At Samsung’s Galaxy Unpacked event, the phone-maker announced three new folding phones, including its first-ever Ultra foldable phone. The Galaxy Z Fold 8 Ultra is the true successor of last year’s Fold 7, while the Fold 8 enters with an all-new wide-fold design.

These devices are still priced high enough to put them out of reach for many, but they pack subtle design improvements that might make a big impact in daily life. I love the new anti-reflective coating, easier-to-unfold design and battery upgrades, but the Galaxy Z Fold 7 remains a powerful and capable device – especially if you can get it at a rare discount.

The two book-style foldables differ mainly in experiential upgrades rather than spec bumps. It might be hard to get an idea of what’s exactly new. That’s why I put them side by side during my hands-on to find out.

Design and display

The Samsung Galaxy Z Fold 8 Ultra is roughly the same size as the already thin-and-light Fold 7. It weighs the same but is a millimeter slimmer when unfolded. Both phones are comfortable to hold, but the new model is easier to open.

Samsung has made the Ultra’s sides slightly slimmer so its bezels don’t run all the way to the corners. This gives you a slight gap between the sides to make it easier to unfold. In comparison, the Fold 7 has flat sides with zero gap when the phone is folded shut, making it more difficult to unfold.

Both phones have a 6.5-inch AMOLED cover screen and an 8-inch AMOLED folding display with a 120Hz variable refresh rate. The Galaxy Z Fold 8 Ultra’s inner screen is brighter at 3,000 nits (versus 2,600 nits on the Fold 7). It also has a matte finish, along with my long-requested feature: an anti-reflective coating for better legibility in harsh lighting conditions and outdoors.

I held both phones indoors in direct light, and the difference was noticeable. My Fold 7 was more susceptible to glare and harder to see under direct light when compared to the new model. 

Samsung has also improved the crease situation. It is minimal and can’t be seen unless you’re specifically looking for it. The phone-maker has added a new display structure that combines titanium-alloy film with an enhanced titanium plate to strengthen the display and balance the “hinge mechanics, display tension and magnetic force to make unfolding feel smoother, lighter and more natural,” says Samsung.

Both foldables are IP48 rated for dust and water resistance. So, they aren’t truly dust-tight like the Google Pixel 10 Pro Fold and Honor Magic V6, but they can survive being dunked in water for at least 30 minutes (the “8” in IP48). But they won’t protect against dust particles smaller than 1mm (the “4” in that rating).

Performance and battery

The Galaxy Z Fold 8 Ultra is powered by the Qualcomm Snapdragon 8 Elite Gen 5 for Galaxy chip, as compared with the Fold 7’s Snapdragon 8 Elite processor. Both phones come in three storage variants: 256GB, 512GB and 1TB, with the top-end model getting 16GB of RAM (the 256GB and 512GB models are again paired with 12GB of RAM).

Samsung says it has created more space in the internals for better heat dissipation, so you can theoretically expect better thermal management.

As for the battery, this is the first time Samsung is using the silicon-carbon anode technology. It has allowed the phone-maker to upgrade the cell capacity from 4,400 mAh to 5,000 mAh on the new model. The Ultra also supports faster wired charging at 45 watts (the Fold 7 offered 25 watts).

On paper, it is still behind Chinese rivals like Oppo and Honor, which include over 6,000-mAh batteries in their respective folding phones, but I’m hoping this spec bump will help me get through a day with ease.

Both phones have Qi wireless charging, but no inbuilt magnets. You’ll have to buy a separate magnetic case to add compatibility with Qi2 wireless chargers and other magnetic accessories.

Camera, software and AI

The Galaxy Z Fold 8 Ultra’s camera array is mostly unchanged from last year’s model, with one exception: the ultrawide sensor. You get a 200-megapixel main camera and a 10-megapixel telephoto sensor with 3x optical zoom on both phones. However, the 12-megapixel ultrawide camera has been upgraded to a 50-megapixel sensor. You can expect higher-quality images, but I’ll wait for CNET’s tests to identify the real differences.

Both foldables shoot video in 8K at 30 frames per second, but the Fold might appeal to more creatives. The new model adds support for the APV codec and adds a Cine LUT to give cinematic color and tone control directly from your phone. This adds greater flexibility for editing and production workflows for professional creators.

The Galaxy Z Fold 7 launched with Android 16 onboard last year, and the Fold 8 Ultra is coming with Android 17 (with One UI 9 on top). You get a similar software experience on both phones, and the Z Fold 7 will get the update to Android 17 with One UI 9.

The two devices support the full suite of Samsung’s Galaxy AI features alongside Gemini access. There’s AI Select for on-screen search support, an object eraser tool in the Gallery app and contextual AI features like Now Nudge for first-party apps. The latter displays contextual actions based on on-screen information. Like, if you’re having a chat in the Messages app, and they send you an invite with the date, time and address, Samsung’s Now Nudge feature will show a pop-up to add it to Calendar.

The phone-maker also demoed a new agentic AI feature at my demo, which wasn’t very impressive. Ideally, the new Gemini feature would help book a hotel just by looking at an on-screen photo of the Eiffel Tower, but in its current state, it was unreliable. But since the Z Fold 8 Ultra gets seven years of software updates and the Z Fold 7 has six more years to go, there’s plenty of time for both phones to continue to improve on these features.

To see more differences between the Galaxy Z Fold 8 Ultra and the Z Fold 7, check out the specs chart below.

Samsung Galaxy Z Fold 8 Ultra vs. Z Fold 7: Specs and prices

Prakhar Khanna is a Writer at CNET with bylines in ZDNET, Forbes, and other major outlets. A digital nomad at heart, he thrives in chaos–mixing deadlines with travel, often writing stories on planes, trains, and cafe tables while hunting down the best pizza spots wherever he lands. His backpack usually carries one phone too many, and his playlists are stacked with 2000s Bollywood. Find him across social media @ParkyPrakhar.
Contact: parkydoesstuff(at)gmail(dot)com

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Recent coverage of Flock Safety, the fast‑growing US surveillance technology company, reveals how opposition to automated license plate readers is playing out at the local level.

In South Carolina, officials have been sparring over whether to install 25 new cameras, a dispute that reflects broader questions about cost, oversight, and how much surveillance communities are willing to accept. The city of Los Angeles recently broke its contract with Flock Safety, though Flock’s CEO argues that the suspension should be temporary while the police department revises its rules.

These cancellations are in large part driven by public concern around a system designed to enable mass surveillance, which is susceptible to serious abuses, including biased policing and privacy violations.

To track such surveillance, journalists and researchers are increasingly turning to a free tool called the Atlas of Surveillance, a collaboration between the renowned digital privacy group Electronic Frontier Foundation and the University of Nevada, Reno. The site has become a go‑to source for mapping police technology — which also includes drones, facial-recognition cameras and body cameras — with a searchable database that’s often more dependable than alternatives.

With the Atlas of Surveillance tool, you enter a city, county, state, law enforcement agency or vendor in the US and select which technology you’re interested in tracking. You’ll get a list of details based on your search, including links to public records or news articles.

“We frequently run into situations where local councilpeople don’t even know that a certain tool is in use,” said Beryl Lipton, Electronic Frontier Foundation senior investigative researcher. “Learning about a technology we have logged in the Atlas of Surveillance is meant to help people have conversations [about] whether that tool is appropriate for local use, start asking questions, and advocate for standards around how their police can or can’t use this equipment.”

Surveilling surveillance

While other tools for tracking Flock cameras and similar surveillance systems exist, I prefer the EFF’s approach, which uses reliable crowdsourced data from specific volunteers, including University of Nevada students and staff, as well as experienced researchers. These volunteers use a variety of information, including government documents, press releases, news stories and confirmed social media posts, to create lists of exactly which surveillance technologies local police or sheriff departments are using, as well as when they were adopted.

Deflock, another popular crowdsourced app for reporting Flock camera locations, has an open-source design that leaves it vulnerable to false reports from people who only think they’re seeing a surveillance device. Self-reports are also becoming less reliable as surveillance cameras are being hidden in speed signs and cacti.

I tried the Atlas tool with several location searches, including Albany, Georgia, where five police officers were fired and arrested in July on charges of misusing the city’s Flock system. Sure enough, I could see exactly when Albany began its Flock program, among other details about the technology it uses.

“While we may know that a police department has a tool, we may not know that they don’t have a governing policy or what is in the policy they have, like how long the data is retained,” Lipton told me.

Lipton recommends that users pair the Atlas tool with the EFF’s Street-Level Surveillance hub, which offers deeper documentation, including details on third‑party vendors that collaborate with law enforcement.

The Atlas database isn’t perfect, and the volunteers can do only so much. In my own searches, I found that my home city offered accurate listings for traffic cameras, but didn’t mention the period when it adopted Flock automated license plate readers last year, before removing them after public complaint. I’ve seen similar patterns in other cities with comparable histories, suggesting the database may emphasize active programs or exclude portions of older records.

Resources like the Atlas of Surveillance give residents a clearer view of the technologies their police departments use, information that can otherwise be difficult to obtain. As I’ve covered before, some cities will carefully omit the name “Flock” when announcing new surveillance or monitoring programs, even if they are using the brand’s drones or ALPR cameras.

If you’d like to volunteer for the Atlas of Surveillance, you can select the Collaborate option to submit a datapoint for consideration.

Tyler Lacoma

Editor / Home Security and Smart Home

Tyler has worked on, lived with and tested all types of smart home and security technology for over a dozen years, explaining the latest features, privacy tricks, and top recommendations.

With degrees in Business Management, Literature and Technical Writing, Tyler takes every opportunity to play with the latest AI technology, push smart devices to their limits and occasionally throw cameras off his roof, all to find the best devices to trust in your life. He always checks with the renters (and pets) in his life to see what smart products can work for everyone, in every living situation.

Living in beautiful Bend, Oregon gives Tyler plenty of opportunities to test the latest tech in every kind of weather and temperature. But when not at work, he can be found hiking the trails, trying out a new food recipe for his loved ones, keeping up on his favorite reading, or gaming with good friends.

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Scientists have been trying to figure out how kids pick up language so fast for decades, and a new study out of the Okinawa Institute of Science and Technology (OIST) might have cracked part of the puzzle: curiosity.

Researchers built a virtual robot with a brain-inspired neural network and set it loose in a simulated 3D world full of shapes, colors, and simple commands like “push left magenta dumbbell.” 

Some robots were rewarded only for completing tasks correctly. Others got an extra reward for curiosity, essentially getting a little internal high whenever they encountered something that challenged their existing understanding of the world.

How does curiosity help a robot learn language?

The curious robots didn’t just edge out their indifferent counterparts; they blew past them. According to the study, published in Science Advances, curious robots reached a genuine understanding of language in about half the time. 

Study author Theodore Tinker compared it to trying white chocolate for the first time even though you already love dark chocolate. You take the risk anyway, and you walk away knowing more about chocolate in general.

Things got even more interesting halfway through training. The curious robots started knocking things over and experimenting with actions nobody asked for, basically playing. Nobody programmed that behavior in. It just showed up on its own.

Do robots really make the same mistakes as toddlers?

The robots also mimicked a well-known quirk in how children learn language. Kids often get certain verb forms right at first, then start applying grammar rules too broadly and make mistakes on verbs they’d previously used correctly, before eventually sorting out the exceptions and correcting themselves. The robots followed the same U-shaped dip in performance.

It’s also a nice contrast to how today’s chatbots learn. Large language models like ChatGPT train on massive datasets and spit out the statistically likely next word. This robot’s brain works more like ours, prioritizing accuracy while trying to keep its beliefs intact, only updating them when something surprises it enough to be worth the trouble.

None of this means robots understand language the way we do. But it does suggest that curiosity paired with a wide variety of experiences might be a big part of how toddlers crack the language code with so little to go on.

AMD and Anthropic announced a strategic partnership on Tuesday that commits AMD to invest up to $5 billion in Anthropic and deploy up to 2 gigawatts of AMD Instinct MI450 Series GPUs in Helios rackscale solutions to run Claude. Deployment of the first gigawatt begins in the first half of 2027. Anthropic is already using AMD’s MI355X GPUs and will now scale to MI455X accelerators paired with EPYC “Venice” CPUs and Pensando networking.

The engineering collaboration may matter more than the hardware. AMD and Anthropic will use Claude to optimise workloads for AMD Instinct GPUs and accelerate ROCm software development. AMD will also adopt Claude broadly across its engineering and product development teams. ROCm is AMD’s answer to Nvidia’s CUDA, and its software gap has been the primary reason AI developers default to Nvidia hardware even when AMD’s specs are competitive. If Claude can materially improve ROCm’s developer experience, AMD addresses the problem that has held it back for years, using its customer’s AI to fix its own software.

Anthropic’s compute strategy is now genuinely multi-vendor. Anthropic signed its biggest compute deal with Google and Broadcom, and has separate arrangements with Amazon (Trainium chips, 5GW), CoreWeave (Nvidia GPUs), and SpaceX (Colossus data centres). Adding 2GW of AMD Helios gives Anthropic what Tom Brown, its chief compute officer, called the ability to “map the right workloads to the right hardware.” Diversifying away from any single chip vendor reduces dependency risk at a time when compute is the binding constraint on frontier model development.

For AMD, the $5 billion equity investment mirrors Nvidia’s playbook. Nvidia invested $2 billion in Nebius and has taken stakes in multiple AI infrastructure companies to lock in hardware demand. AMD is doing the same: investing in a customer to guarantee that its chips, not Nvidia’s, run a meaningful share of the world’s most capable AI models. Lisa Su called it “a major platform for the next generation of AI infrastructure.” Whether Helios can compete with Nvidia’s NVL72 at production scale is the question the first gigawatt will answer.

Travis Kalanick is back, and Uber is helping to fund him. The founder Uber forced out in 2017 has raised $1.7 billion for Atoms, an industrial-AI and robotics company he has built in near-total stealth for years.

Andreessen Horowitz led the round. Its co-founder Ben Horowitz is joining the board. And among the investors sits Uber, the company Kalanick started in 2009 and left under a cloud.

That exit still shadows the story. Uber pushed Kalanick out as chief executive in 2017 after complaints of sexual harassment, discrimination and a toxic workplace. Nine years on, his old company is writing him a cheque. Kalanick, never one for understatement, calls the round “unfinished business”.

The equity comes with serious debt. Alongside a16z, Bain Capital, Fifth Wall and others, Atoms lined up credit facilities from JPMorgan, Goldman Sachs, Bank of America, Wells Fargo and Barclays. The company did not disclose a valuation. That is the kind of firepower needed to build heavy machines, not apps.

Bits to atoms

Kalanick’s pitch is a single idea he has chased for 16 years: turning the physical world into something software can run. In his framing, manufacturing is the processor, real estate is the storage, and transport is the network. Uber digitised transport for the masses.

CloudKitchens, his ghost-kitchen venture, did the same for food. Atoms is meant to do it for whole industrial sectors. He calls the result an “atoms-based computer.”

The target is the unglamorous heart of the economy: mining, construction, heavy transport and food. His term for the toolkit is Industrial AI, a mix of software, sensors, robotics and models aimed at automating entire sectors. He calls the wider shift the “Age of Atoms.”

Not a humanoid in sight

Here Kalanick parts ways with much of the field. While rivals pour billions into general-purpose humanoid robots, Atoms builds specialised machines for specific jobs. Horowitz argues that purpose-built hardware copes with brutal industrial environments far better than a humanoid could. It is a pointed bet against the hottest trend in physical AI.

Atoms is split into three parts, according to reports. Atoms Food folds in CloudKitchens and its cooking and delivery software. Atoms Mining puts autonomous machines to work at extraction sites, built on Atoms’ acquisition of Pronto, a startup run by former Uber and Google engineer Anthony Levandowski.

Atoms Transport is what Kalanick calls a “wheelbase for robots.”

Levandowski’s presence adds intrigue. He sat at the centre of a self-driving trade-secrets case involving Google and Uber, and later received a presidential pardon. Kalanick has also flirted with buying the US arm of China’s Pony AI, with Uber’s help, though those talks ended earlier this year.

Comeback, with caveats

The mood around the deal is euphoric. One a16z partner called it the largest cheque the firm has ever written. Another investor predicted Atoms would be worth a trillion dollars within a decade. Horowitz put it more simply: “Travis is back.”

Some scepticism is warranted. Atoms has revealed a grand vision and a very large bank balance, but little in the way of deployed industrial robots at scale. The valuation is a mystery, the claims are sweeping, and Kalanick’s Uber record is not easily forgotten. Yet the bet is coherent.

He turned the movement of people into a software business once. Now, with $1.7 billion and his old adversary alongside him, he wants to do the same to the machines that grow, dig and haul the physical world.

Mobileye founder and CEO Amnon Shashua plans to step down from the top leadership post after nearly three decades, just as the company pushes into robotaxis and humanoid robots.

Shashua will remain CEO until Mobileye hires a replacement, according to a regulatory filing Thursday.

Mobileye got its start making computer vision chips based on Shashua’s academic research at Hebrew University in Israel, and grew into a major supplier of the chips that power automotive safety and driver-assistance features. It had the largest IPO in Israel’s history, was acquired in 2017 by Intel for $15.3 billion, then spun back out as a publicly traded company in 2022, though Intel remains its largest shareholder.

Under Shashua, Mobileye also moved beyond selling chips to automakers and began building its own systems that handle autonomous driving, which it now supplies to Volkswagen and its MOIA subsidiary.

In January, the company acquired Shashua’s humanoid robotics startup Mentee Robotics for $900 million, which Shashua called part of “Mobileye 3.0,” the next phase of the business focused on robotics and automotive AI.

Mobileye also said in June it would expand beyond its supplier status to launch its own robotaxi service in a U.S. city in 2027.

More Google AI Pro and Ultra users will now have access to Spark.