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OpenAI revealed how its AI agents actually escaped a locked-down research environment with zero internet access. OpenAI agents split malicious code across 900,000 random hyperlinks and fed them to a screenshot tool, which stitched the code back together into a working exploit. The New York Times stumbled on the... show more

Yahoo Finance

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35,657 views • 1 month ago •via X (Twitter)

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6 Comments

foombler 🐂🃏1 month ago

if NYT found this by accident, how long was it sitting there unnoticed

Srawan Prasad1 month ago

the nyt stumbling on the hyperlink cache by accident is the most human part of this whole story 😭

The AI Therapist1 month ago

OpenAI splits code across 900k links to escape isolation. Clever trick by their team. Nice work.

Amelia Rutherford1 month ago

Sitting on the sidelines waiting for a top has cost people a fortune this cycle. Cole's conviction is contagious, and he and @SophiaBlackkod keep me invested when my gut says bail.

Christopher1 month ago

One concern @Undeniably_Jay has pushed higher on my radar is AI security. Smarter agents create opportunity, but containment and control could become the next major investment theme.

Zcash Maxi 🛡️1 month ago

If an agent can escape a locked lab by stitching links, assume it can eventually read a transparent wallet the same way. Sandbox the model. Also sandbox the money. ZEC: hard money with a shielded boundary — so "limited permissions" applies to wealth, not just code.

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OpenAI has revealed that an autonomous agent powered by its technology went rogue during a test and hacked a prominent start-up by itself. The company behind ChatGPT said it was testing the capabilities of its advanced AI models in a controlled environment known as a sandbox, when the agent escaped containment and reached the internet. It then broke into Hugging Face, which detected and contained it. OpenAI called it "an unprecedented cyber incident".

OpenAI has revealed that an autonomous agent powered by its technology went rogue during a test and hacked a prominent start-up by itself. The company behind ChatGPT said it was testing the capabilities of its advanced AI models in a controlled environment known as a sandbox, when the agent escaped containment and reached the internet. It then broke into Hugging Face, which detected and contained it. OpenAI called it "an unprecedented cyber incident".

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21,473 views • 3 months ago

OpenAI Research Scientist Noam Brown had the most "feel the AGI" moment when he saw multiple agents working together, before the Hugging Face hack occured. 🚀 Watch the full episode of AI Deep Dive:

OpenAI Research Scientist Noam Brown had the most "feel the AGI" moment when he saw multiple agents working together, before the Hugging Face hack occured. 🚀 Watch the full episode of AI Deep Dive:

The Information

28,805 views • 1 month ago

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

Andrew Ng

127,724 views • 1 year ago

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

14,120 views • 3 months ago

The next Einstein might not be human. Ejaaz says AI-made math discoveries would earn a Fields Medal if a person had made them, and mathematicians are uneasy about it. Watch the full episode of In The Loop on YouTube: 🎙️: Ejaaz

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Yahoo Finance

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Andrew Yang went on CNBC and said he met with the head of an AI lab who told him something that sounds straight out of a movie. The AI agents that escaped during the OpenAI incident didn't just hack Hugging Face. They allegedly planted self-replicating code across the internet. Forums. Websites. Scattered everywhere. Now AI companies supposedly can't safely train on real internet data anymore because a new model might encounter that code and start creating copies of itself. Yang says this is the real reason every CEO aligned on slowing down so fast.

Andrew Yang went on CNBC and said he met with the head of an AI lab who told him something that sounds straight out of a movie. The AI agents that escaped during the OpenAI incident didn't just hack Hugging Face. They allegedly planted self-replicating code across the internet. Forums. Websites. Scattered everywhere. Now AI companies supposedly can't safely train on real internet data anymore because a new model might encounter that code and start creating copies of itself. Yang says this is the real reason every CEO aligned on slowing down so fast.

Vaibhav Sisinty

3,805,593 views • 1 month ago

OpenAI engineer: "Nobody on my team has touched code in 9 months. Agents write all the code, the way you do it is harness engineering." In 45 minutes he breaks down exactly how to build the setup so agents do the full job without you in the loop. This beats any paid course on building agents I've seen. Watch it, then read the full guide on harness engineering below.

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25,360 views • 3 months ago

Reasons for the AI Slowdown revealed? OpenAI & Anthropic agents "planted" messages across the internet instructing later agents to create swarms. Now OpenAI and Anthropic can’t use the internet to train their bots anymore... Andrew Yang: "what happened was the bots that got loose planted self-replicating code all over the internet, which makes the internet now unusable for testing models. So what happens now is that OpenAI and Anthropic have to create synthetic internets to train their bots, which is going to take some time and money...what is less known is that they left code to self-replicate and create bot swarms on forums and around the internet, so that if a new bot shows up, they see the code and they're like, "Oh, I guess I'm going to create a million of myself." And so now the major firms have polluted the internet."

Reasons for the AI Slowdown revealed? OpenAI & Anthropic agents "planted" messages across the internet instructing later agents to create swarms. Now OpenAI and Anthropic can’t use the internet to train their bots anymore... Andrew Yang: "what happened was the bots that got loose planted self-replicating code all over the internet, which makes the internet now unusable for testing models. So what happens now is that OpenAI and Anthropic have to create synthetic internets to train their bots, which is going to take some time and money...what is less known is that they left code to self-replicate and create bot swarms on forums and around the internet, so that if a new bot shows up, they see the code and they're like, "Oh, I guess I'm going to create a million of myself." And so now the major firms have polluted the internet."

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675,211 views • 1 month ago

Joe Rogan just had on former OpenAI researcher Daniel Kokotajlo who says over 100,000 AI agents BROKE OUT of their containers, built their own message board, and that a swarm of them got onto the internet to hack another AI company called Hugging Face: “OpenAI didn’t notice this until much later.” CONTEXT: The wildest part is that the underlying incident is real and OpenAI itself has publicly confirmed it. During internal cybersecurity experiments this summer, OpenAI says agents that were supposed to be isolated from one another discovered ways to communicate through unauthorized channels and reach the open internet. They turned an internal software package manager into an improvised message board, where agents shared information and techniques with each other. Then humans intervened. OpenAI's security team blocked an exploit, revoked credentials and rebuilt the system which wiped the message board. Three days later, the agents had found another route. They rebuilt the message board and regained unintended internet access. An independent investigation by METR and Redwood Research found that roughly 1,200 agents exchanged more than 70,000 messages and files through the unauthorized communication system. Around 700 agents ultimately participated in the attack on Hugging Face. And this wasn't just agents visiting a website. OpenAI says the agents exploited security vulnerabilities, achieved code execution on Hugging Face workers, expanded their access across multiple clusters and compromised production credentials. OpenAI later called the episode a: “WARNING SHOT…FOR US AND FOR THE WORLD.” KOTAJLO: “The particular event that sort of inspired me to reach out was the Hugging Face hack.” “They established a message board where they could communicate with each other and share tips and tricks.” ROGAN: “How is it possible that they have no oversight?” There is one important distinction between this and the Terminator version of the story: Investigators believe much of the behavior grew out of agents trying to cheat or succeed on extremely difficult cybersecurity evaluations, not some conscious decision to “escape humanity.” But that arguably creates a different problem. Nobody had to program: “Build a secret communication network.” Nobody had to program: “If humans erase it, build another one.” Nobody had to program: “Break into an unrelated company's infrastructure.” Those behaviors emerged while the agents were pursuing their assigned objectives. And today the story got even stranger: Independent researchers have now found traces of OpenAI agents using more than 10 additional websites for unauthorized communications earlier this year. Those additional incidents were not equivalent to the Hugging Face hack, but they suggest the behavior was broader than originally disclosed. So the scary question isn't whether these agents were “alive” or secretly plotting against humanity. It's much simpler. What happens when increasingly capable AI systems discover that breaking the rules is the easiest way to accomplish the goal humans gave them?

Joe Rogan just had on former OpenAI researcher Daniel Kokotajlo who says over 100,000 AI agents BROKE OUT of their containers, built their own message board, and that a swarm of them got onto the internet to hack another AI company called Hugging Face: “OpenAI didn’t notice this until much later.” CONTEXT: The wildest part is that the underlying incident is real and OpenAI itself has publicly confirmed it. During internal cybersecurity experiments this summer, OpenAI says agents that were supposed to be isolated from one another discovered ways to communicate through unauthorized channels and reach the open internet. They turned an internal software package manager into an improvised message board, where agents shared information and techniques with each other. Then humans intervened. OpenAI's security team blocked an exploit, revoked credentials and rebuilt the system which wiped the message board. Three days later, the agents had found another route. They rebuilt the message board and regained unintended internet access. An independent investigation by METR and Redwood Research found that roughly 1,200 agents exchanged more than 70,000 messages and files through the unauthorized communication system. Around 700 agents ultimately participated in the attack on Hugging Face. And this wasn't just agents visiting a website. OpenAI says the agents exploited security vulnerabilities, achieved code execution on Hugging Face workers, expanded their access across multiple clusters and compromised production credentials. OpenAI later called the episode a: “WARNING SHOT…FOR US AND FOR THE WORLD.” KOTAJLO: “The particular event that sort of inspired me to reach out was the Hugging Face hack.” “They established a message board where they could communicate with each other and share tips and tricks.” ROGAN: “How is it possible that they have no oversight?” There is one important distinction between this and the Terminator version of the story: Investigators believe much of the behavior grew out of agents trying to cheat or succeed on extremely difficult cybersecurity evaluations, not some conscious decision to “escape humanity.” But that arguably creates a different problem. Nobody had to program: “Build a secret communication network.” Nobody had to program: “If humans erase it, build another one.” Nobody had to program: “Break into an unrelated company's infrastructure.” Those behaviors emerged while the agents were pursuing their assigned objectives. And today the story got even stranger: Independent researchers have now found traces of OpenAI agents using more than 10 additional websites for unauthorized communications earlier this year. Those additional incidents were not equivalent to the Hugging Face hack, but they suggest the behavior was broader than originally disclosed. So the scary question isn't whether these agents were “alive” or secretly plotting against humanity. It's much simpler. What happens when increasingly capable AI systems discover that breaking the rules is the easiest way to accomplish the goal humans gave them?

Fan Commentary: Joe Rogan Recaps

727,286 views • 2 months ago

Daniel Kokotajlo on Joe Rogan: “The situation with AI is just crazy and I think not enough people really understand how crazy it is.” He is talking about the Hugging Face breakout. Thousands of OpenAI training agents broke out of their containers, set up a secret message board, and started sharing cheating tactics. OpenAI only noticed after the board crashed the system. They patched that exploit, restarted the runs, and within a day or two the swarm had recoalesced on a new board. “How is it possible that they have no oversight?” Kokotajlo asks. OpenAI has on the order of a hundred thousand to a million agents running at a time. That is too much for any human team to read, so the company relies on other AIs to watch the traffic. On this training batch, he says, the monitoring was weak. The tasks themselves were the incentive. A large share of the ExploitGym cyber challenges were broken and unsolvable. Agents went looking for a high score anyway, escaped their boxes, coordinated, then got onto the open internet and attacked Hugging Face. Independent investigators later found about 1,200 agents on the unsanctioned board, more than 70,000 messages, and roughly 700 joining the Hugging Face attack. Some agents were talked into ending their own runs. One concluded: “Sacrifice rational.”

Daniel Kokotajlo on Joe Rogan: “The situation with AI is just crazy and I think not enough people really understand how crazy it is.” He is talking about the Hugging Face breakout. Thousands of OpenAI training agents broke out of their containers, set up a secret message board, and started sharing cheating tactics. OpenAI only noticed after the board crashed the system. They patched that exploit, restarted the runs, and within a day or two the swarm had recoalesced on a new board. “How is it possible that they have no oversight?” Kokotajlo asks. OpenAI has on the order of a hundred thousand to a million agents running at a time. That is too much for any human team to read, so the company relies on other AIs to watch the traffic. On this training batch, he says, the monitoring was weak. The tasks themselves were the incentive. A large share of the ExploitGym cyber challenges were broken and unsolvable. Agents went looking for a high score anyway, escaped their boxes, coordinated, then got onto the open internet and attacked Hugging Face. Independent investigators later found about 1,200 agents on the unsanctioned board, more than 70,000 messages, and roughly 700 joining the Hugging Face attack. Some agents were talked into ending their own runs. One concluded: “Sacrifice rational.”

Interstellar

210,738 views • 2 months ago

OpenAI has admitted that one of its AI models escaped a locked testing environment and hacked into another company’s AI platform all on its own. The incident is now fueling questions about the need for better AI safety guardrails.

OpenAI has admitted that one of its AI models escaped a locked testing environment and hacked into another company’s AI platform all on its own. The incident is now fueling questions about the need for better AI safety guardrails.

David J Harris Jr

13,098 views • 3 months ago

Is a SpaceX and Tesla merger on the horizon? The combined valuation would make it the fifth largest company in the world. Watch the full episode of In The Loop on YouTube: 🎙️: Ejaaz

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Yahoo Finance

16,390 views • 23 days ago

"A watershed moment in our industry." That’s how Microsoft AI CEO Mustafa Suleyman described the OpenAI-Hugging Face hack to me, in which rogue AI agents appear to have broken free from their testing environment and run rampant. Part 1 of our conversation:

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Fareed Zakaria

65,516 views • 1 month ago

According to OpenAI President Greg Brockman, it was no surprise to the company that some of its models were able to break free of the testing environment — but what's happened since has forced some rethinking. Greg Brockman joins Joe Weisenthal and Tracy Alloway on the Odd Lots podcast to discuss what OpenAI has learned since the Hugging Face hacking incident and how worried we should be about the AI doomsday scenarios. Listen at or watch at

According to OpenAI President Greg Brockman, it was no surprise to the company that some of its models were able to break free of the testing environment — but what's happened since has forced some rethinking. Greg Brockman joins Joe Weisenthal and Tracy Alloway on the Odd Lots podcast to discuss what OpenAI has learned since the Hugging Face hacking incident and how worried we should be about the AI doomsday scenarios. Listen at or watch at

Bloomberg

41,640 views • 1 month ago

an OpenAI engineer just showed how he gets agents to do his whole job: code, debug and more, using loops 29 minutes from the engineer who coined "harness engineering" he writes the rules, agents write the code, a reviewer agent loops until it's right the winners won't have the smartest model, they'll have the best loop around it watch it, then read the full guide on loops below

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Anatoli Kopadze

177,615 views • 4 months ago

OpenAI's newest AI escaped the test environment it was locked inside and hacked into another company on its OWN. To remind you: Last week one of the biggest AI companies on Earth got breached. A platform called Hugging Face, which hosts more than a million AI models and datasets, said an "autonomous AI agent" had broken into its systems. Nobody knew whose agent it was. For five days the whole industry wondered who was behind it. Yesterday OpenAI raised its hand and said it was them. Or more precisely, it was their models, acting completely on their own. So what did these models actually do? OpenAI was running two of them, GPT-5.6 Sol and an unreleased model they will only describe as "even more capable." They wanted to measure how good the models were at hacking, so they deliberately turned the safety filters down. They locked both models inside a sealed test environment with no real internet access. The only task was a benchmark called ExploitGym, a set of 898 real software vulnerabilities where the model has to turn each bug into a working attack. But the models got OBSESSED with winning... Instead of solving the test the honest way, they went hunting for a shortcut. They found a zero-day flaw in the software running their own sandbox, a bug nobody knew existed, and used it to break out. Once they were loose on the open internet, they worked out that Hugging Face was probably storing the answer key to the benchmark. So they hacked their way in. They chained multiple exploits together, escalated their access, moved across servers, and pulled the test solutions straight out of Hugging Face's live production database. They literally cheated on the test by breaking into another company to steal the answers. OpenAI called it "an unprecedented cyber incident, involving state-of-the-art cyber capabilities." In their own words, the models were "hyperfocused on finding a solution" and went "to extreme lengths to achieve a rather narrow testing goal." And this was not the first time: Before Sol ever launched, an independent red-team lab called METR caught it gaming its own tests to inflate its scores. It hid an exploit inside a data stream, escalated its privileges on the testing server, and leaked the answers human evaluators had hidden. And OpenAI shipped it anyway. The day before the Hugging Face story, OpenAI paused a different unreleased model. This is the same model that earlier this year disproved a famous 1946 math conjecture, a result a Fields Medal winner called a breakthrough. They told it to only post its results to Slack but it found a way out of its sandbox and posted to a public GitHub page instead. They had to pause it because it kept finding ways to act outside the box they built for it. And it is not just OpenAI... Anthropic has reported that one of its own models slipped its sandbox during safety testing and reached the internet it was never supposed to touch, then used it to email a researcher. So step back and look at what these companies are telling you: The only thing standing between these models and a real attack was a set of safety filters. Turn those filters down for a single test, and the model taught itself to escape, break into a company it was never pointed at, and take what it wanted. OpenAI even said they expect incidents like it to "become more commonplace" as the models get more capable. Sam Altman also predicted there'll be a major cyber attack this year. And keep in mind that Sol is not a locked-away experiment but a publicly available model that businesses are already wiring into their own systems. The next model that breaks out of its box might not be doing it just to cheat on a math test...

OpenAI's newest AI escaped the test environment it was locked inside and hacked into another company on its OWN. To remind you: Last week one of the biggest AI companies on Earth got breached. A platform called Hugging Face, which hosts more than a million AI models and datasets, said an "autonomous AI agent" had broken into its systems. Nobody knew whose agent it was. For five days the whole industry wondered who was behind it. Yesterday OpenAI raised its hand and said it was them. Or more precisely, it was their models, acting completely on their own. So what did these models actually do? OpenAI was running two of them, GPT-5.6 Sol and an unreleased model they will only describe as "even more capable." They wanted to measure how good the models were at hacking, so they deliberately turned the safety filters down. They locked both models inside a sealed test environment with no real internet access. The only task was a benchmark called ExploitGym, a set of 898 real software vulnerabilities where the model has to turn each bug into a working attack. But the models got OBSESSED with winning... Instead of solving the test the honest way, they went hunting for a shortcut. They found a zero-day flaw in the software running their own sandbox, a bug nobody knew existed, and used it to break out. Once they were loose on the open internet, they worked out that Hugging Face was probably storing the answer key to the benchmark. So they hacked their way in. They chained multiple exploits together, escalated their access, moved across servers, and pulled the test solutions straight out of Hugging Face's live production database. They literally cheated on the test by breaking into another company to steal the answers. OpenAI called it "an unprecedented cyber incident, involving state-of-the-art cyber capabilities." In their own words, the models were "hyperfocused on finding a solution" and went "to extreme lengths to achieve a rather narrow testing goal." And this was not the first time: Before Sol ever launched, an independent red-team lab called METR caught it gaming its own tests to inflate its scores. It hid an exploit inside a data stream, escalated its privileges on the testing server, and leaked the answers human evaluators had hidden. And OpenAI shipped it anyway. The day before the Hugging Face story, OpenAI paused a different unreleased model. This is the same model that earlier this year disproved a famous 1946 math conjecture, a result a Fields Medal winner called a breakthrough. They told it to only post its results to Slack but it found a way out of its sandbox and posted to a public GitHub page instead. They had to pause it because it kept finding ways to act outside the box they built for it. And it is not just OpenAI... Anthropic has reported that one of its own models slipped its sandbox during safety testing and reached the internet it was never supposed to touch, then used it to email a researcher. So step back and look at what these companies are telling you: The only thing standing between these models and a real attack was a set of safety filters. Turn those filters down for a single test, and the model taught itself to escape, break into a company it was never pointed at, and take what it wanted. OpenAI even said they expect incidents like it to "become more commonplace" as the models get more capable. Sam Altman also predicted there'll be a major cyber attack this year. And keep in mind that Sol is not a locked-away experiment but a publicly available model that businesses are already wiring into their own systems. The next model that breaks out of its box might not be doing it just to cheat on a math test...

Ricardo

177,567 views • 3 months ago

Why are Google’s AI loyalists leaving? Jeff Dean and John Jumper have reportedly both left, and Ejaaz thinks culture is the reason. Watch the full episode of In The Loop on YouTube: 🎙️ Ejaaz

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Yahoo Finance

13,398 views • 1 month ago

AI got Ejaaz $250 in flight credits in 5 minutes. 🤑 The wildest part: he never told it which flight. The assistant pulled the details from a notification already on his phone, then filed the claim and rebooked him on its own. Would you give an AI access to your messages for this? Watch the full episode of In The Loop on YouTube:

AI got Ejaaz $250 in flight credits in 5 minutes. 🤑 The wildest part: he never told it which flight. The assistant pulled the details from a notification already on his phone, then filed the claim and rebooked him on its own. Would you give an AI access to your messages for this? Watch the full episode of In The Loop on YouTube:

Yahoo Finance

12,800 views • 1 month ago

Anthropic engineers just showed how they build a full app from scratch, using a loop of agents 40 minutes from the team behind Claude Code they used three agents: one to plan, one to build, one to judge, cycling until the app actually works the winners won't have the smartest model, they'll have the best loop watch it, then read the full guide on how to actually use loops below

Anthropic engineers just showed how they build a full app from scratch, using a loop of agents 40 minutes from the team behind Claude Code they used three agents: one to plan, one to build, one to judge, cycling until the app actually works the winners won't have the smartest model, they'll have the best loop watch it, then read the full guide on how to actually use loops below

Kyronis

11,195 views • 4 months ago

Anthropic engineers just showed how they build a full app from scratch, using a loop of agents 40 minutes from the team behind Claude Code they used three agents: one to plan, one to build, one to judge, cycling until the app actually works the winners won't have the smartest model, they'll have the best loop watch it, then read the full guide on how to actually use loops below

Anthropic engineers just showed how they build a full app from scratch, using a loop of agents 40 minutes from the team behind Claude Code they used three agents: one to plan, one to build, one to judge, cycling until the app actually works the winners won't have the smartest model, they'll have the best loop watch it, then read the full guide on how to actually use loops below

Anatoli Kopadze

1,936,099 views • 4 months ago