challe...
We may certainly hope Eliezer's other predictions don't prove so well-calibrated.
On the other hand, I think human hubris naturally makes us dramatically overestimate how special brains are.
Not sure there is a good writeup about it yet but here is the livestream: https://www.youtube.com/live/TG3ChQH61vE.
I am just happy the prize is so big for AI that there are enough money involve to push for all the hardware advancement. Foundry, Packaging, Interconnect, Network etc, all the hardware research and tech improvements previously thought were too expensive are now in the "Shut up and take my money" scenario.
The correlation between "companies make smarter AI" and "our lives get better" is still a rounding error.
Many people will say "don't worry, tech always makes our lives better eventually", they'll probably stop saying this once autonomous killer drone-swarms are a thing.
There’s so much to do at inference time. This result could not have been achieved without the substrate of general models. Its not like Go or protein folding. You need the collective public global knowledge of society to build on. And yes, there’s enough left for ten years of exploration.
More importantly, the stakes are high. There may be zero day attacks, biological weapons, and more that could be discovered. The race is on.
If you looked at RLHF hiring over the last year, there was a huge hiring of IMO competitors to RLHF. This was a new, highly targeted, highly funded RLHF’ing.
Which is greater, 9.11 or 9.9?
I kid, this is actually pretty amazing!! I've noticed over the last several months that I've had to correct it less and less when dealing with advanced math topics so this aligns.
My proving skills are extremely rusty so I can’t look at these and validate them. They certainly are not traditional proofs though.
It reads like someone who found the correct answer but seemingly had no understanding of what they did and just handed in the draft paper.
Which seems odd, shouldn't an LLM be better at prose?
Second, happy to test it on open math conjectures or by attempting to reprove recent math results.
For 2, there's an army of independent mathematicians right now using automated theorem provers to formalise more or less all mathematics as we know it. It seems like open conjectures are chiefly bounded by a genuine lack of new tools/mathematics.
However, I expect that geometric intuition may still be lacking mostly because of the difficulty of encoding it in a form which an LLM can easily work with. After all, Chatgpt still can't draw a unicorn [1] although it seems to be getting closer.
When (not if) AI does make a major scientific discovery, we'll hear "well it's not really thinking, it just processed all human knowledge and found patterns we missed - that's basically cheating!"
https://ai.vixra.org/pdf/2506.0065v1.pdf
A satirical paper, but it's hilariously brilliant.
> It is tempting to view the capability of current AI technology as a singular quantity: either a given task X is within the ability of current tools, or it is not. However, there is in fact a very wide spread in capability (several orders of magnitude) depending on what resources and assistance gives the tool, and how one reports their results.
> One can illustrate this with a human metaphor. I will use the recently concluded International Mathematical Olympiad (IMO) as an example. Here, the format is that each country fields a team of six human contestants (high school students), led by a team leader (often a professional mathematician). Over the course of two days, each contestant is given four and a half hours on each day to solve three difficult mathematical problems, given only pen and paper. No communication between contestants (or with the team leader) during this period is permitted, although the contestants can ask the invigilators for clarification on the wording of the problems. The team leader advocates for the students in front of the IMO jury during the grading process, but is not involved in the IMO examination directly.
> The IMO is widely regarded as a highly selective measure of mathematical achievement for a high school student to be able to score well enough to receive a medal, particularly a gold medal or a perfect score; this year the threshold for the gold was 35/42, which corresponds to answering five of the six questions perfectly. Even answering one question perfectly merits an "honorable mention".
> But consider what happens to the difficulty level of the Olympiad if we alter the format in various ways:
> * One gives the students several days to complete each question, rather than four and half hours for three questions. (To stretch the metaphor somewhat, consider a sci-fi scenario in the student is still only given four and a half hours, but the team leader places the students in some sort of expensive and energy-intensive time acceleration machine in which months or even years of time pass for the students during this period.)
> * Before the exam starts, the team leader rewrites the questions in a format that the students find easier to work with.
> * The team leader gives the students unlimited access to calculators, computer algebra packages, textbooks, or the ability to search the internet.
> * The team leader has the six student team work on the same problem simultaneously, communicating with each other on their partial progress and reported dead ends.
> * The team leader gives the students prompts in the direction of favorable approaches, and intervenes if one of the students is spending too much time on a direction that they know to be unlikely to succeed.
> * Each of the six students on the team submit solutions, but the team leader selects only the "best" solution to submit to the competition, discarding the rest.
> * If none of the students on the team obtains a satisfactory solution, the team leader does not submit any solution at all, and silently withdraws from the competition without their participation ever being noted.
> In each of these formats, the submitted solutions are still technically generated by the high school contestants, rather than the team leader. However, the reported success rate of the students on the competition can be dramatically affected by such changes of format; a student or team of students who might not even reach bronze medal performance if taking the competition under standard test conditions might instead reach gold medal performance under some of the modified formats indicated above.
> So, in the absence of a controlled test methodology that was not self-selected by the competing teams, one should be wary of making apples-to-apples comparisons between the performance of various AI models on competitions such as the IMO, or between such models and the human contestants.
Source:
https://mathstodon.xyz/@tao/114881418225852441
The latest models can score something like 70% on SWE-bench verified and yet it’s difficult to say what tangible impact this has on actual software development. Likewise, they absolutely crush humans at sport programming but are unreliable software engineers on their own.
What does it really mean that an LLM got gold on this year’s IMO? What if it means pretty much nothing at all besides the simple fact that this LLM is very, very good at IMO style problems?
The proofs are correct, and it's very unlikely that IMO problems were leaked ahead of time. So the options for cheating in this circumstance are that a) IMO are colluding with a few researchers at OpenAI for some reason, or b) @alexwei_ solved the problems himself - both seem pretty unlikely to me.
[1] https://imo2025.au/wp-content/uploads/2025/07/IMO-2025_Closi...
The thing is, only leading AI companies and big tech have the money to fund these big benchmarks and run inference on them. As long as the benchmarks are somewhat publicly available and vetted by reputable scientists/mathematicians it seems reasonable to believe they're trustworthy.
- OpenAI denied training on FrontierMath, FrontierMath-derived data, or data targeting FrontierMath specifically
- The training data for o3 was frozen before OpenAI even downloaded FrontierMath
- The final o3 model was selected before OpenAI looked at o3's FrontierMath results
Primary source: https://x.com/__nmca__/status/1882563755806281986
You can of course accuse OpenAI of lying or being fraudulent, and if that's how you feel there's probably not much I can say to change your mind. One piece of evidence against this is that the primary source linked above no longer works at OpenAI, and hasn't chosen to blow the whistle on the supposed fraud. I work at OpenAI myself, training reasoning models and running evals, and I can vouch that I have no knowledge or hint of any cheating; if I did, I'd probably quit on the spot and absolutely wouldn't be writing this comment.
Totally fine not to take every company's word at face value, but imo this would be a weird conspiracy for OpenAI, with very high costs on reputation and morale.
It’s an annual human competition.
chatgpt gave me the wrong answer, it claimed 2 points of intersection, but for n=4 there are 3 as one can easily derive. one for negative x and 2 points for positive x because exp(x) is growing faster than x^4.
then i corrected it and said 3 points of intersection. it said yes and gaev me the 3 points. then i said no there are 4 points of intersection and it again explained to me that there are 2 points of intersection. which is wrong.
then i asked it how many points of intersection for n=e and it said: zero
well, exp(x)=x^e for x=e, isnt it?
GPT-5 finally on the horizon!
What? This is a claim with all the trust-worthiness of OpenAI's claim. I mean I can claim anything I want at this point and it would still be just as trust-worthy as OpenAI's claim, with exactly zero details about anything else than "we did it, promise".
Edit due to rate-limiting:
o3-pro returned an answer after 24 minutes: https://chatgpt.com/share/687bf8bf-c1b0-800b-b316-ca7dd9b009... Whether the CoT amounts to valid mathematical reasoning, I couldn't say, especially because OpenAI models tend to be very cagey with their CoT.
Gemini 2.5 Pro seems to have used more sophisticated reasoning ( https://g.co/gemini/share/c325915b5583 ) but it got a slightly different answer. Its chain of thought was unimpressive to say the least, so I'm not sure how it got its act together for the final explanation.
Claude Opus 4 appears to have missed the main solution the others found: https://claude.ai/share/3ba55811-8347-4637-a5f0-fd8790aa820b
Be interesting if someone could try Grok 4.
I don't know which one i would consider the most prestigious math competition but it wouldn't be The IMO. The Putnam ranks higher to me and I'm not even an American. But I've come to realise one thing and that is that high-school is very important to Americans...
I’ll wait to see third party verification and/or use it myself before judging. There’s a lot of incentives right now to hype things up for OpenAI.
>GPT5 soon
>it will not be as good as this secret(?) model
Nice result but it's just another game humans got beaten at. This time a game which isn't even taken very seriously (in comparison to ones that have professional scene).
I think its opposite: general public blindly trusts all kind of hyped stuff, its a very few hyper-skeptical who are some fraction of percent of population.
Edit: why was my comment moved from the one I was replying to? It makes no sense here on its own.
These models are trained on all old problem and their various solutions.For LLM models, solving thses problems are as impressive as writing code.
There is no high generalization.
We are simply greasing the grooves and letting things slide faster and faster and calling it progress. How does this help to make the human and nature integration better?
Does this improve climate or make humans adapt better to changing climate? Are the intelligent machines a burning need for the humanity today? Or is it all about business and political dominance? At what cost? What's the fall out of all this?
>Commenters on HN claim it must not be that hard, or OpenAI is lying, or cheated. Anything but admit that it is impressive
Every time on this site lol. A lot of people here have an emotional aversion to accepting AI progress. They’re deep in the bargaining/anger/denial phase.
All my life I’ve taken for granted that your value is related to your positive impact, and that the unique value of humans is that we can create and express things like no other species we’ve encountered.
Now, we have created this thing that has ripped away many of my preconceptions.
If an AI can adequately do whatever a particular person does, then is there still a purpose for that person? What can they contribute? (No I am not proposing or even considering doing anything about it).
It just makes me sad, like something special is going away from the world.
> There is a related “Theorem” about progress in AI: once some mental function is programmed, people soon cease to consider it as an essential ingredient of “real thinking”. The ineluctable core of intelligence is always in that next thing which hasn’t yet been programmed. This “Theorem” was first proposed to me by Larry Tesler, so I call it Tesler’s Theorem: “AI is whatever hasn’t been done yet.”
Given that AI companies are constantly trying to slurp up any and all data online, if the model was derived from existing work, it's maybe less impressive than at first glance. If present-day model does well at IMO 2026, that would be nice.
I've been reading this website for probably 15 years, its never been this bad. many threads are completely unreadable, all the actual educated takes are on X, its almost like there was a talent drain
You say, "explaining away the increasing performance" as though that was a good faith representation of arguments made against LLMs, or even this specific article. Questionong the self-congragulatory nature of these businesses is perfectly reasonable.
The Pro AI crowd, VC, tech CEOs etc have strong incentive to claim humans are obsolete. Many tech employees see threats to their jobs and want to poopoo any way AI could be useful or competitive.
Finding a critic perspective and try to understand why it can be wrong is more fun. You just say "I was wrong" when proved wrong.
The problem with the hype machine is that it provokes an opposite reaction and the noise from it buries any reasonable / technical discussion.
People here were pretty skeptical about AlexNet, when it won the ImageNet challenge 13 years ago.
And then, it's likewise easy to be a reactionary to the extremes of the other side.
The middle is a harder, more interesting place to be, and people who end up there aren't usually chasing money or power, but some approximation of the truth.
Usually my go-to example for LLMs doing more than mass memorization is Charton's and Lample's LLM trained on function expressions and their derivatives and which is able to go from the derivatives to the original functions and thus perform integration, but at the same time I know that LLMs are essentially completely crazy with no understanding of reality-- just ask them to write some fiction and you'll have the model outputting discussions where characters who have never met before are addressing each other by name, or getting other similarly basic things wrong, and when something genuinely is not in the model you will end up in hallucination land. So the people saying that the models are bad are not completely crazy.
With the wrong codebase I wouldn't be surprised if you need a finetune.
At this point, there are much better places to find technical discussion of AI, pros and cons. Even Reddit.
So, as much I get the frustration comments like these don't really add much. Its complaining about others complaining. Instead this should be taken as a signal that maybe HN is not the right forum to read about these topics.
AI is of course a direct attack on the average HNers identity. The response you see is like attacking a Christian on his religion.
The pattern of defense is typical. When someone’s identity gets attacked they need to defend their identity. But their defense also needs to seem rational to themselves. So they begin scaffolding a construct of arguments that in the end support their identity. They take the worst aspects of AI and form a thesis around it. And that becomes the basis of sort of building a moat around their old identity as an elite programmer genius.
Tell tale sign you or someone else is doing this is when you are talking about AI and someone just comments about how they aren’t afraid of AI taking over their own job when it wasn’t even directly the topic.
If you say like ai is going to lessen the demand for software engineering jobs the typical thing you here is “I’m not afraid of losing my job” and I’m like bro, I’m not talking about your job specifically, I’m not talking about you or your fear of losing a job I’m just talking about the economics of the job market. This is how you know it’s an identity thing more than a technical topic.
Also, the performance of LLMs on imo 2025 was not even bronze [3].
Finally, this article shows that LLMs were just mostly bluffing [4] on usamo 2025.
[1] https://www.reddit.com/r/slatestarcodex/comments/1i53ih7/fro...
X is higher signal, but very group thinky. It's great if you want to know the trends, but gotta be careful not to jump off the cliff with the lemmings.
Highest signal is obviously non digital. Going to meetups, coffee/beers with friends, working with your hands, etc.
Some of us are implementing things in relation to AI so we know it's not about "increasing performance of models" but actual about the right solution for the right problem.
If you think Twitter has "educated takes" then maybe go there and stop being pretentious schmuck over here.
Talent drain, lol. I'd much rather have skeptics and good tips than usernames, follows and social media engagement.
i.e. it is a culture of meritocracy; where no matter your social connections, political or financial capital if you are smart and driven you can make it.
AI flips that around. It devalues human intelligence and moves the moats to the ol' school things of money, influence and power. The big winners are no longer the most hard working, or above average intelligence. Intelligence is devalued; as a wealthy person I now have intelligence at my fingertips making it a commodity rather than a virtue - but money, power and connections - that's now the moat.
If all you have is your talent the future could look quite scary in an AI world long term. Money buys the best models, connections, wealth and power become the remaining moats. This doesn't gel typically in a "indie hacker" like culture in most tech forums.
As a partially separate issue, there are people trying to punish comments quoting AI by downvotes. You don't need to have a non-informative reply, just sourcing it to AI is enough. A random internet dude telling the same thing with less justification or detail is fine to them.
As hackers we have more responsibility than the general public because we understand the tech and its side effects, we are the first line of defense so it is important to speak out not only to be on the right side of history but also to protect society.
Almost every technical comment on HN is wrong (see for example essentially all the discussion of Rust async, in which people keep making up silly claims that Rust maintainers then attempt to patiently explain are wrong).
The idea that the "educated" takes are on X though... that's crazy talk.
How is it rational to 10x the budget over and over again when it mangles data every time?
The mind blowing thing is not being skeptical of that approach, it's defending it. It has become an article of faith.
It would be great to have AI chatbots. But chatbots that mangle data getting their budgets increased by orders of magnitude over and over again is just doubling down on the same mistake over and over again.
भारत दुर्दशा न देखी जाई...
We detached this subthread from https://news.ycombinator.com/item?id=44615783.
Now it is just doing a bunch of tweets?
And many other things
I wouldn't trust these results as it is. Considering that there are trillions of dollars on the line as a reward for hyping up LLMs, I trust it even less.
BTW; “Gold medal performance “ looks a promotional term for me.
any details?
> Btw, we are releasing GPT-5 soon, and we’re excited for you to try it. But just to be clear: the IMO gold LLM is an experimental research model. We don’t plan to release anything with this level of math capability for several months.
Conclusion: It is overwhelmingly likely that this document was generated by a human.
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Self-Correction/Refinement and Explicit Goals:
"Exactly forbidden directions. Good." - This self-affirmation is very human.
"Need contradiction for n>=4." - Clearly stating the goal of a sub-proof.
"So far." - A common human colloquialism in working through a problem.
"Exactly lemma. Good." - Another self-affirmation.
"So main task now: compute K_3. And also show 0,1,3 achievable all n. Then done." - This is a meta-level summary of the remaining work, typical of human problem-solving "
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