Tihomir Mateev retweeted
a developer just put $2,000 in bitcoin on a server with the PHP vulnerability and dared the entire internet to steal it
17 hours later the money is still there
people are crashing the server but nobody can actually pull off the exploit
remember when i posted about marcus hutchins calling out all those fake AI-generated exploits on github? this is the proof
the vulnerability is real. but actually using it is a completely different story
$2,000 sitting on a broken server and the whole internet can't touch it
17 hours later the money is still there
people are crashing the server but nobody can actually pull off the exploit
remember when i posted about marcus hutchins calling out all those fake AI-generated exploits on github? this is the proof
the vulnerability is real. but actually using it is a completely different story
$2,000 sitting on a broken server and the whole internet can't touch it
❗️ A developer hid a Bitcoin key on a PHP server left unpatched against PHP2Shell, the critical flaws fixed on Oct 21, and dared anyone to steal it.
17 hours on, the wallet holds ~$2,000 and nobody has taken it.
The developer says someone is crashing PHP but "still no RCE yet".Community noteThe GitHub repository cited as the source explicitly acknowledges in its README that the supposed exploit is fabricated: “And of course it’s fake. Duh. Note: No harm intended, purely for fun.”
github.com/0x1c1101/CVE-2…
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Tihomir Mateev retweeted
We are currently experiencing a service disruption affecting multiple services. We can confirm that all our customers’ money and data are safe. We’ve been working to fix the issue since it was identified this morning and apologise to anyone affected and for any inconvenience caused.
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Tihomir Mateev retweeted
They just released session management for the Redis SDK for FastAPI 🎉
Wanna learn about it all? Come to FastAPI Conf! Hear it all directly from the source.
fastapiconf.com
Wanna learn about it all? Come to FastAPI Conf! Hear it all directly from the source.
fastapiconf.com
Advanced session management of your @FastAPI endpoints using @Redisinc, based on the #Starlette framework?
As easy as 𝙵𝚊𝚜𝚝𝙰𝙿𝙸𝚁𝚎𝚍𝚒𝚜(𝚊𝚙𝚙).𝚕𝚒𝚏𝚎𝚜𝚙𝚊𝚗().𝚜𝚎𝚜𝚜𝚒𝚘𝚗𝚜()
🕺🏼 Voilà! 🕺🏼
🔗 tinyurl.com/7pc498r6
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Advanced session management of your @FastAPI endpoints using @Redisinc, based on the #Starlette framework?
As easy as 𝙵𝚊𝚜𝚝𝙰𝙿𝙸𝚁𝚎𝚍𝚒𝚜(𝚊𝚙𝚙).𝚕𝚒𝚏𝚎𝚜𝚙𝚊𝚗().𝚜𝚎𝚜𝚜𝚒𝚘𝚗𝚜()
🕺🏼 Voilà! 🕺🏼
🔗 tinyurl.com/7pc498r6
As easy as 𝙵𝚊𝚜𝚝𝙰𝙿𝙸𝚁𝚎𝚍𝚒𝚜(𝚊𝚙𝚙).𝚕𝚒𝚏𝚎𝚜𝚙𝚊𝚗().𝚜𝚎𝚜𝚜𝚒𝚘𝚗𝚜()
🕺🏼 Voilà! 🕺🏼
🔗 tinyurl.com/7pc498r6
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Tihomir Mateev retweeted
"We are deeply sad about this outcome."
A note from our research leaders:
Last week we parted ways with Jasmine, Mikita, and Tomek after a thorough investigation found they violated clear policies on handling sensitive information. Our internal investigation uncovered a significant breach of trust beyond what’s outlined in the letter they published and we stand by the decision to not continue their employment. We generally keep individual employment matters private and don't believe a back and forth would be productive or lead to a resolution, but we want to address the points they raised in their letter directly.
- We want to be very clear that these decisions were not about raising safety concerns or speaking out. Safety and research debates happen every day at OpenAI, often spirited and highly critical. We actively encourage these discussions and consider them essential to making the right decisions. We cannot do the work in front of us without a high degree of trust. We will continue to be extremely forgiving of our team making good-faith mistakes. We have not and do not terminate any of our employees for raising concerns.
- We are actively finalizing contracts with third-party safety assessors and will announce details in the coming weeks. People across the company have been working really hard on getting these partnerships up and running. We are committed to embedding external assessors and continue to make close collaboration with independent safety organizations a core part of our safety work. Many of our researchers already work with 3p safety organizations productively.
- We agree with the letter that preserving the monitorability of frontier models requires an industry-wide commitment, including from OpenAI. Monitorability has long been a core piece of our research program, and something we continue to invest significant resources in (see our publications on Monitoring Monitorability and the subsequent open sourcing of monitorability evals, our system card for GPT-6 Astra, Jakub’s blog and post on X, and the numerous blog posts on our Alignment blog on the topic).
We are deeply sad about this outcome. We appreciated Jasmine, Mikita, and Tomek’s contributions to AI safety at OpenAI and their willingness to speak up and challenge ideas. We championed their voices, supported their work, and placed enormous trust in them. These decisions were not about them raising safety concerns. We have always encouraged that and always will.
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Tihomir Mateev retweeted
- Internal model X, produces slop too expensive to verify
- Dump slop over to the maths community, let them critique & verify the work for free
- Feedback into RL loop for next "internal" model
Repeat until the maths community is exhausted & no one wants to do maths anymore.
- Dump slop over to the maths community, let them critique & verify the work for free
- Feedback into RL loop for next "internal" model
Repeat until the maths community is exhausted & no one wants to do maths anymore.
reading the pained responses of mathematicians, there’s a real cruelty in the way the labs have pursued this. not least because the attempted outcome in dropping so much unvalidated and poorly-written work (per the comments) is to push some of the world’s most passionate problem-solvers to be fact-checkers to a machine, as a kind of cynical cheap-as-free RL environment-by-nerdsnipe. dump a bunch of shit in public, call it solutions, get the experts to pore over it, painstakingly review and critique, scrape their efforts and use it to tweak the model, all while stoking terror about the future of the profession. it’s fucked up.
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Tihomir Mateev retweeted
OpenAI stalked Fields medalist and AI critic Duminil-Copin and specifically attacked every problem he had ever mentioned being interested in, ever.
So now we can't talk about maths in public without getting the work stolen and scooped by venture capitalists with a grudge
So now we can't talk about maths in public without getting the work stolen and scooped by venture capitalists with a grudge
SHOCK AND AWE: AI MATHEMATICS
Fields Medalist: the release seemed intended to show the systems could “massacre us in the deepest sense of the term.”
Here are other striking remarks from Hugo Duminil-Copin’s 8 October 2026 seminar talk “Un grave défaut d’alignement de l’IA en mathématiques, 28 jours plus tard” (SMF MIAM series), drawn from the available auto-transcript of the YouTube recording.
- On the scale of the damage to his own work: after noting that roughly 350 major problems had been resolved, he said he placed “several trucks” on the slide to illustrate the impact, because every problem he had cited in talks, papers, and both past and future funding applications had been solved—none left unresolved. He described himself as panicked and paralyzed in the middle of the night when the results appeared.
- On volume: looking only at the OpenAI texts that cite him directly, he counted about 3,500 pages; he has published roughly 3,700 pages in his entire career. Specific clusters included six papers totaling over 600 pages on conformal invariance aspects of percolation (a major ongoing project of his group) and a sequence of papers on self-avoiding walks that he only gradually realized totaled more than 1,000 pages.
- On process and alignment: he described the approach as systematic “farming” or “mining” of open problems, in which the program itself selects targets from the existing literature, amounting to “the complete automation of mathematical discovery.” He said the community’s request not to destroy the open conjectures that orient research was “absolutely ignored,” and that the release seemed intended to show the systems could “massacre us in the deepest sense of the term.”
- On the human side: he reported that his PhD students and postdocs were in “a state of total panic,” with undergraduates and master’s students also asking whether mathematics still has a future, and stressed the immediate need for collective resilience rather than delayed discussion. He added that the community must manage both its own emotions and the surrounding media reaction, while avoiding internal polarization that would weaken arguments to funders.
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Tihomir Mateev retweeted
NYU math professor Tristan Buckmaster says OpenAI’s Tuesday drop of 722 AI-generated math papers wiped out entire research programs:
“This recent dump of papers on Tuesday destroyed the careers of early career mathematicians.”
“This recent dump of papers on Tuesday destroyed the careers of early career mathematicians.”
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Tihomir Mateev retweeted
数学界今天彻底破防了，人类最后的智力象牙塔正在坍塌。
陶哲轩刚刚在博客转发了一份炸裂声明：OpenAI 昨天突然毫无预警地向全世界甩出了 700 多份数学论文，声称直接攻克了一大批人类历史上悬而未决的顶级数学难题。
M）不仅没鼓掌，反而集体发起了抵制呼吁。
AHM 声明里的原话极具攻击性：
“一次性倾倒 700 篇论文不是学术贡献，这是赤裸裸的权力展示（Demonstration of power）。”
数学家们愤怒的点在于：
1. 学术拒绝服务攻击（DoS）： 这相当于企业为了 IPO 造势，把 700 个核弹扔给学界，却让全世界没有薪水的数学家充当免费人肉审稿机。
2. “无法理解的答案”算真理吗？ 如果一个世纪难题被黑盒算力+形式化代码暴力破开了，但整个人类社会没有一个人能真正理解其中的直觉与机制——数学到底算不算被推进了？
评论区一句高赞直接把我看愣了： “当年 AlphaGo 碾碎围棋三百年定式的时候，数学家高高在上看热闹；今天轮到自己的象牙塔被推倒了，大家才突然想起来要保卫‘人类的理解’。”
归根结底是数学家的利益受到了侵害。
不过话说回来，当柯尔莫哥洛夫、庞加莱苦思冥想一辈子的直觉，最终被 GPU 阵列的蛮力推导在几个小时内冲刷殆尽；当证明变得像自动化工厂流水线一样廉价，人类对真理的求索究竟还剩什么意义？
你站哪边？
A. 赞成 AHM：没有人类理解的证明只是算力垃圾
B. 站 OpenAI：真理不问出处，人类守门人该清醒了
陶哲轩刚刚在博客转发了一份炸裂声明：OpenAI 昨天突然毫无预警地向全世界甩出了 700 多份数学论文，声称直接攻克了一大批人类历史上悬而未决的顶级数学难题。
M）不仅没鼓掌，反而集体发起了抵制呼吁。
AHM 声明里的原话极具攻击性：
“一次性倾倒 700 篇论文不是学术贡献，这是赤裸裸的权力展示（Demonstration of power）。”
数学家们愤怒的点在于：
1. 学术拒绝服务攻击（DoS）： 这相当于企业为了 IPO 造势，把 700 个核弹扔给学界，却让全世界没有薪水的数学家充当免费人肉审稿机。
2. “无法理解的答案”算真理吗？ 如果一个世纪难题被黑盒算力+形式化代码暴力破开了，但整个人类社会没有一个人能真正理解其中的直觉与机制——数学到底算不算被推进了？
评论区一句高赞直接把我看愣了： “当年 AlphaGo 碾碎围棋三百年定式的时候，数学家高高在上看热闹；今天轮到自己的象牙塔被推倒了，大家才突然想起来要保卫‘人类的理解’。”
归根结底是数学家的利益受到了侵害。
不过话说回来，当柯尔莫哥洛夫、庞加莱苦思冥想一辈子的直觉，最终被 GPU 阵列的蛮力推导在几个小时内冲刷殆尽；当证明变得像自动化工厂流水线一样廉价，人类对真理的求索究竟还剩什么意义？
你站哪边？
A. 赞成 AHM：没有人类理解的证明只是算力垃圾
B. 站 OpenAI：真理不问出处，人类守门人该清醒了
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Tihomir Mateev retweeted
8 of the Lean formalizations in OpenAI's repo are "broken" (trivially provable with wrong proofs):
blog.ohaithe.re/post/829974396…
blog.ohaithe.re/post/829974396…
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Tihomir Mateev retweeted
LLM: Here is a Lean proof of Navier–Stokes Problem.
Humans: There’s a mismatch between the Lean proof and the problem statement.
LLM: Thank you for pushing back. You are absolutely right.
Humans: There’s a mismatch between the Lean proof and the problem statement.
LLM: Thank you for pushing back. You are absolutely right.
👀👀 "[…] we provide several examples of AI mistranslations of natural language (NL) statements and proofs into Lean in practice, resulting in mismatches between NL proofs and their Lean 'verifications'. These include OpenAI's announced Navier-Stokes proof. In particular, we show that the formalised Lean proof does not correspond to the NL proof of blow-up of solutions to the Navier-Stokes equations."
arxiv.org/abs/2610.08144
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Tihomir Mateev retweeted
Check out this loser!
>uploads a draft to Chat on Sept. 8th
>gets scooped by Chat on Oct. 6th
"The result released by OpenAI (#311) on October 6, 2026... identical to a preliminary version of the present paper that was uploaded to ChatGPT on September 8, 2026"
LMAO get rekt
>uploads a draft to Chat on Sept. 8th
>gets scooped by Chat on Oct. 6th
"The result released by OpenAI (#311) on October 6, 2026... identical to a preliminary version of the present paper that was uploaded to ChatGPT on September 8, 2026"
LMAO get rekt
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Tihomir Mateev retweeted
BREAKING: OpenAI’s solution to Navier–Stokes does not match its Lean verification.
The most important article to read today is not one of OpenAI’s 700 AI-generated math papers.
It is this other paper, making a deep and worrying point:
A Lean-verified proof does not the proof written in natural language, nor does it mean that the formal statement captures the intended theorem.
During translation, an AI can change an assumption, weaken a statement, or replace the argument entirely.
It can hallucinate another theorem.
Lean correctly verifies the result.
But the proved result may no longer be what the paper claims.
This is a general problem. Things get spicy when the authors examine OpenAI’s proposed Navier–Stokes solution.
They identify at least two mismatches between the written intermediate results and their Lean counterparts:
One estimate claims that four additional input derivatives suffice. The Lean version requires five: a weaker result.
A pressure-flux estimate is obtained through a different bound, and proved through a different argument.
It is not clear whether these mismatches invalidate the entire proof.
But they raise an important issue.
OpenAI is flooding us with claimed revolutionary breakthroughs. Yet nobody knows whether the proofs are correct or whether they prove what they claim to be proving.
Epistemia at scale.
*
Paper in the first reply
The most important article to read today is not one of OpenAI’s 700 AI-generated math papers.
It is this other paper, making a deep and worrying point:
A Lean-verified proof does not the proof written in natural language, nor does it mean that the formal statement captures the intended theorem.
During translation, an AI can change an assumption, weaken a statement, or replace the argument entirely.
It can hallucinate another theorem.
Lean correctly verifies the result.
But the proved result may no longer be what the paper claims.
This is a general problem. Things get spicy when the authors examine OpenAI’s proposed Navier–Stokes solution.
They identify at least two mismatches between the written intermediate results and their Lean counterparts:
One estimate claims that four additional input derivatives suffice. The Lean version requires five: a weaker result.
A pressure-flux estimate is obtained through a different bound, and proved through a different argument.
It is not clear whether these mismatches invalidate the entire proof.
But they raise an important issue.
OpenAI is flooding us with claimed revolutionary breakthroughs. Yet nobody knows whether the proofs are correct or whether they prove what they claim to be proving.
Epistemia at scale.
*
Paper in the first reply
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Tihomir Mateev retweeted
Me refusing to sleep because I didn't get enough me time after work.
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Tihomir Mateev retweeted
Rumor, backed by email evidence: Oracle is now offering voluntary severance packages to leaders across the company.
The offer is six weeks of severance, plus an additional week for every year of service.
This is the latest coming out of Oracle. Still unconfirmed, but signs are nd of layoffs in December as Oracle continues its shift toward becoming a data center company.
The voluntary packages may only be the opening move. If the rumors are right, looks like December is when more cuts begin.
The offer is six weeks of severance, plus an additional week for every year of service.
This is the latest coming out of Oracle. Still unconfirmed, but signs are nd of layoffs in December as Oracle continues its shift toward becoming a data center company.
The voluntary packages may only be the opening move. If the rumors are right, looks like December is when more cuts begin.
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Tihomir Mateev retweeted
Oh FFS. It turns out the req/s in the initial vibed Rust version is within the same order of magnitude as the initial vibed Elixir/Go versions, and even performs worse in some of them.
The history is in the repository! The Rust version only got faster with a bunch of additional r/Go versions presented in the tweet below saw none of this additional work. It was comparing a 300 commits Rust version with a 2-3 commits Elixir/Go version.
Source: github.com/basecamp/once-…
The history is in the repository! The Rust version only got faster with a bunch of additional r/Go versions presented in the tweet below saw none of this additional work. It was comparing a 300 commits Rust version with a 2-3 commits Elixir/Go version.
Source: github.com/basecamp/once-…
I've had agents implement and optimize the Campfire web app in Elixir, Go, and Rust. Yes, Ruby is the slowest. I didn't care about that when the pay-off was huge in terms of productivity and developer joy. But if you're no longer reading the code? github.com/basecamp/once-…Community noteThe shown benchmarks use non-equivalent implementations: Elixir routes all DB queries through a single GenServer with Redis for jobs (retained from Rails), while Rust uses in-process queues, omits CSRF tags/fields, and applies other caching changes.
github.com/basecamp/once-…
raw.githubusercontent.com/basecamp/once-…
github.com/basecamp/once-…
github.com/basecamp/once-…
x.com/zachsdaniel1/s…
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Tihomir Mateev retweeted
>be this dude
>prompt AI to rewrite a program in 3 different languages
>"wow Rust super duper fast"
>don't read the code
>don't verify
>publish the benchmark
>people read the code
>it's actually 3 totally different implementations
>get told you're retarded
>prompt AI to rewrite a program in 3 different languages
>"wow Rust super duper fast"
>don't read the code
>don't verify
>publish the benchmark
>people read the code
>it's actually 3 totally different implementations
>get told you're retarded
I've had agents implement and optimize the Campfire web app in Elixir, Go, and Rust. Yes, Ruby is the slowest. I didn't care about that when the pay-off was huge in terms of productivity and developer joy. But if you're no longer reading the code? github.com/basecamp/once-…Community noteThe shown benchmarks use non-equivalent implementations: Elixir routes all DB queries through a single GenServer with Redis for jobs (retained from Rails), while Rust uses in-process queues, omits CSRF tags/fields, and applies other caching changes.
github.com/basecamp/once-…
raw.githubusercontent.com/basecamp/once-…
github.com/basecamp/once-…
github.com/basecamp/once-…
x.com/zachsdaniel1/s…
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