demirbey05

From Levent Alpöge : https://x.com/__alpoge__/status/2097383870773748190?s=20

>so far the proof looks more along the lines of another euler blowup proof we had, off of whose ansatz naming we were making really stupid puns like “smooth criminale”, unlike the much better “ideal fluids explode”, Tristan

There are too many ambiguities around OpenAI. Unanswered questions making this ambiguity more.

Why they didn't properly explain to Tristan about usage of their data.

Yes same thoughts. I dont know anyone who are both enthusiastic about those and work for salary. If you dont have any financial concern, this is really great.

We are talking that hundred millions of people will switch their jobs, how you will keep your value or earning as personal trainer. It's not easy to say switch the job. This question must be answered by politicians not us.

I have always been saying: don't be optimistic about AI. Software people prepared their end with this optimism without knowing how the economy works. "AI will make our jobs easier, I will have more time with my family," blah blah. Now we will see more news like that in the coming years. I am okay with supporting AI for curing cancer, but I am against supporting AI for disrupting the white-collar economy without a proper government plan.

It's showcase more than being take home assignment. I couldnt understand what the task is ,only performance comparisons between their LLM

https://news.ycombinator.com/item?id=46550836 Another view on that.

It's hard to cut through the AI hype when there are billions of dollars at stake. I usually trust negative comments more, as long as the person isn't trying to sell a course. Even though Terence Tao is a respected scientist, I wonder if his recent comments are driven by a need for funding due to federal cuts. I’ve had similar experiences with LLMs—whenever I ask them about hard math or RL theory, they almost always give me the wrong answers.

I am not mathematician, so it's hard to understand the comment. Thank you for your warning. I will be more careful.

I read how GPT-5 contributed to proof. It is not fully solved by GPT-5 instead assisted. For more look here https://www.math.columbia.edu/~msawhney/Problem_848.pdf

This is response from mathematician: "This is quite something, congratulations to Boris and Aristotle!

On one hand, as the nice sketch provided below by tsaf confirms, the final proof is quite simple and elementary - indeed, if one was given this problem in a maths competition (so therefore expected a short simple solution existed) I'd guess that something like the below would be produced. On the other hand, if something like this worked, then surely the combined talents of Burr, Erdős, Graham, and Li would have spotted it.

Normally, this would make me suspicious of this short proof, in that there is overlooked subtlety. But (a) I can't see any and (b) the proof has been formalised in Lean, so clearly it just works!

Perhaps this shows what the real issue in the [BEGL96] conjecture is - namely the removal of 1 and the addition of the necessary gcd condition. (And perhaps at least some subset of the authors were aware of this argument for the easier version allowing 1, but this was overlooked later by Erdős in [Er97] and [Er97e], although if they were aware then one would hope they'd have included this in the paper as a remark.)

At the moment I'm minded to keep this as open, and add the gcd condition in the main statement, and note in the remarks that the easier (?) version allowing 1 and omitting the gcd condition, which was also asked independently by Erdős, has been solved."

The commentator is saying: "I can't believe this famous problem was solved so easily. I would have thought it was a fake proof, but the computer verified it. It turns out the solution works because it addresses a slightly different set of constraints (regarding the number 1) than what Erdős originally struggled with. (Generated by Gemini)

I started fully coding with Claude Code. It's not just vibe coding, but rather AI-assisted coding. I've noticed there's a considerable decrease in my understanding of the whole codebase, even though I'm the only one who has been coding this codebase for 2 years. I'm struggling to answer my colleagues' questions.

I am not defending we should drop AI, but we should really measure its effects and take actions accordingly. It's more than just getting more productivity.

LLMs can do small things well, but you must use small parts to form big picture.

it is not that easy, there is lazy loading in the page that is triggered by scroll of specific sections. You need to find clever way, no way to scrape with bs4, so tough with even selenium.

Yesterday, I was asked to scrape data from a website. My friend used ChatGPT to scrape data but didn't succeded even spent 3h+. I looked website code and understand with my web knowledge and do some research with LLM. Then I described how to scrape data to LLM it took 30 minutes overall. The LLM cant create best way but you can create with using LLM. Everything is same, at the end of the day you need someone who can really think.

I mean test-time scaling coming to end, there are many open rooms for next thing.

test time scaling is based on methods from pre-2020. If you look details of modern LLMs its pretty small prob to encounter method from 2020+(ROPE,GRPO). I am not saying IMO is not impressive, but it is not breakthrough, if they said they used different paradigm then test-time scaling I would say breakthrough.

> We are not close to solving IMO with publicly known methods. The point here is not method rather computation power. You can solve any verifiable task with high computation, absolutely there must be tweaks in methods but I don't think it is something very big and different. Just OAI asserted they solved with breakthrough.

Wait for self-adapting LLMs. We will see at most in 2 years, now all big tech are focusing on that I think.

I don't remember as a big fan of DeepSeek.