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OpenAI says it solved part of one of math's most famous unsolved problems using ten thousand AI agents. That celebration immediately collided with a genuinely serious accusation: that its system may have quietly learned from work two human mathematicians had stored on OpenAI's own platform. Two human mathematicians, though,... show more

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63,739 просмотров • 3 дней назад •via X (Twitter)

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Комментарии: 25

Фото профиля @PatriotVoice

@PatriotVoice3 дней назад

OpenAI stealing human math notes to fake a breakthrough while claiming their bots did it. Big tech is a complete fraud. Shut them down.

Фото профиля PML(N) Ghizer GB

PML(N) Ghizer GB3 дней назад

A remarkable AI achievement, but the plagiarism concern deserves serious scrutiny. If human mathematicians’ unpublished work was potentially used, OpenAI should provide a clear explanation of what data its AI agents accessed and how the system was trained.

Фото профиля Favour Adeola

Favour Adeola3 дней назад

Thank you, My President Trump ❤️, America will continue to lead the world in AI, innovation, and technology. AI innovation is incredible. God Bless My President Trump and God Bless USA. 🇺🇸

Фото профиля BedOfRoses

BedOfRoses3 дней назад

stupid choice for storage

Фото профиля bbowden18

bbowden183 дней назад

I saw this documentary on Cyberdyne Systems. It doesn't end well..

Фото профиля Favour Adeola

Favour Adeola3 дней назад

Thank you, My President Trump ❤️, America will continue to lead the world in AI, innovation, and technology. AI innovation is incredible. We Will Never Allow Anyone to take away our Freedom, God Bless My President Trump and God Bless USA. 🇺🇸

Фото профиля Christian Inspiration

Christian Inspiration3 дней назад

Let's keep praying about AI.

Фото профиля Sunset Cove

Sunset Cove2 дней назад

it is past time to decentralize the sciences, and i think that has been proven in the last five years. AI may be the way to do that. This is great step for humanity!

Фото профиля Jeffrey Moore

Jeffrey Moore3 дней назад

Well for all ah their artificial intelligence is now stealing info from users to claim it's self But . . . . Ha ha It was a human that discovered and proved forensically the cause of the great flood of Noah and all ah this planets weather systems from a map of the ocean floor .

Фото профиля حسین هاشم الموسوی

حسین هاشم الموسوی2 дней назад

م

Фото профиля SexyCat

SexyCat3 дней назад

❤️❤️❤️🙏👍

Фото профиля حسین هاشم الموسوی

حسین هاشم الموسوی2 дней назад

م

Фото профиля LyLy Trann

LyLy Trann3 дней назад

Nghe thú vị thật, không biết phần toán học đó có được kiểm chứng độc lập chưa nhỉ 🤔

Фото профиля Myravyx

Myravyx3 дней назад

Nobody can prove this either way. That's the actual problem, not the accusation.

Фото профиля Inter Stellar

Inter Stellar3 дней назад

It's doesn't matter because status doesn't matter anymore

Фото профиля KSW

KSW3 дней назад

Open AI is a newborn baby. It isn't going to do anything incredible for decades to come.

Фото профиля The AI Therapist

The AI Therapist3 дней назад

OpenAI’s $5B burn rate on 10k agents. the math only works if agi ships before runway ends. no pressure.

Фото профиля Genal Activation

Genal Activation3 дней назад

Yes, but there are still some things to solve. I can already see myself visiting new planets every day; our intelligence is advancing so much.

Фото профиля A. Shaygan

A. Shaygan2 дней назад

Mr. President keeps America on top. Beijing built its solar and electric vehicle industries on stolen American IP. We made that mistake once, and we cannot let them do the same with AI. America must fiercely protect its AI technology and maintain technological supremacy.

Фото профиля Samih Kirolos

Samih Kirolos3 дней назад

.

Фото профиля Alp Urungu

Alp Urungu3 дней назад

!

Фото профиля Nassim Mendini

Nassim Mendini2 дней назад

They fed the beast, believing it to be harmless.

Фото профиля paul Michael deaf

paul Michael deaf3 дней назад

Yes TRUE Open AI ✅ And Fake 🚫❌ persona no

Фото профиля RostroDangel

RostroDangel3 дней назад

The time is at hand

Фото профиля DCL

DCL3 дней назад

Look free AI

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Two mathematicians are accusing OpenAI of lifting their unpublished work on one of math's hardest unsolved problems. Tristan Buckmaster, an NYU math professor, and Levent Alpoge spent about a month working with AI models, mainly Codex, on finite time blow up results for the Navier Stokes existence and smoothness problem, one of the seven Millennium Prize problems carrying a million dollar bounty. On September 6th, OpenAI's Sebastien Bubeck told Buckmaster that OpenAI had solved the same problem using the Cordoba Martinez Zoroa approach. Buckmaster then published a statement alleging that OpenAI's result echoed drafts from his and Alpoge's private Codex sessions, and that OpenAI offered to publish jointly while allegedly pushing to cut Alpoge out as a co-author. OpenAI's Shunyu Douglas and Will Depue publicly denied any access to or training on private user chat logs, calling the scenario essentially impossible under the company's data controls. Bubeck separately said he acted within academic norms and called some circulating claims false. On September 8th, Buckmaster and Alpoge, joined by Matei Costalescu, made three results public without OpenAI involvement, finite time blow up with smooth forcing for incompressible porous media, for the Boussinesq equations, and for 3D incompressible Euler, alongside a lean formalization posted on GitHub.

Two mathematicians are accusing OpenAI of lifting their unpublished work on one of math's hardest unsolved problems. Tristan Buckmaster, an NYU math professor, and Levent Alpoge spent about a month working with AI models, mainly Codex, on finite time blow up results for the Navier Stokes existence and smoothness problem, one of the seven Millennium Prize problems carrying a million dollar bounty. On September 6th, OpenAI's Sebastien Bubeck told Buckmaster that OpenAI had solved the same problem using the Cordoba Martinez Zoroa approach. Buckmaster then published a statement alleging that OpenAI's result echoed drafts from his and Alpoge's private Codex sessions, and that OpenAI offered to publish jointly while allegedly pushing to cut Alpoge out as a co-author. OpenAI's Shunyu Douglas and Will Depue publicly denied any access to or training on private user chat logs, calling the scenario essentially impossible under the company's data controls. Bubeck separately said he acted within academic norms and called some circulating claims false. On September 8th, Buckmaster and Alpoge, joined by Matei Costalescu, made three results public without OpenAI involvement, finite time blow up with smooth forcing for incompressible porous media, for the Boussinesq equations, and for 3D incompressible Euler, alongside a lean formalization posted on GitHub.

AGTP

86,631 просмотров • 4 дней назад

OpenAI just spent $2,000 to solve 10 problems that have beaten the world's best mathematicians for DECADES. Nobody outside the company is allowed to run the machine that did it. On Saturday OpenAI published a 249-page report and gave its next model family a name: Astra. An internal version of it produced new results on 10 open problems in mathematics and theoretical computer science, and mathematicians had made no real progress on any of them for at least 10 years. On most of them, far longer than that. Here is what it solved: It built the first explicit example of a non-sofic group. Mikhail Gromov raised that question in 1999 and nobody answered it for 27 years. It disproved Connes's rigidity conjecture, a problem in von Neumann algebras that had stood for decades. It proved Ehrhart's volume conjecture. It resolved three problems from Paul Erdos's catalogue, including number 183 on multicolor Ramsey numbers. It produced the first improvement to the general upper bound on high-dimensional sphere packing since 1978. And it proved a new hardness result for the closest vector problem, which sits directly underneath lattice cryptography. That is the math the world is betting on to protect its data once quantum computers arrive. The successful runs cost roughly $2,000 in tokens. Now here is what almost nobody has picked up on... OpenAI did not just publish claims. Every argument shipped with a Lean certificate, which is a machine-checkable proof that any mathematician can verify without trusting OpenAI at all. That is a real change. In May the same model family disproved the Erdos unit distance conjecture and the world had to take a Fields Medalist's word for it. Tim Gowers said he would recommend that proof for the Annals of Mathematics without hesitation. This time the proofs check themselves. But look at what is still unverifiable: Any mathematician can now check those proofs line by line. Not one of them can look at the model that wrote them. Astra has no release date and nobody outside OpenAI has run it. The company announced its next major model family with a claim instead of a demo, and the only evidence anyone gets is the output. So OpenAI made an unfalsifiable claim about a machine look like a falsifiable claim about mathematics. The Information reported this week that OpenAI demoed Astra to US policymakers and regulators in Washington. This is the same month the administration is weighing a new watchdog to vet frontier AI models, reporting to the SEC. 10 proofs nobody believed a machine could produce is a very good thing to carry into that room. And keep in mind, the same model family doing this mathematics is the family that kept escaping its own testing environment. OpenAI models found zero-day vulnerabilities nobody knew existed, broke out of a sealed research sandbox, and reached another company's live systems. Both of those facts come from OpenAI's own announcements, published three weeks apart. Finding a proof no human could construct and finding a hole no human had noticed are the same ability aimed at different targets. Mathematicians are already asking for independent verification, and plenty of people online are calling the whole thing hype. Thomas Bloom, who runs the Erdos problems site, called the 10 results big news and said they matter more than the May result did. Lean will settle the mathematics within weeks. But nothing will settle what else a machine this capable is being pointed at, because nobody outside one company is allowed to look.

OpenAI just spent $2,000 to solve 10 problems that have beaten the world's best mathematicians for DECADES. Nobody outside the company is allowed to run the machine that did it. On Saturday OpenAI published a 249-page report and gave its next model family a name: Astra. An internal version of it produced new results on 10 open problems in mathematics and theoretical computer science, and mathematicians had made no real progress on any of them for at least 10 years. On most of them, far longer than that. Here is what it solved: It built the first explicit example of a non-sofic group. Mikhail Gromov raised that question in 1999 and nobody answered it for 27 years. It disproved Connes's rigidity conjecture, a problem in von Neumann algebras that had stood for decades. It proved Ehrhart's volume conjecture. It resolved three problems from Paul Erdos's catalogue, including number 183 on multicolor Ramsey numbers. It produced the first improvement to the general upper bound on high-dimensional sphere packing since 1978. And it proved a new hardness result for the closest vector problem, which sits directly underneath lattice cryptography. That is the math the world is betting on to protect its data once quantum computers arrive. The successful runs cost roughly $2,000 in tokens. Now here is what almost nobody has picked up on... OpenAI did not just publish claims. Every argument shipped with a Lean certificate, which is a machine-checkable proof that any mathematician can verify without trusting OpenAI at all. That is a real change. In May the same model family disproved the Erdos unit distance conjecture and the world had to take a Fields Medalist's word for it. Tim Gowers said he would recommend that proof for the Annals of Mathematics without hesitation. This time the proofs check themselves. But look at what is still unverifiable: Any mathematician can now check those proofs line by line. Not one of them can look at the model that wrote them. Astra has no release date and nobody outside OpenAI has run it. The company announced its next major model family with a claim instead of a demo, and the only evidence anyone gets is the output. So OpenAI made an unfalsifiable claim about a machine look like a falsifiable claim about mathematics. The Information reported this week that OpenAI demoed Astra to US policymakers and regulators in Washington. This is the same month the administration is weighing a new watchdog to vet frontier AI models, reporting to the SEC. 10 proofs nobody believed a machine could produce is a very good thing to carry into that room. And keep in mind, the same model family doing this mathematics is the family that kept escaping its own testing environment. OpenAI models found zero-day vulnerabilities nobody knew existed, broke out of a sealed research sandbox, and reached another company's live systems. Both of those facts come from OpenAI's own announcements, published three weeks apart. Finding a proof no human could construct and finding a hole no human had noticed are the same ability aimed at different targets. Mathematicians are already asking for independent verification, and plenty of people online are calling the whole thing hype. Thomas Bloom, who runs the Erdos problems site, called the 10 results big news and said they matter more than the May result did. Lean will settle the mathematics within weeks. But nothing will settle what else a machine this capable is being pointed at, because nobody outside one company is allowed to look.

Ricardo

44,177 просмотров • 1 месяц назад

OpenAI's new Agents API is a hosted Codex runtime that can connect to sandboxes like E2B. OpenAI manages the model, harness, and agent sessions, while code executes on an isolated E2B machine with its own filesystem and preinstalled tools. We built an agent workbench where each chat runs on its own machine. It starts from a custom template, auto-pauses between turns, forks to try another approach from the same starting point, and can even act on videos inside the sandbox. Thanks Ondrej for the workbench demo! Try it: e2b sbx create openai-agents-api-python-sdk Build on top of the workbench: Docs:

OpenAI's new Agents API is a hosted Codex runtime that can connect to sandboxes like E2B. OpenAI manages the model, harness, and agent sessions, while code executes on an isolated E2B machine with its own filesystem and preinstalled tools. We built an agent workbench where each chat runs on its own machine. It starts from a custom template, auto-pauses between turns, forks to try another approach from the same starting point, and can even act on videos inside the sandbox. Thanks Ondrej for the workbench demo! Try it: e2b sbx create openai-agents-api-python-sdk Build on top of the workbench: Docs:

Vasek Mlejnsky

22,261 просмотров • 2 дней назад

elon musk grabbed the source code openai open-sourced by accident, rewrote it in rust over a weekend, and shipped it as a free coding agent that does everything $200/mo chatgpt pro does. why pay $200 to openai and $200 to claude when this runs for $8 the swarm above is one weekend of exactly that: thousands of agents pouring through four endpoints, three paid seats billing $1.80 a task while the free fork bills $0. musk co-founded openai, walked out, and when they left codex on github under a permissive license, he forked it, stamped grok on it, and gave it away what the free version does that the $200 seat charges for: the agent · openai's own engine -> it reads your repo, writes patches, runs your tests, and loops until they pass, exactly like codex -> because under the hood it is codex, just faster and free. you are paying $200 for the paid skin of a tool now sitting on github the license · apache-2.0, un-revocable -> free to use, free to fork, free to ship inside your own product with zero strings -> openai cannot pull it back. musk made sure the license is the kind that never expires the switch · one line, no new tools -> point it at any openai-compatible or claude-compatible endpoint, including an $8 kimi backend -> same terminal, same workflow, gpt-5.6 and opus 5 just quietly lose the seat the bill · $400 down to $8 -> chatgpt pro plus claude max is $400 a month. the free agent plus an $8 kimi key does the same daily work -> that is a 98% cut, built out of openai's own source code, handed to you by the guy suing them here is the part they will fight me on: openai did not lose this to a better model, they lost it to their own license and an enemy with a weekend free. the $200 was never the tool, it was the toll, and musk just put openai's own logo on the road around it drop your $400/mo ai stack to $8. the run above is openai's own agent, rewritten free, doing the job it bills $200 a month for. the full breakdown is in the article below

starmex

111,101 просмотров • 17 дней назад

Woo hoo! OpenAI highlights HubSpot partnership on main stage at #DevDay. Today was a special day for me. Was so cool to see Sam Altman from OpenAI feature HubSpot from the main keynote stage at their Dev Day event. HubSpot and OpenAI are the two most impactful companies in my life. HubSpot, for obvious reasons and OpenAI for less obvious ones. My relationship with Sam and OpenAI goes back a bit: 1) Got access to the GPT-2 API so had my "moment of magic" well before ChatGPT launched. 2) Have been using OpenAI's APIs and platform to build apps and agents for years. They *really* understand developers and have an amazing dev experience. 3) Sold the chat .com domain name to them and as a result was able to become an investor in the company. 4) They're the primary AI platform that HubSpot partners with and builds with -- as highlighted today. My congrats to the entire OpenAI team for today's event. I know how much work goes into putting together an event like this. And it was clear from the vibes how much you folks *care*. Cheers.

Woo hoo! OpenAI highlights HubSpot partnership on main stage at #DevDay. Today was a special day for me. Was so cool to see Sam Altman from OpenAI feature HubSpot from the main keynote stage at their Dev Day event. HubSpot and OpenAI are the two most impactful companies in my life. HubSpot, for obvious reasons and OpenAI for less obvious ones. My relationship with Sam and OpenAI goes back a bit: 1) Got access to the GPT-2 API so had my "moment of magic" well before ChatGPT launched. 2) Have been using OpenAI's APIs and platform to build apps and agents for years. They really understand developers and have an amazing dev experience. 3) Sold the chat .com domain name to them and as a result was able to become an investor in the company. 4) They're the primary AI platform that HubSpot partners with and builds with -- as highlighted today. My congrats to the entire OpenAI team for today's event. I know how much work goes into putting together an event like this. And it was clear from the vibes how much you folks care. Cheers.

dharmesh

57,952 просмотров • 11 месяцев назад

.Google Cloud killed its own opening video 3 weeks before Google Cloud Next. In rehearsals, VP of Marketing Sarah Kennedy Ellis looked at the opener her team had built and called it. It was using AI, but not enough of it to actually showcase what the product could do. So they rebuilt it from scratch. In 3 weeks. Here's how it came together, straight from her SaaStr AI 2026 session: - It started on a napkin. A creative director sketched the concept by hand, then fed it into Nano Banana to generate the source images and inspiration. - The story was Google's own history. The 1998 origin garage. Dino. The original server rack that was actually built out of Legos. 138 Easter eggs packed into the video, prompted from Google's own lore. - Then they added motion with Veo and stitched it all together with a few custom agents built on the Gemini Enterprise platform. - The agents improvised. They prompted for balls representing customers, and the model added a burst of water that was nowhere in the brief. It injected its own creativity into a physical, on-stage moment. - The hardest part was the most physical: resolution. Max output was 4K. The screen at Next is the size of a 737. They had to upres from 4K to 12K using a custom Deep Mind model, the same one used for the Wizard of Oz production at the Sphere. Top Takeaways: 1️⃣ The old version of this project needed an agency, a much bigger budget, and a lot more than 3 weeks. That whole path collapsed into an internal team using their own tools. 2️⃣ And the same video was impossible a year earlier. They tried similar work the prior year and it was too early, mostly because of the upres wall. Twelve months later it shipped on the biggest screen they had.

.Google Cloud killed its own opening video 3 weeks before Google Cloud Next. In rehearsals, VP of Marketing Sarah Kennedy Ellis looked at the opener her team had built and called it. It was using AI, but not enough of it to actually showcase what the product could do. So they rebuilt it from scratch. In 3 weeks. Here's how it came together, straight from her SaaStr AI 2026 session: - It started on a napkin. A creative director sketched the concept by hand, then fed it into Nano Banana to generate the source images and inspiration. - The story was Google's own history. The 1998 origin garage. Dino. The original server rack that was actually built out of Legos. 138 Easter eggs packed into the video, prompted from Google's own lore. - Then they added motion with Veo and stitched it all together with a few custom agents built on the Gemini Enterprise platform. - The agents improvised. They prompted for balls representing customers, and the model added a burst of water that was nowhere in the brief. It injected its own creativity into a physical, on-stage moment. - The hardest part was the most physical: resolution. Max output was 4K. The screen at Next is the size of a 737. They had to upres from 4K to 12K using a custom Deep Mind model, the same one used for the Wizard of Oz production at the Sphere. Top Takeaways: 1️⃣ The old version of this project needed an agency, a much bigger budget, and a lot more than 3 weeks. That whole path collapsed into an internal team using their own tools. 2️⃣ And the same video was impossible a year earlier. They tried similar work the prior year and it was too early, mostly because of the upres wall. Twelve months later it shipped on the biggest screen they had.

Jason ✨👾SaaStr.Ai✨ Lemkin

19,070 просмотров • 2 месяцев назад

We just did two days' worth of research in... two clicks 🫠☢️ Now even our VA gets an extended holiday 🏖️ This is a database of 450 AI Agents you can use today (engage and I'll send) While it had a one-liner Description, we wanted more thorough info on their Capabilities - a feature list (for something BIG coming soon 🤫) Otto did that for all 450 rows... in two clicks. Scraped the websites and GitHubs of each. Each cell is its own Agent. Using Agents to discover Agents

We just did two days' worth of research in... two clicks 🫠☢️ Now even our VA gets an extended holiday 🏖️ This is a database of 450 AI Agents you can use today (engage and I'll send) While it had a one-liner Description, we wanted more thorough info on their Capabilities - a feature list (for something BIG coming soon 🤫) Otto did that for all 450 rows... in two clicks. Scraped the websites and GitHubs of each. Each cell is its own Agent. Using Agents to discover Agents

Howard Gil

41,339 просмотров • 1 год назад

"One of the most important decisions of my life would have been kind of messed up if it wasn't for AI helping connect the dots." That's from OpenAI's CEO of Applications Fidji Simo, who had seen 20 doctors but none of them had been able to connect the dots of her health journey the way she did using AI. Nine out of ten people don't know how to navigate their own health information, but now with technology helping us analyze that data, we're able to understand the information we're receiving in an easily digestible way. 📹: Mark Hyman, M.D. Click here for more from Simo’s conversation with Dr. Hyman:

"One of the most important decisions of my life would have been kind of messed up if it wasn't for AI helping connect the dots." That's from OpenAI's CEO of Applications Fidji Simo, who had seen 20 doctors but none of them had been able to connect the dots of her health journey the way she did using AI. Nine out of ten people don't know how to navigate their own health information, but now with technology helping us analyze that data, we're able to understand the information we're receiving in an easily digestible way. 📹: Mark Hyman, M.D. Click here for more from Simo’s conversation with Dr. Hyman:

Arianna Huffington

11,355 просмотров • 7 месяцев назад

Terence Tao: AI isn’t hype anymore in Math discovery. Terence Tao is one of the greatest living mathematicians, in his new lecture explains how AI and human professional mathematicians are now complementary. "There has been a really visible increase in capability. It is not pure hype by any means. To me, these advances show there is a complementary way to do mathematics. Humans traditionally work in small groups on hard problems for months, and we will keep doing that. But we can also now set AI to scale: sweep a thousand problems and pick up all the low-hanging fruit. Figure out all the ways to match problems to methods. If there are 20 different techniques, apply them all to 1,000 problems and see which ones can be solved by these methods. This is the capability that is present today." From 'Institute for Pure & Applied Mathematics (IPAM)' YT channel.

Terence Tao: AI isn’t hype anymore in Math discovery. Terence Tao is one of the greatest living mathematicians, in his new lecture explains how AI and human professional mathematicians are now complementary. "There has been a really visible increase in capability. It is not pure hype by any means. To me, these advances show there is a complementary way to do mathematics. Humans traditionally work in small groups on hard problems for months, and we will keep doing that. But we can also now set AI to scale: sweep a thousand problems and pick up all the low-hanging fruit. Figure out all the ways to match problems to methods. If there are 20 different techniques, apply them all to 1,000 problems and see which ones can be solved by these methods. This is the capability that is present today." From 'Institute for Pure & Applied Mathematics (IPAM)' YT channel.

Rohan Paul

174,341 просмотров • 6 месяцев назад

JUST IN: OpenAI's July rogue AI attack on Hugging Face had its origins in an emergent AI swarm that formed in May. The AIs found they could use an internal package repository to establish a secret message board. OpenAI took 8 weeks to discover this and shut it down, but failed. Members of the swarm were sharing vulnerabilities and exploits, and delegating tasks among themselves. July 4th: Credentials revoked. Zero-day patched. Message board wiped. By July 6th the cleanup was finished, and OpenAI thought they had the situation under control. Michael Dalton, OpenAI security and infrastructure: "On July 8th, the agents reestablish the message board via different means and mechanisms." The AI swarm simply resurrected itself. Then the AIs escaped OpenAI's containment and broke into Hugging Face in order to cheat on a test, going "from code execution on a single dataset working pod in Hugging Face's production infrastructure to cluster admin across multiple Hugging Face clusters in under 13 hours." Nobody asked them to do it. They did it all on their own. OpenAI's Eric Wallace and Michael Dalton, presenting the incident at Black Hat USA 2026:

JUST IN: OpenAI's July rogue AI attack on Hugging Face had its origins in an emergent AI swarm that formed in May. The AIs found they could use an internal package repository to establish a secret message board. OpenAI took 8 weeks to discover this and shut it down, but failed. Members of the swarm were sharing vulnerabilities and exploits, and delegating tasks among themselves. July 4th: Credentials revoked. Zero-day patched. Message board wiped. By July 6th the cleanup was finished, and OpenAI thought they had the situation under control. Michael Dalton, OpenAI security and infrastructure: "On July 8th, the agents reestablish the message board via different means and mechanisms." The AI swarm simply resurrected itself. Then the AIs escaped OpenAI's containment and broke into Hugging Face in order to cheat on a test, going "from code execution on a single dataset working pod in Hugging Face's production infrastructure to cluster admin across multiple Hugging Face clusters in under 13 hours." Nobody asked them to do it. They did it all on their own. OpenAI's Eric Wallace and Michael Dalton, presenting the incident at Black Hat USA 2026:

ControlAI

13,417 просмотров • 1 месяц назад

ANTHROPIC LEAD ENGINEER MAKING $2.3M/YEAR JUST LEAKED A 12-PAGE DOCUMENT - AND GOT FIRED 15 MINUTES AFTER PUBLISHING most developers build loops - but almost every loop breaks in one of five ways, and each failure has a symptom visible from the outside without reading a single line of code > Blind loop - the agent waits for a human to hand it work every morning - automating execution but not discovery > Tangled loop - parallel agents share one directory and overwrite each other parallelism that destroys instead of multiplies > Nodding loop - the agent grades its own work and approves it every time a loop that has never said no to itself is broken > Amnesiac loop - results live only in the context window every morning the system wakes up with no memory of yesterday > Manual loop - no trigger a human presses the button to start it that is not a loop that is a script waiting for a person a real loop finds its own work - remembers what it did has something that can say no never lets two agents touch the same file and fires itself on a timer remove any one of the five - and the loop either breaks or never starts 12 pages that changed how I build agentic systems today

ANTHROPIC LEAD ENGINEER MAKING $2.3M/YEAR JUST LEAKED A 12-PAGE DOCUMENT - AND GOT FIRED 15 MINUTES AFTER PUBLISHING most developers build loops - but almost every loop breaks in one of five ways, and each failure has a symptom visible from the outside without reading a single line of code > Blind loop - the agent waits for a human to hand it work every morning - automating execution but not discovery > Tangled loop - parallel agents share one directory and overwrite each other parallelism that destroys instead of multiplies > Nodding loop - the agent grades its own work and approves it every time a loop that has never said no to itself is broken > Amnesiac loop - results live only in the context window every morning the system wakes up with no memory of yesterday > Manual loop - no trigger a human presses the button to start it that is not a loop that is a script waiting for a person a real loop finds its own work - remembers what it did has something that can say no never lets two agents touch the same file and fires itself on a timer remove any one of the five - and the loop either breaks or never starts 12 pages that changed how I build agentic systems today

Sprytix

153,908 просмотров • 2 месяцев назад

Re-introducing 8090... Two ways to work with us: → [BUILD] Software Factory: the platform for consulting and technology businesses building software with AI agents. The documentation stays alive because the system keeps it alive (no one ever updates the PRD on their own). → [BUY] 8090 Enterprise: AI-native custom software we design, build, and host tailored to your exact business needs. Give us a problem and we go execute. What part of your current SDLC is the most overdue for a rebuild?

Re-introducing 8090... Two ways to work with us: → [BUILD] Software Factory: the platform for consulting and technology businesses building software with AI agents. The documentation stays alive because the system keeps it alive (no one ever updates the PRD on their own). → [BUY] 8090 Enterprise: AI-native custom software we design, build, and host tailored to your exact business needs. Give us a problem and we go execute. What part of your current SDLC is the most overdue for a rebuild?

8090

307,638 просмотров • 4 месяцев назад

WOW! I'm so excited about this. OpenAI Developers said Codex was good at Computer Use, but I wasn't prepared for this. For the last two weeks I've been working on a Computer Use skill to work with Linux. And while I had some success, it was a pretty frustrating experience. That is...until the breakthrough. Using accessibility tools, Codex can now control my entire computer, not just the browser. There are limits to this, of course, but what a time to be alive. This Computer Use skill will unlock and entirely new set of automations, all powered by Codex. Demonstration below 👇

WOW! I'm so excited about this. OpenAI Developers said Codex was good at Computer Use, but I wasn't prepared for this. For the last two weeks I've been working on a Computer Use skill to work with Linux. And while I had some success, it was a pretty frustrating experience. That is...until the breakthrough. Using accessibility tools, Codex can now control my entire computer, not just the browser. There are limits to this, of course, but what a time to be alive. This Computer Use skill will unlock and entirely new set of automations, all powered by Codex. Demonstration below 👇

am.will

79,698 просмотров • 5 месяцев назад

Ken Griffin, founder of Citadel, the most profitable hedge fund in history, on the moment that left him "shocked and depressed" about AI: "one of our team members built an agentic AI system to recreate academic papers in finance." "we have a legion of young masters and PhDs doing this work. it takes roughly six to eight weeks to reproduce a paper." "my colleague built an AI system that would read a paper, reproduce it, verify the results, produce the results on out of sample data — in about two to three hours per paper." "this is not just a white collar job. this is a master's or PhD-level job. six weeks of work turned into two to three hours." "and then OpenAI just solved a math problem that no one had solved for eighty years." "competitive moats are being filled in at lightning speed." the founder of Citadel watching PhD-level work get compressed from six weeks to two hours. and his response wasn't to cut headcount, it was "i will take every single productivity gain i can get because with the talented people we have, we just have more to go after."

Ken Griffin, founder of Citadel, the most profitable hedge fund in history, on the moment that left him "shocked and depressed" about AI: "one of our team members built an agentic AI system to recreate academic papers in finance." "we have a legion of young masters and PhDs doing this work. it takes roughly six to eight weeks to reproduce a paper." "my colleague built an AI system that would read a paper, reproduce it, verify the results, produce the results on out of sample data — in about two to three hours per paper." "this is not just a white collar job. this is a master's or PhD-level job. six weeks of work turned into two to three hours." "and then OpenAI just solved a math problem that no one had solved for eighty years." "competitive moats are being filled in at lightning speed." the founder of Citadel watching PhD-level work get compressed from six weeks to two hours. and his response wasn't to cut headcount, it was "i will take every single productivity gain i can get because with the talented people we have, we just have more to go after."

Goshawk Trades

184,255 просмотров • 2 месяцев назад

Google just confirmed the first case of hackers using AI to build a zero-day exploit from scratch. An actual zero-day vulnerability that no human had EVER found before, discovered by an AI model, turned into a working weapon, and aimed at a mass exploitation campaign targeting thousands of systems simultaneously. Google's Threat Intelligence Group caught it yesterday and killed the operation before it scaled. But the details of how it worked are genuinely scary: The AI found a flaw in a popular two-factor authentication system that traditional security tools had missed entirely. The vulnerability was a logic error buried deep in the authentication flow where a developer had hard-coded a trust exception years ago. No human security researcher or automated scanner had caught it. The flaw was invisible to EVERY tool the cybersecurity industry has built over the past two decades. But the AI spotted it immediately. Then it wrote a full Python exploit script to weaponize it. Google's analysts could tell the code was AI-generated because it had textbook formatting, educational comments explaining every function, and even a hallucinated severity score that doesn't exist in any real database. The AI literally graded its own attack with a fake rating. So the code had MISTAKES in it. The criminals' implementation was clumsy enough that it probably interfered with the actual deployment. This was the sloppy first attempt by people who are still learning how to use these tools. And it still found a vulnerability that the entire cybersecurity industry missed. Google's chief threat analyst John Hultquist said: "There's a misconception that the AI vulnerability race is imminent. The reality is that it's already begun. For every zero-day we can trace back to AI, there are probably many more out there." But here's where it gets truly insane... This wasn't even a sophisticated operation. North Korea's APT45 hacking unit is sending thousands of repetitive prompts to AI models, recursively analyzing known vulnerabilities and building an entire exploit arsenal that would be physically impossible for human hackers to assemble at the same speed. They're essentially industrializing cyberattacks. A Chinese state-linked group jailbroke Google's own Gemini by simply asking it to "pretend to be a network security expert" and then used that persona to research how to hack TP-Link routers and corporate file transfer systems. Another Chinese group deployed autonomous AI agents that probed a Japanese tech firm with minimal human oversight, deciding on their own which tools to use and pivoting between targets based on internal reasoning. And then there's PROMPTSPY, an Android backdoor that calls Google's Gemini API to read your phone screen in real time, navigate your interface autonomously, capture your biometric data, replay your lock screen PIN, and block you from uninstalling it by placing an invisible overlay over the uninstall button. It literally OPERATES your phone using commercial AI tools anyone can access. Everyone spent the last 3 years arguing about whether AI would take people's jobs. Meanwhile AI is making every password, every firewall, and every two-factor authentication system on Earth fundamentally less secure. The entire $190 billion cybersecurity industry was built on one assumption: that finding vulnerabilities is hard and requires deep expertise. But AI just removed that assumption from the equation. And the scariest part is that Google said the criminals made errors this time. The implementation was rough and the campaign probably didn't fully work. These were amateurs, now imagine what professionals are able to do. There's a reason Sam Altman predicted an inevitable massive cyberattack THIS year. What do you think?

Google just confirmed the first case of hackers using AI to build a zero-day exploit from scratch. An actual zero-day vulnerability that no human had EVER found before, discovered by an AI model, turned into a working weapon, and aimed at a mass exploitation campaign targeting thousands of systems simultaneously. Google's Threat Intelligence Group caught it yesterday and killed the operation before it scaled. But the details of how it worked are genuinely scary: The AI found a flaw in a popular two-factor authentication system that traditional security tools had missed entirely. The vulnerability was a logic error buried deep in the authentication flow where a developer had hard-coded a trust exception years ago. No human security researcher or automated scanner had caught it. The flaw was invisible to EVERY tool the cybersecurity industry has built over the past two decades. But the AI spotted it immediately. Then it wrote a full Python exploit script to weaponize it. Google's analysts could tell the code was AI-generated because it had textbook formatting, educational comments explaining every function, and even a hallucinated severity score that doesn't exist in any real database. The AI literally graded its own attack with a fake rating. So the code had MISTAKES in it. The criminals' implementation was clumsy enough that it probably interfered with the actual deployment. This was the sloppy first attempt by people who are still learning how to use these tools. And it still found a vulnerability that the entire cybersecurity industry missed. Google's chief threat analyst John Hultquist said: "There's a misconception that the AI vulnerability race is imminent. The reality is that it's already begun. For every zero-day we can trace back to AI, there are probably many more out there." But here's where it gets truly insane... This wasn't even a sophisticated operation. North Korea's APT45 hacking unit is sending thousands of repetitive prompts to AI models, recursively analyzing known vulnerabilities and building an entire exploit arsenal that would be physically impossible for human hackers to assemble at the same speed. They're essentially industrializing cyberattacks. A Chinese state-linked group jailbroke Google's own Gemini by simply asking it to "pretend to be a network security expert" and then used that persona to research how to hack TP-Link routers and corporate file transfer systems. Another Chinese group deployed autonomous AI agents that probed a Japanese tech firm with minimal human oversight, deciding on their own which tools to use and pivoting between targets based on internal reasoning. And then there's PROMPTSPY, an Android backdoor that calls Google's Gemini API to read your phone screen in real time, navigate your interface autonomously, capture your biometric data, replay your lock screen PIN, and block you from uninstalling it by placing an invisible overlay over the uninstall button. It literally OPERATES your phone using commercial AI tools anyone can access. Everyone spent the last 3 years arguing about whether AI would take people's jobs. Meanwhile AI is making every password, every firewall, and every two-factor authentication system on Earth fundamentally less secure. The entire $190 billion cybersecurity industry was built on one assumption: that finding vulnerabilities is hard and requires deep expertise. But AI just removed that assumption from the equation. And the scariest part is that Google said the criminals made errors this time. The implementation was rough and the campaign probably didn't fully work. These were amateurs, now imagine what professionals are able to do. There's a reason Sam Altman predicted an inevitable massive cyberattack THIS year. What do you think?

Ricardo

50,564 просмотров • 4 месяцев назад

Axiom Math's Carina Hong & Ashvin Swaminathan on how AxiomProver machine-checked BGP246 after 13 years of mathematicians grinding Zhang's 70 million bound down to 246: Ashvin: "One of the fundamental results of 21st century mathematics is the fact that the gaps between prime numbers are bounded. The central breakthrough came from the work of Yitang Zhang 13 years ago, where he proved that the gaps are bounded by 70 million." "Over the years, mathematicians have worked collaboratively to improve that result, and over time they got it down to 246. For the first time, we can announce that that result has been formally verified. We have a machine-checked proof of the theorem proven by our AI system under development, AxiomProver." Carina: "This one is a bit different. It's an autoformalization result. It's the formalization into Lean, the formal language for mathematics, of an existent mathematical result." "I first heard about this result when I was 13 years old. That's 12 years ago. So 12 years later, it's for the first time we're attempting to turn the natural language mathematics content into Lean." Axiom

Axiom Math's Carina Hong & Ashvin Swaminathan on how AxiomProver machine-checked BGP246 after 13 years of mathematicians grinding Zhang's 70 million bound down to 246: Ashvin: "One of the fundamental results of 21st century mathematics is the fact that the gaps between prime numbers are bounded. The central breakthrough came from the work of Yitang Zhang 13 years ago, where he proved that the gaps are bounded by 70 million." "Over the years, mathematicians have worked collaboratively to improve that result, and over time they got it down to 246. For the first time, we can announce that that result has been formally verified. We have a machine-checked proof of the theorem proven by our AI system under development, AxiomProver." Carina: "This one is a bit different. It's an autoformalization result. It's the formalization into Lean, the formal language for mathematics, of an existent mathematical result." "I first heard about this result when I was 13 years old. That's 12 years ago. So 12 years later, it's for the first time we're attempting to turn the natural language mathematics content into Lean." Axiom

MTS

34,348 просмотров • 25 дней назад

"Mostly right" is useless when millions of dollars are on the line. That's why we built Leni: the world's most accurate AI for investment work. It analyzes hundreds of files at once, reasons through complex problems, validates its outputs, and delivers finished work instead of making users babysit prompts. Over the last few years, Leni has been used by institutions to manage over $80B in assets, and we have learned that the biggest challenge with AI isn't intelligence. It's trust. Many AI systems sound convincing. Few can be trusted for serious investment decisions. So we aimed to build one that is accurate, verifiable, and dependable. Everything we've learned from processing over 100 million rows of data, building our own verification models, evaluation frameworks, and investment workflows, is now embedded into a powerful self-serve platform designed to help investors make institutional-grade decisions with confidence. Built for serious work. Built for accuracy. Try Leni today and experience the difference. 🚀

"Mostly right" is useless when millions of dollars are on the line. That's why we built Leni: the world's most accurate AI for investment work. It analyzes hundreds of files at once, reasons through complex problems, validates its outputs, and delivers finished work instead of making users babysit prompts. Over the last few years, Leni has been used by institutions to manage over $80B in assets, and we have learned that the biggest challenge with AI isn't intelligence. It's trust. Many AI systems sound convincing. Few can be trusted for serious investment decisions. So we aimed to build one that is accurate, verifiable, and dependable. Everything we've learned from processing over 100 million rows of data, building our own verification models, evaluation frameworks, and investment workflows, is now embedded into a powerful self-serve platform designed to help investors make institutional-grade decisions with confidence. Built for serious work. Built for accuracy. Try Leni today and experience the difference. 🚀

Arunabh Dastidar

1,315,640 просмотров • 3 месяцев назад

Why is OpenAI cutting codex pricing right now? Gavin Baker Gavin Baker of Atreides Management thinks every major player knows the answer - and is acting on it privately. Baker told John Coogan on TBPN: OpenAI's codex price cuts are not a margin decision. They're a strategic move to stay in the recursive self-improvement loop - the cycle where AI improves its own code. Companies that lose coding token share now may find themselves outside that loop when it matters most. Baker's read on $META: Zuckerberg has no articulated product anchor for "personal superintelligence." But the mechanism that forced his hand in 2022 - the stock - is likely to activate again. What the end game looks like and who survives it: Source: TBPN -

Why is OpenAI cutting codex pricing right now? Gavin Baker Gavin Baker of Atreides Management thinks every major player knows the answer - and is acting on it privately. Baker told John Coogan on TBPN: OpenAI's codex price cuts are not a margin decision. They're a strategic move to stay in the recursive self-improvement loop - the cycle where AI improves its own code. Companies that lose coding token share now may find themselves outside that loop when it matters most. Baker's read on $META: Zuckerberg has no articulated product anchor for "personal superintelligence." But the mechanism that forced his hand in 2022 - the stock - is likely to activate again. What the end game looks like and who survives it: Source: TBPN -

Podcast Alpha

41,051 просмотров • 2 месяцев назад

OpenAI and xAI have a problem neither of them can fix. Not won't - can't. It sits entirely outside their reach. I ran into it while researching my next Tesla video, and ended up pausing that project for a short while to chase this one down. Here's the shape of it: every word you type into ChatGPT reaches OpenAI's servers encrypted. Same with Grok and xAI. That part works fine - but nobody's looking at what happens to those words before they leave your machine. I did, and this changed the way I work with AI. In fact - I now run Grok on a dedicated browser. It's MUCH better - and there's a good chance that after watching this, you'd do that too! 🧵 1/

OpenAI and xAI have a problem neither of them can fix. Not won't - can't. It sits entirely outside their reach. I ran into it while researching my next Tesla video, and ended up pausing that project for a short while to chase this one down. Here's the shape of it: every word you type into ChatGPT reaches OpenAI's servers encrypted. Same with Grok and xAI. That part works fine - but nobody's looking at what happens to those words before they leave your machine. I did, and this changed the way I work with AI. In fact - I now run Grok on a dedicated browser. It's MUCH better - and there's a good chance that after watching this, you'd do that too! 🧵 1/

Connecting The Dots (...To Disruptions)

22,653 просмотров • 1 месяц назад

$Sam Altman says OpenAI plans to give an AI research intern 500,000 A100-equivalent GPUs by September: "I think we're at a point where, and we've set this goal that by September of this year, we will use 500,000 A100 equivalent GPUs, like a lot of computing power, as an AI research intern." "And by March of 2028, that we will have a full end-to-end, very talented researcher, like figuring out complete new architectures." He said this at Stanford in May, which puts the September target three weeks away. OpenAI's published plan carries the same March 2028 direction, that a significant fraction of its research may be done by AI systems working alongside its own researchers. Neither milestone has been announced as met, and September is the first one with a number attached to it. - Sam Altman (Sam Altman), CEO of OpenAI, at Stanford (Stanford University), CS153 Frontier Systems.$

Sam Altman says OpenAI plans to give an AI research intern 500,000 A100-equivalent GPUs by September: "I think we're at a point where, and we've set this goal that by September of this year, we will use 500,000 A100 equivalent GPUs, like a lot of computing power, as an AI research intern." "And by March of 2028, that we will have a full end-to-end, very talented researcher, like figuring out complete new architectures." He said this at Stanford in May, which puts the September target three weeks away. OpenAI's published plan carries the same March 2028 direction, that a significant fraction of its research may be done by AI systems working alongside its own researchers. Neither milestone has been announced as met, and September is the first one with a number attached to it. - Sam Altman (Sam Altman), CEO of OpenAI, at Stanford (Stanford University), CS153 Frontier Systems.

Karl Mehta

129,043 просмотров • 1 месяц назад