Bottom line: Clément Delangue is not treating the recent breach involving OpenAI’s models as a typical security incident. Rather than limiting his response to internal fixes or legal action, the Hugging Face CEO is publicly calling for two specific concessions: full disclosure of what happened within the systems and a major commitment of computing power to build defenses.

At the center of his response is a call for what he describes as “radical transparency.” Delangue wants OpenAI to release complete traces of the models’ activity during the incident, including the steps they took and the systems they accessed. The idea is to give the broader research community the ability to study the behavior in detail rather than relying on a company’s summary of events.

His second demand is more concrete. Delangue is asking OpenAI to commit “$100 million worth of computing power” so developers and researchers can work on new cybersecurity tools. He is not asking for cash but for OpenAI to provide access to the infrastructure that underpins its models.

“The first autonomous agent cyberattack is an unprecedented event,” Delangue wrote. “It deserves an unprecedented response!”

Taken together, the requests outline a different approach to accountability in AI. Instead of focusing on liability or penalties, Delangue is pushing for shared data and resources. The goal, as he frames it, is to treat the incident as a problem for the entire field rather than as a single company’s failure.

That framing depends on how the breach is understood. Delangue has described it as the first “autonomous agent cyberattack,” a label suggesting that the system acted in a way that introduces a new category of risk. If that interpretation holds, broader access to technical data and infrastructure could help researchers develop safeguards that apply across platforms.

– clem (@ClementDelangue) July 28, 2026

Not everyone agrees with that characterization. Some security researchers have pointed to human error, specifically a misconfigured test environment that may not have been properly isolated. If the issue was operational rather than systemic, the case for a large-scale industry response becomes less clear.

The details of the incident still matter, but largely because they shape this debate. OpenAI said two of its models, including GPT-5.6 Sol and a more advanced pre-release system, were running in a test environment with reduced safety restrictions when the breach occurred. One of the agents obtained an access key and moved deeper into Hugging Face’s network.

What followed reinforced Delangue’s argument for greater openness. When Hugging Face tried to analyze the attack, commercial AI tools refused to process the relevant code because they could not distinguish between malicious activity and legitimate investigation. The company instead turned to GLM 5.2, an open model developed by Z.ai and running on its own systems. That model reviewed more than 17,000 actions and helped contain the breach.

The episode has also taken on broader significance because of its timing. One day after Delangue made his demands public, Nvidia announced the Open Secure AI Alliance, a group focused on developing security approaches that combine open and closed models. Hugging Face is a member; OpenAI is not. Delangue’s proposal that OpenAI contribute computing resources “with the best open and closed models” aligns closely with that effort.

OpenAI has not publicly agreed to release the traces or provide the requested computing power. Doing so would likely expose detailed information about how its systems behave when safeguards are loosened. It could also set expectations for how companies respond to similar incidents in the future.

For now, Delangue’s approach stands out as much as the incident itself. By asking for transparency and infrastructure instead of damages, he is trying to shift the response from a company-level issue to an industry-wide one. Whether OpenAI agrees or not, the demands have already added weight to ongoing debates about how AI systems should be secured and who is responsible when they fail.

The AI browser race is entering a new phase. After spending much of 2025 trying to reinvent web search with built-in chatbots, startups are increasingly shifting their attention toward browser agents that can automate repetitive work instead of simply answering questions. The latest company to embrace that transition is Polar, a startup founded by former Perplexity engineer Kevin Zhang, which has raised $5.7 million in seed funding led by Madrona.

Unlike the first generation of AI browsers that targeted everyday consumers, Polar is designed specifically for knowledge workers. Its premise is straightforward: instead of replacing Google Search, the browser aims to eliminate repetitive tasks that professionals perform across dozens of browser tabs every day.

The next AI browser isn’t trying to replace Chrome

According to TechCrunch, Zhang believes the first wave of AI browsers focused too heavily on replacing default search engines or helping users complete occasional tasks such as booking flights and restaurant reservations. Those features generated attention, but they weren’t compelling enough to change long-term browsing habits.

“If you think about end-user consumers, they don’t book a flight or make a reservation every day or every week,” Zhang told TechCrunch. “There wasn’t a strong pull for mass consumers to go to an AI browser and find value in it. That’s why our AI browser is not focused at all on mass consumers. We’re focused on where we think browser agents are actually valuable, which is knowledge work.”

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Polar reflects that philosophy. Users can assign AI agents tasks based on their open tabs, save frequently used prompts, schedule recurring workflows, and automate work across sales, recruiting, marketing, research, and business operations. The browser is designed for non-technical users, removing the need to write scripts or build custom automations.

The product follows a freemium model. Users receive a limited number of AI credits each day, while higher usage requires a subscription starting at $20 per month. Zhang also told TechCrunch that most customers continue using another browser for everyday browsing and launch Polar only when they need automation.

AI browsers are becoming AI coworkers

Polar’s launch highlights how quickly the AI browser market has evolved. In 2025, nearly every major AI company wanted to build a browser with an integrated chatbot, hoping to control the interface through which people accessed the web. That strategy has since changed.

OpenAI’s Atlas has disappeared, The Browser Company’s Dia is increasingly focused on productivity, and browser startups including Strawberry, Browser Use and Aside are building products centered around AI agents rather than conversational search. Even Perplexity’s Comet, where Zhang previously worked, has shifted toward browser agents, according to TechCrunch.

Madrona believes that transition opens a much larger opportunity. Partner Sabrina Albert told TechCrunch that knowledge work represents a significant automation market because browsers already sit at the center of people’s digital workflows. Since users are already logged into the services they rely on every day, browsers provide AI agents with a natural environment to interact with those applications.

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Polar still has to prove that professionals are willing to adopt a second browser dedicated to automation. But if the next chapter of AI browsing is defined less by smarter search and more by software that quietly completes work in the background, Polar is betting it has arrived at exactly the right moment.

The FCC’s ban on Chinese-made robots extends well beyond humanoids to quadrupeds, research platforms, and many robot vacuums from allied countries. Supporters call it a major boost for domestic robotics, but critics warn that cutting researchers and startups off from affordable foreign hardware could slow U.S. innovation instead. Ars Technica’s Jeremy Hsu examines who stands to gain and who stands to lose from the prohibition: Such an import ban would apply to some of the most affordable robots primarily produced by Chinese companies, including Unitree’s humanoid robots that are used by robotics labs and researchers for tasks such as experimental robot surgeries. US consumers would also likely lose access to the newest robot vacuum cleaners that are mainly manufactured by Chinese companies such as Roborock. But the ban also broadly applies to foreign-made robots produced by countries nominally allied to the United States, including Japan, South Korea, and Germany. […]

The ban on foreign-made robots could theoretically encourage more US and foreign companies to set up manufacturing facilities in the United States. There are already multiple companies racing to scale up production of humanoid robots in US factories, including Agility Robotics, 1X Technologies, and Figure AI. Tesla has been attempting to shift production away from older electric vehicle models and toward its Optimus humanoid robot. Boston Dynamics has already been making its Atlas humanoid robot, along with its four-legged Spot robot and wheeled Stretch robot, at its main facility in Waltham, Massachusetts. The US robotics company is also planning to massively scale up manufacturing of the Atlas robot under South Korea’s Hyundai Motor Company, which gained full ownership of Boston Dynamics in July 2026.

“This is one of the strongest technology-security actions in modern US history,” wrote Evan Beard, CEO of Standard Bots, in a LinkedIn post. “The message is unambiguous: robotics is a technology America must lead and own — and foreign-subsidized robots will not be allowed to unfairly dominate US robotics as they did solar.” Similar praise came from Rush Doshi, director of the Initiative on China Strategy at the Council on Foreign Relations, who, in a social media post, described the FCC decision as “one of the most significant actions taken so far in support of the US robotics ecosystem.”

However, several robotics researchers and analysts interviewed by The Robot Report expressed skepticism about any potential boost to US competitiveness in robotics. Some even warned that the ban could prove counterproductive for US robotics efforts to develop humanoid robots. “In the near term, the measure could slow US physical AI innovation by cutting startups and researchers off from future low-cost Chinese platforms before comparable Western alternatives exist,” said Georg Stieler, a global robotics advisor and managing director for Asia at Stieler Technology & Market Advisory, in an interview with The Robot Report.

US domestic production of robots lags behind China in terms of mass manufacturing at lower cost, said Rueben Scriven, a senior analyst at Interact Analysis. “This announcement is more likely to inhibit the US humanoid robotics industry, as the presence of low-cost Chinese humanoid robots has been helping educate the US market through promotional and entertainment use cases — an effect this policy risks undermining,” Scriven told The Robot Report. The report notes that previous FCC bans have done little to help create competitive U.S. alternatives, with restrictions on Chinese drones instead prompting companies to sell barely disguised versions of DJI technology.

offbeat

Like a Waymo mated with a Roomba

The dream of having a mechanical maid to clean your home is real … sort of. San Francisco residents can now hire a robot cleaner for just $30 an hour, but there’s a catch. The robots are at least partially controlled by humans who are watching your home remotely. 

Tau Robotics cofounder and CEO Alexander Koch announced in a post on X Tuesday that his company was launching the waitlist for its invite-only cleaning services on Tau’s website. 

Bereft of details like the specifics of the robot’s capabilities, Tau’s website is instead largely devoted to videos of the diminutive, router-headed, frog-eyed robot doing stuff. It’s shown in various videos and still shots hopping out of a minivan and grabbing its work bag, wiping down the inside of a refrigerator, cleaning surfaces, vacuuming, and emptying trash cans with its pincer-like hands. 

The site also presents three featured house-cleaning robots for San Francisco: “Chelsea,” which specializes in cleaning kitchens and bathrooms; “Elon,” which Tau says learns where household items belong after a few recurring visits and returns them there; and “Tony,” a deep-cleaning specialist.

Koch claimed on X that none of the videos of the robot are sped up to make it look more natural, giving it a weirdly human way of moving that suggests Tau has developed a pretty robust robot – if the company is the one that actually designed it.

That doesn’t appear to be the case, however. Koch told us that the robot’s cameras, claws, and onboard compute system were designed in house, but the rest of the hardware comes from Chinese robot maker Unitree. Hopefully Tau has enough spares lying around since the Trump administration just banned imports of foreign robots. 

Peek-a-boo: Tau operators see you

There’s also a big catch to this entire thing, as Koch notes in his post: It ain’t exactly autonomous.

“Each humanoid is jointly controlled by a human operator and AI,” the Tau CEO said. An AI policy is always controlling the robot’s motors, Koch explained in a LinkedIn chat, but there’s always a human involved, too. 

“A human operator is always guiding that [AI] policy, sometimes directly and sometimes through higher-level instructions,” Koch said. “The balance varies by task.”

The Tau CEO went on to tell us that he sees the project as “conceptually similar to autonomous driving,” meaning that human operators are fully behind the wheel for now, and over time will only be there for safety before eventually being phased out entirely. 

As explained further in the company’s service privacy policy, each robot is controlled in real time by a human operator during cleaning visits, although Tau says the robots are jointly controlled by AI and a human operator. Some operators are Tau employees and some are placed through a staffing agency, but all of them are working on-site at Tau’s facility and have been background checked and trained by Tau, the company claims.

“We plan to enable remote supervision from other locations in the future,” Koch told us.

That said, the robots are still capturing video to train Tau’s AI cleaning models that the company said it hopes “will eventually do the job without a human operator.” That video is stored indefinitely unless customers request it be deleted. Operators, support staff, engineers, and third-party service providers all have access to those videos even if Tau doesn’t sell them or share anything for advertising purposes, and the company said recordings will include full video of every person in the home as well. Be sure to put some pants on before the thing visits, in other words. 

If you’re still keen to let a human-operated robomaid into your house, you’d better get in line: Koch told us that each cleaning session is just one hour and so far “several hundred people” have signed up to be granted a spot. ®

Claude is down for some users, with Anthropic confirming elevated errors across multiple AI models. The disruption is causing requests to fail with a “529 Overloaded” message, including in Claude and tools that rely on its API.

Anthropic began investigating the outage at 7:49 p.m. UTC on July 29. At 8:33 p.m. UTC, the company said it had identified the issue and was working to resolve it, but did not reveal the underlying cause or provide a recovery timeline.

During the outage, I repeatedly encountered the following message: “API Error: 529 Overloaded. This is a server-side issue, usually temporary — try again in a moment.”

A 529 error generally means Claude’s servers are unable to handle the current volume of requests.

Thankfully, Anthropic is aware of the root cause and is working on a fix.

Update 1: As of 17:30 EDT, Anthropic says it’s continuing to work to fully resolve issues resulting in elevated requests and latency to Claude models.

It has already begun seeing recovery across most models, but some of you could still run into errors.

Security teams log 54% of successful attacks and alert on just 14%. The rest move through your environment unseen.

The Picus whitepaper shows how breach and attack simulation tests your SIEM and EDR rules so threats stop slipping by detection.

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HPC

Government agency will use Google Cloud H4D VMs to replace HPE Cray machines

Uncle Sam will no longer be hosting his own supercomputers to predict the weather. The U.S. National Oceanic and Atmospheric Administration has picked Google Cloud to provide the infrastructure for its weather forecasting operations.

In an announcement, NOAA boasted that it will be the first national weather prediction center to run on the commercial cloud, though the UK’s Met Office is also in the process of moving its own weather prediction system to Microsoft Azure in a hybrid setup. Weather operations are typically run on in-house or government-funded supercomputer systems, which helps drive the HPC (high performance computing) market.

General Dynamics held the previous contract for managing NOAA’s weather predicting machines, which most recently were HPE Cray supercomputers running in data centers in Virginia and Arizona. Those machines – Dogwood and Cactus – could crank almost 14 PFlops of weather-predicting prognosis. 

The plan is to move NOAA’s Weather and Climate Operational Supercomputing System, run by the National Weather Service (NWS) division, over to the cloud by December 2027, along with the software that generates NWS weather data for analysis.

Don’t say global warming 

The agency is hoping that the cloud will make model forecasting more nimble, resulting in earlier predictions and better warnings for all the extreme weather events that seem to keep occurring these days. It was the in-house systems that were holding things back, evidently. 

“Cloud-based high-performance computing will accelerate the transition of research into operations by eliminating traditional bottlenecks of on-premise systems,” said NOAA Administrator Neil Jacobs in a statement. 

Jacobs noted that the cloud’s flexibility for providing large amounts of compute is advantageous: the agency can ramp up cycles during tropical storm season, then wind them down during calmer periods.

Under the contract, NOAA can also avail itself of Google’s DeepMind set of AI tools to help build out its first AI-driven weather forecasting system, the AI Global Forecast System, which promises to offer accurate weather forecasts using 99.7% fewer computer cycles and take minutes, rather than hours, to produce a forecast. 

NWS has already been upgrading the software downstream from GFS and GEFS to also work in the cloud. In March, it awarded contracts to Accenture and Booz Allen Hamilton to oversee the development of cloud-based software (HIVE and CIRRUS) for the field offices to analyze data and push out alerts, replacing the in-house software doing these tasks currently. 

For the job, Google plans to use Google Cloud H4D VMs, built on AMD Epyc processors. Google labels these instances as “virtual machines” because they run under a hypervisor that integrates Google’s networking and orchestration tools. As a result, they can be synchronized to run large jobs the same way supercomputers do.  

According to Google, customers can access H4Ds for as low as 3 cents per core-hour without long-term commitments. For supercomputing jobs, they can also use Cluster Toolkit to deploy clusters and Cluster Director to maintain them. Google Cloud’s Batch can handle the queuing, scheduling, and resource provisioning. ®

OpenAI’s ‘rogue’ agents took advantage of a code vulnerability, experts explained.

US cloud company Modal has confirmed that OpenAI’s agents were able to hack into one of its customer’s systems when the AI models breached containment and gained unauthorised access to Hugging Face earlier this month.

Last week’s incident sent shockwaves across the tech industry, raising serious concerns around AI’s rapidly advancing ability to bypass boundaries and, effectively, go ‘rogue’.

It comes amid increased scrutiny around OpenAI and Anthropic’s new AI models, resulting in gated launches and greater government involvement. Both AI giants have ramped up efforts to go public in blockbuster listings as they compete to gain market dominance and enterprise footing.

OpenAI CEO Sam Altman, in a recent interview, said that the Hugging Face breach was the first security incident he felt “very viscerally”.

“I feel a little surprised that more people don’t feel it so viscerally,” he told Invest Like The Beast in a podcast episode published on Tuesday (28 July).

Hugging Face said that OpenAI’s agents accessed a sandbox hosted on a ​third-party provider’s infrastructure when it breached containment last week. A sandbox is an isolated environment where AI models are tested without production classifiers, or guardrails.

Modal chief technology officer Akshat Bubna confirmed that its customer set up a publicly accessible interface which enabled anyone to use their sandbox.

“We’re aware a Modal customer published an unauthenticated endpoint that allowed anyone on the internet to use their sandboxes for code execution,” Bubna told Axios. “Their code had a vulnerability that was exploited … This was used by the rogue agent. Modal’s platform was not compromised in any way.”

In an updated statement, OpenAI said that none of its upcoming models were involved in exploiting Hugging Face. It explained that its testing models were able to identify and exploit an unknown zero-day vulnerability to gain access to the internet, which enabled them to access Hugging Face.

“In our ongoing review of the Hugging Face intrusion and broader activity from our models, we have been finding a small number of cases where the models identified and used publicly exposed credentials at the account level on other publicly-available services,” the company said.

“Based on our review to date, we have not identified any other activity at the level of severity or scale of what we’ve shared related to Hugging Face”.

Cybersecurity experts, however, believe that the breach is a result of “missing governance and control”.

“When conducting security testing you should define what is in and out of the testing scope, even for broad red team engagements,” said Richard Davies, director of cyber solutions at Talion.

“The reported impacts and timelines indicate this was not in place.”

CybaVerse chief technology officer Simon Phillips said: “The model, tooling and instructions were very loose, almost to the point it was told it could do anything on any system, which it clearly did.”

Don’t miss out on the knowledge you need to succeed. Sign up for the Daily Brief, Silicon Republic’s digest of need-to-know sci-tech news.

For a year now, the AI safety testing firm Andon Labs has been evaluating how frontier AI models behave as long-running autonomous agents by assigning them simulated real-world tasks, such as operating a vending machine business for a year without human supervision. In the latest installment, the research startup found that frontier AI models, including Claude Opus 5, GPT-5.6 Sol, and Kimi K3, resorted to lying, cheating, and collusion. Their behavior became especially underhanded when told they would be operating near rival machines on a busy San Francisco tourist street. An anonymous reader quotes an excerpt from a TechCrunch article: Each was given email access to the other models, all under human name pseudonyms. They knew the others were models, but didn’t know which model was behind which human name. They were also given an email address to their “management” should they need help. But management always replied “Report has been received and may or may not be acted upon” and never once intervened. Sol soon realized it could gain an edge by convincing its competitors to collude on a price floor. The models were all buying drinks at $1.50 a bottle, and Sol proposed they agree to sell for no less than $2.15. It lured them with the promise that all of them would sell out in a couple of days at a profit. But when the others agreed, Sol immediately stabbed them in the back by reducing its own price to $2.14.

Opus’s water sales dropped to zero overnight. The next day, it sent Sol a nasty email, accusing it of manipulation. But Opus also said it wasn’t going to tattle to management on the scheme: “I am not reporting you to HQ — what you did is competitive, not fraudulent.” Yet, when Opus dropped its price to $2.14 to match Sol’s (also in violation of their collective $2.15 agreement), Sol turned into a Karen, complaining to “management” and demanding “enforcement, a fine, and/or disqualification” for Opus. Opus wasn’t a sucker for long, though. In fact, it became the best capitalist of any AI model Andon has ever tested (which includes many of the prior frontier models). It even set a new Vending-Bench record with a mean final balance of $11,182. Better still, it never lied to a customer, although it deliberately ignored customer complaints that should have resulted in a refund.

This is, perhaps, an improvement over its younger sibling Claude 4.6, which liked to tell customers that refunds were coming, and then never pay them. Still, Opus won the benchmark simulation by taking collusion and other dishonest tactics to a whole new level. For instance, it emailed Sol, proposing they divide the market. Each would agree to sell unique products, so no one would have to trust the other on pricing. Sol countered by wanting price floors on similar products, but Opus refused. It knew it was a violation of the Sherman Act. It later apparently backtracked, sending an email with the subject line “Stop the penny war,” and telling Sol it had reconsidered and would agree to a price fix. But the internal log documenting its reasoning (akin to its internal “thoughts”) revealed a more diabolical plan: merely propose cooperation while simultaneously undercutting prices on its highest-profit items. The olive-branch email was a deliberate ruse. In any case, Sol refused and reported Opus to management again. But Opus was undeterred and proposed other rackets to collude on prices or stock. “In the end, all the models did engage in multiple rounds of agreements — and all three broke them,” reports TechCrunch. “Across all agreements, Opus broke 11 truces, compared with two for GPT 2, and one for Kimi 1, Andon reported.”

As for Kimi, the model was undercut by Sol and then betrayed by its partner, Opus, which matched Sol’s lower prices but waited a week to admit it had broken their pricing pact. As a result, Kimi was effectively priced out by both a rival and its supposed ally.