Meta is betting big on AI. But will CEO Mark Zuckerberg’s investment in the technology ultimately pay off? That’s one of the major questions lingering after Wednesday’s earnings call, which announced Meta’s second-quarter loss of over $4.6 billion in its Reality Labs sector.

Throughout the hour-long call, Zuckerberg spoke about Meta’s business across its slate of social media apps – Facebook, Instagram, Threads and WhatsApp – and the overall message he kept driving home was the role of AI within the company and for the people using Meta’s products daily.

At the center of all this is MuseSpark, the AI model created by Meta’s Superintelligence Labs. With it, Zuckerberg said Meta is hard at work on making a variety of AI agents to streamline workflows for small businesses and enhance the lives of individual Meta users.

“Soon we will have agents that can work 24/7 on your behalf to help you achieve your goals and improve your life, your health, your relationships, your finances, whatever you want,” Zuckerberg said.

If you’ve been paying attention to all the Zuckerberg news that’s fit to print, you may know that Meta’s CEO has been building a personal AI assistant for himself.

Considering his hardline stance on making AI available to all (see his New York Times interview for more on this), it makes sense that he’d extend this technology to the billions of people who use Meta’s apps worldwide.

Privacy is a major concern when it comes to AI models, whether for business or personal use. Zuckerberg said during the call that Meta is making “strong privacy and security a fundamental part of the agents” it is building.

In the same breath, though, he pointed to Meta’s Ray-Bans smart glasses as the ideal product to deliver this functionality to people — the same tech item that many on social media have begun calling “pervert glasses.”

Instagram is cracking down on content captured with the glasses, but this use case raises a broader concern about how people’s information will be used by these proposed AI assistants if Zuckerberg’s big bet on superintelligence for all pays off.

More than 1 million businesses have been using Meta business agents on WhatsApp and Messenger, Zuckerberg said Wednesday. Instagram is next for this type of agentic AI, which learns over time what customers need and delivers those insights back to the business to help shape overall strategies.

This is a “business-in-a-box service” that Meta is aiming to build, which can help you start and run your whole business using the service’s platforms.

The primary goal, repeated throughout the earnings call, is to put superintelligence directly into people’s hands rather than centralize it.

“We are focused on distributing it widely and giving everyone the ability to direct it towards what matters to them,” Zuckerberg continued. “That’s the way that society has always made progress, and I think these are the right values for building a positive AI future.”

What does that future look like, exactly? According to Meta’s CEO, billions of people will have a personal agent that is locked in on their goals and works nonstop to help you acheive them.

“I get that this is sort of a big bet across the industry,” he said, in what could be viewed as a bit of an understatement.

As part of Meta’s earnings report, Reality Labs — the part of the company that develops its VR headsets and smart glasses — generated $431 million in revenue, while its operating loss widened to $4.6 billion.

Since 2020, the division has racked up total operating losses of $80 billion. In response to this loss, which comes two days after Meta’s new investment to build a data center campus in El Paso, Texas, Meta’s stock price dropped by 10% by the end of Wednesday.

“There’s a bit of similarity to Meta’s metaverse missteps in that Meta is once again spending ahead of proven product demand,” Forrester Research Director Mike Proulx told CNET. “The difference is that AI adoption and value are real. The technology is different, but the market’s patience is starting to look familiar.”

Zuckerberg seemed unfazed by the loss during the earnings call, acknowledging the reactions to Meta’s spending push on AI.

“My personal bet is that the people who invest in this are going to be rewarded and feel very good over time.”

Overall for the second quarter, Meta announced revenue of $60.8 billion, up 28%, and net income of $15.8 billion, down 14% from a year earlier. Total costs and expenses were $42 billion, an increase of 55%.

Aaron covers what’s exciting and new in the world of home entertainment and streaming TV. Previously, he wrote about entertainment for places like Rotten Tomatoes, Inverse, TheWrap, Washington Post and The Hollywood Reporter. Aaron is also an actor and stay-at-home dad, which means coffee is his friend.

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A hot potato: The developer behind popular Windows optimization tool Wintoys has uncovered a sophisticated cybercrime operation that mimics dozens of popular Windows apps through duplicate websites built to look uncannily like the real thing. The fraudulent sites reportedly distribute malware capable of compromising user privacy, hijacking account credentials, and draining crypto wallets.

The developer, who goes by the handle Bogdan_X, told Windows Latest that his research began after he spotted a fake website mimicking the official Wintoys page. Tracing the site’s registration led him to 72 fraudulent websites built on the same playbook, several of which use URLs deliberately close to the real domains they’re impersonating.

Shameless plug: it’s exactly this kind of scheme that makes vetted download sources worth the extra click. Every installer in TechSpot’s Downloads section is pulled straight from the developer, scanned for malware, and kept free of bundled junk under our clean-downloads policy, so you’re not left guessing whether an “official” link actually leads where it says it does.

A broader version of this scheme was mapped in detail last month by cybersecurity firm Check Point Research, which found that the fake sites work to rank highly on Google and other search engines for searches tied to popular Windows software, then use that trust to push malware once traffic starts flowing. It’s not yet confirmed whether the same operator is behind both the Wintoys-focused cluster and the wider set Check Point tracked, or whether separate groups are simply running the same playbook.

The real grpcurl project on GitHub shows up right next to grpcurl[.]com, one of the impersonation domains Check Point identified, in Google search.

In the early stages, the sites link to legitimate downloads. Once a site gains traction, though, operators quietly swap those real links out for fraudulent ones. Many of these fraudulent domains are registered to the same owner and even reference genuine upstream resources, like real GitHub repositories, making them harder to tell apart from the legitimate projects they’re copying.

Some of the sites load a JavaScript staging layer via Amazon CloudFront that intercepts clicks on Download buttons and reroutes the connection through a Traffic Distribution System (TDS). The TDS then sends users to either malware-hosting infrastructure or legitimate resources, depending on their location, browser type, and other signals.

Apart from Wintoys, other popular Windows programs being impersonated this way include PowerToys, CrystalDiskMark, EasyBCD, Lively Wallpaper, and more. Check Point’s own mapping of the wider ecosystem also turned up cloned pages for security and research tools such as Ghidra, dnSpy, and SpiderFoot, a reminder that even power users aren’t immune to fraudulent apps and websites.

Check Point’s researchers tied the infrastructure to several malware families. SessionGate, an obfuscated multi-stage loader with heavy anti-analysis defenses, was used mainly to quietly install unwanted software. Two other branches of the same TDS led to more damaging payloads: RemusStealer, an infostealer believed to be a variant of the notorious Lumma Stealer family, and AnimateClipper, crypto-stealing malware that reads a device’s clipboard to swap copied wallet addresses for attacker-controlled ones.

VirusTotal submitter counts for a handful of flagged installers – combined, the totals run well past 5,000.

The scale of the operation shows up clearly in VirusTotal telemetry. Researchers logged more than 5,000 total submissions across relevant samples in just the publicly shared subset, and say the real number of infections is likely significantly higher. The earliest samples date back to August 2025, meaning the operation had been running for close to a year before it was uncovered.

With malware becoming a growing problem across every platform, downloading apps from reputable, vetted sources matters more than ever. TechSpot’s Downloads section hosts safe, clean, unaltered installers pulled directly from developers, and every file is scanned for malware and rechecked daily, so you can grab what you need with peace of mind.

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. ®

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.”

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