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New York sues Valve for promoting illegal gambling via game loot boxes

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New York Attorney General Letitia James sued video game developer and publisher Valve Corporation for using game loot boxes to facilitate illegal gambling activities among children and teenagers.

Valve operates Steam, one of the largest digital game distribution services in the world, offering access to thousands of games for millions of users worldwide. At the time this article was published, Steam was reporting over 29 million players online, with nearly 7.5 million playing a game.

Attorney General James said the gaming giant is violating the state’s gambling laws by offering players the opportunity to win random virtual prizes that can be exchanged for real money, in a process described as being similar to a slot machine.

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“Illegal gambling can be harmful and lead to serious addiction problems, especially for our young people,” said James. “Valve has made billions of dollars by letting children and adults alike illegally gamble for the chance to win valuable virtual prizes. These features are addictive, harmful, and illegal, and my office is suing to stop Valve’s illegal conduct and protect New Yorkers.”

The lawsuit targets loot boxes in Counter-Strike 2, Team Fortress 2, and Dota 2 that award players with random items, such as weapon skins or character accessories. However, the odds of winning rare items are allegedly deliberately skewed by Valve to make them far more valuable, leading the total value of market items to balloon to an estimated $4.3 billion as of March 2025, according to Attorney General James.

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Some individual items (such as AK-47 skins) have even fetched prices of over $1 million, making Steam accounts a frequent target for hackers and scammers.

The lawsuit also highlights the potential harm to children, as they may be drawn into loot box purchases to win rare items and boost social status within gaming communities. “Children who are introduced to gambling are four times more likely to develop a gambling problem later in life than those who are not,” according to research cited in the Wednesday press release.

Attorney General James has asked the court to permanently bar Valve from operating loot box features in the state, to require the company to return all profits generated by the practice, and to impose fines for the alleged violations.

In January 2025, Genshin Impact developer Cognosphere (aka Hoyoverse) agreed to pay $20 million to settle a U.S. Federal Trade Commission (FTC) lawsuit over unfair marketing of loot boxes to minors, obscuring the actual costs. andmisleading the players about the odds of winning prizes.

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BleepingComputer reached out to a Valve spokesperson for comment, but a response was not immediately available.

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Investigation: over 75% of Android VPNs fail basic transparency tests

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  • 77% of Android VPNs studied fail basic accountability and transparency tests.
  • These 2,666 apps have amassed over 2 billion downloads combined
  • 43% of the apps lack a usable website for troubleshooting or product research.
  • 63% rely on non-proprietary email addresses (such as Gmail)
  • 54% of apps have a substandard or inaccessible privacy policy

If you’re looking for a VPN for your Android device, it pays to be skeptical. Exclusive research by TechRadar has found that 77% of VPNs on the Google Play Store raise significant transparency and accountability concerns. And given these apps handle your sensitive browsing data, that’s a major red flag.

Of the 3,471 Android applications that claim to protect user privacy, we found that 2,666 have significant flaws.

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Bluehost VPS review | TechRadar

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We spend hours testing every product or service we review, so you can be sure you’re buying the best. Find out more about how we test.

If you feel it is time to upgrade your shared hosting to one of the best VPS providers, Bluehost looks like a good place to start. With its managed VPS offering, you potentially get the benefit of the performance boost that comes with a VPS without the headache of supporting the website software yourself.

To find out if Bluehost’s managed VPS option is as attractive as it looks, we’ve evaluated it, comparing the plans and pricing options, the server infrastructure, and features. We also assessed performance of Bluehost VPS hosting with benchmark tools, and spent some time with its site building tools, which are aimed at anyone launching a small business website.

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Universal Pre-K Is a Hot Policy Idea. But What About Kindergarten?

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Even casual observers of the early childhood space likely noticed the massive push for expanding access to care and education programs over the last year, most notably with universal preschool options.

But a less splashy effort has been quietly underway for years: making kindergarten mandatory, enrolling the small percent of children holding out from the entry-level grade in order to boost their academic and emotional success.

Enrolling children in kindergarten is only legally required for families in 20 states, though every state makes it mandatory for public schools to offer the entry-level grade to students. Students in those states can also complete kindergarten in private school or through homeschool, instead.

The mandate has gained momentum slowly over several decades, most recently in California, Michigan, New Jersey and Louisiana, though only the latter two ultimately passed new laws.

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But as state leaders grapple with dwindling funds for early childhood education, and with the spotlight shining on the more popular push for universal preschool, the future of mandatory kindergarten remains murky.

“I bet there are lawmakers who don’t even know it’s not mandatory,” says Hanna Melnick, director of early learning policy at the Learning Policy Institute.

The Push for Kindergarten

The purpose of kindergarten has shifted over the years. Once a haven for educational play, kindergarten classrooms now tend to emphasize academic work. Regardless, educators and experts use it as a way to identify whether students have the social-emotional, language and motor skills they need for elementary school. Plenty of studies prove that enrolling in kindergarten reaps long-term rewards, both academically and socially, particularly for lower-income and minority students.

Those benefits are often mentioned by lawmakers looking to make kindergarten mandatory.

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For example, Detroit Public Schools Community District Superintendent Nikolai Vitti said in 2024 that mandatory kindergarten could decrease student absenteeism in addition to increasing student achievement. That measure failed to pass, though the state instead launched its expansive PreK for All initiative that same year.

“Any time a group of kids are being underserved, it’s not good for the kid or family,” Christina Weiland, a professor of education and public policy at the University of Michigan, says.

“But for the teacher, if students are placed in first grade and they are behind, it places more demand on teachers on how to get every kid to the same place.”

Even states without technically mandated kindergarten have workarounds. Florida, for example, does not mandate kindergarten for all students, but for a student to enroll in a public school first-grade classroom, having completed kindergarten is a prerequisite. New Jersey leaves it up to individual school districts, and some require completing the grade while others do not.

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Alabama in 2024 passed legislation requiring children who did not attend kindergarten to pass the “First Grade Readiness Assessment” in order to enroll directly into first grade. The test is being administered for the first time this school year. Those who do not pass will be required to attend kindergarten.

“This new law will ensure students are truly prepared to enter the first grade,” Alabama state representative Pebblin Warren, who has pushed for this legislation since 2019, said in a statement. She added that she hoped it would help even the playing field for students and their teachers, and help with future school retention.

Comparing Costs

California’s policy history offers a case study about the push and pull between investing in mandatory kindergarten versus other public early learning programs.

In California, 5 percent of families do not enroll their children into kindergarten. That adds up to about 200,000 kids sitting out.

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In 2024, a bill was put forth to legally mandate students attend kindergarten before entering first grade. As of now, 6-year-olds must attend school, and it is up to parents whether to enroll them in kindergarten or first grade.

California’s proposal made it through the state House and Senate before Gov. Gavin Newsom vetoed the bill, pointing toward the $268 million it would cost annually as too high a price tag.

However, starting in the 2022-2023 school year, similarly to in Michigan, Newsom approved California’s transitional kindergarten program, which sought to increase access to public education programs for 4-year-olds. In the most recent budget, Newsom proposed $1.8 billion in additional funding for expanding the state’s transitional kindergarten program, which effectively serves as universal pre-K.

Financial cost is one big factor as officials weigh which kinds of early learning programs to support. Sometimes the pain of big upfront bills seem to outweigh the potential longer-term payoff for society, says Emma Garcia, a principal researcher at the Learning Policy Institute.

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“I feel like sometimes the argument used against it is, ‘Oh, it costs a lot and the effects fade,” she says. “But it’s what society gains from the early investment.”

There’s also the political “costs” of passing new regulations mandating participation in school.

“Offering a service tends to be fairly popular; requiring it tends to be less so,” Sarah Novicoff, a research fellow at the Public Policy Institute of California, says. “It’s all about questions of priorities, about what the state particularly thinks will make change in the most impactful way and there’s trade-offs to all these things.”

Today’s political climate favors “parental choice,” both in the ideological sense of parents knowing what is best for their children, and in the literal sense via school vouchers.

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“Family choice has always been embedded in any child care policy,” Jade Jenkins, an associate professor of education at the University of California Irvine who has studied the effects of mandatory kindergarten since 2015. “And with the conservative sentiment dominating the landscape these days, which is parental choice and the push toward educational choice for school-aged children, nationalizing or any kind of early childhood educational mandates are further off.”

It’s not obvious that even families who do participate in kindergarten always value it fully, at least according to attendance records. According to the American Enterprise Institute, 1 in 3 California kindergarten students were chronically absent, or missed 10 percent or more of a school year.

If families put less stock in kindergarten, it might be because of the reality that, in many places, only half-day programs are available. According to the Education Commission of the States, a nonprofit tracking education policy, only 16 states and Washington D.C. require schools to offer all-day kindergarten options, with the remainder mandating half-day offerings.

Without that all-day offering, many parents are left in the lurch for half of the work day.

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“It’s not just about parent choice: They offer half day, and you often have to pay for full day [care], which is a real access problem where policies could make a difference,” Weiland says. “A push toward offering full day is probably more meaningful, at least on the equity side.”

Potential Wins and Roadblocks

It turns out that the two policy ideas — offering universal pre-K and mandating kindergarten — may lead to the same place. Some experts posit that expanded pre-K could help place students on the elementary public school track earlier.

After all, Weiland says, “I’ve never heard of doing universal pre-K and then not kindergarten; that’s not too much of a common path, at least.”

That seems especially likely in areas like Washington, D.C., and Boston, where universal preschool programs are embedded in public school settings (as opposed to offered at standalone centers or in-home programs).

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“In a mixed-delivery system, we have no reason to believe this would make me stay in public school, but in places like Boston where it’s highly regarded in the public schools, we have found they are somewhat more likely to stay in public schools,” Jenkins says.

And that could help in a small way with the enrollment issues schools have encountered since the pandemic. While school enrollment rates for 5-year-olds are high — 84 percent across the country, according to the National Center for Education Statistics — they began dipping postpandemic, down 6 percent for 5-year-olds from 2019 to 2021.

These days, education leaders are also worried about longer-term demographic and birth rate changes primed to hurt schools, such as “the fertility cliff and the enrollment cliff,” Jenkins says. For institutions that are funded based on a per-pupil method of calculation, that means fewer dollars.

Weiland pointed toward states like Vermont, Maine and West Virginia that have all been hit particularly hard with enrollment dips and had to close down schools.

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“We have these school enrollment crises, where the birth cohorts are getting smaller, and it doesn’t make great financial sense for kindergarten classrooms to go under-enrolled,” she says. “That could have some political momentum to increase enrollment numbers.”

For schools trying to stay open, every additional kindergartener helps.

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Samsung Galaxy S26 skips magnets, but they still charge faster and get new gear

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If you were betting on Samsung finally baking built-in magnets into the Galaxy S26 series this year, well — you lost. For the second year running, Samsung has skipped native Qi2 magnetic hardware across the entire S26 lineup.

No MPP (Magnetic Power Profile), no satisfying snap onto your wireless charger. Just a phone that sits on a pad and hopes for the best with alignment. The reason? Space, apparently.

Samsung didn’t include wireless charging magnets due to “space constraints”

At the Unpacked event, when asked why the Galaxy S26 still misses out on magnets, a Samsung executive told Digital Trends that it was probably due to space constraints. They didn’t go deep into the engineering process behind it all, but ruled out that there were any thermal concerns behind the decision.

So the internals are just too busy to fit a ring of magnets. Sure, Samsung. We believe you. I’d say that Google’s decision to finally include wireless magnetic charging on the Pixel 10 series — dubbed Pixelsnap charging — gives the series a clear edge in terms of daily charging convenience.

Here’s where things get genuinely better — on paper, at least. Wireless charging speeds are up across the lineup: the S26+ now tops out at 20W, and the Ultra goes all the way to 25W Qi2. The base S26 is still stuck at 15W, but the jump for the two bigger models is real and welcome.

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Faster wireless charging — on supported models — is a bit confusing

The catch? Those faster speeds come with strings attached. Specifically, a magnetic case. Without one, your phone is limited to whatever the Qi spec’s Extended Power Profile allows — which is 15W, full stop.

Anything beyond that sits in Magnetic Power Profile territory, and that standard physically needs magnets to lock alignment and pull higher wattage from the charger. No magnets on the phone means no alignment means no extra speed (via 9To5Google).

Samsung’s own 25W wireless charger is itself a magnetic puck, which tells you everything — it barely works without a magnetic case in the picture.

At least you get new magnetic cases and charging gear

To Samsung’s credit, the company isn’t leaving buyers stranded. It launched a fresh lineup of first-party magnetic cases alongside the S26 series — silicone options with Qi2 magnets built inside, a transparent patterned “Rugged” variant, and a returning clear case.

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Samsung also dropped a new Magnet Wireless Charger (a slim 4.4mm Qi2 puck with a fabric USB-C cable, currently on sale for $34.99 from $49.99) and a 5,000mAh magnetic power bank with a kickstand.

Third-party brands like Belkin, ESR, and dbrand have piled in too, with magnetic S26 cases that bring full MagSafe-style snap-on functionality to the table.

Is it a little absurd that you need to buy a case just to use a charger at full speed? Absolutely. But at least the ecosystem around that workaround is now genuinely good.

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Easily Replaceable USB-C Port Spawned By EU Laws

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The USB-C port has become a defacto connectivity standard for modern devices, largely supplanting the ugly mess of barrel jacks and micro USB connectors that once cursed us. While their reliability is good, they don’t last forever, and can be a pain to replace in most devices if they do fail. However, a new part from JAE Electronics could change that.

The problem with replacing USB connectors in most hardware is that they’re soldered in place. To swap them out, you have to master both desoldering and soldering leads of a rather fine pitch. It’s all rather messy. In the interest of satisfying the EU’s new Ecodesign for Sustainable Products Regulation (ESPR), JAE Electronics has developed a USB-C connector that’s easier to replace. Rather than being soldered in, the part is simply clamped down on to a printed circuit board with small screws. As the part is torqued down, small gold-plated contacts are compressed into pads on the PCB to make the necessary contact.

The connector is fully compatible with USB 4 version 2.0 (don’t ask us how they number these things anymore). It comes in single and dual connector versions, and is capable of USB PD EPR at up to 240 W (5A/48V). The part does have some drawbacks—namely, the footprint of the metal-shelled part is somewhat larger than most soldered USB C connectors. Whether this precludes its use is very much an application-specific matter for product engineers to decide.

In any case, if you find yourself designing hardware with heavily-used USB C ports, you might find this part useful. It’s not widely available yet, but some parts should be landing at Mouser in coming months. We’ve explored some of the ways USB-C connectors can be fouled and damaged before, too. Sound off with your opinions on this new part in the comments.

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Thanks to [James] for the tip!

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Xiaomi’s latest wireless charger is just 6mm thick – thinner than much of the competition

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Xiaomi has unveiled one of the slimmest magnetic power banks we’ve seen, measuring just 6mm thick and weighing 98g.

The new Xiaomi UltraThin Magnetic Power Bank 5000 15W is designed to snap onto compatible smartphones. At the same time, it remains barely noticeable in your pocket.

Despite its card-like profile, the power bank packs a 5,000mAh battery. Xiaomi uses a high energy-density silicon-carbon cell with 16% silicon content, alongside a large graphite cooling sheet. This helps keep the design thin without sacrificing everyday usability. It’s small enough to sit flush against the back of a phone. Yet it is substantial enough to provide a meaningful top-up.

Wireless charging tops out at 15W, though iPhone models support up to 7.5W magnetic charging. There’s also 22.5W wired charging via USB-C, giving users a faster option when needed. Xiaomi says wired use can extend the music playback time of its latest Xiaomi 17 handset by roughly nine hours. The power bank can also charge two devices simultaneously, and even continue powering devices while it recharges itself.

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Compatibility covers a broad range of devices, including Xiaomi’s recent flagship models. It also supports the iPhone 12 through the iPhone 17 series. Furthermore, select Samsung Galaxy Ultra handsets are supported. Google Pixel devices, including the Pixel 9 and Pixel 10 lines, are included as well.

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Design is clearly part of the appeal. The aluminium alloy shell features a subtle metallic finish and rounded edges. Additionally, it has a photolithographically etched logo. Xiaomi has also added a fibreglass surface layer with a heat-resistant coating. Along with that, dual NTC temperature controls help regulate heat during charging.

Ultra-thin magnetic battery packs aren’t new, but Xiaomi’s 6mm approach pushes portability further than most.

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ASUS 2026 Creator Series Launched: ProArt GoPro Edition, ROG Flow Z13-KJP, TUF A14

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ASUS gaming laptops have been the cream of the crop for some time now, as evidenced by my review of last year’s Zephyrus G14 and Strix G16. Building on that momentum, the Taiwanese laptop maker has announced its 2026 Creator lineup in India under the campaign “Built for Originals.” The new portfolio expands ASUS’s AI-powered laptop range with three key models: the ProArt GoPro Edition (PX13), the limited-edition ROG Flow Z13-KJP in collaboration with KOJIMA PRODUCTIONS, and the 2026 TUF Gaming A14. Here’s everything you need to know about them.

ProArt GoPro Edition (PX13)

Image of the ProArt GoPro Edition (PX13) laptop bundle

The ProArt GoPro Edition (PX13) is made for people who need serious editing performance on the move. It runs on the AMD Ryzen AI Max+ 395 processor with up to 50 TOPS NPU and supports up to 128GB LPDDR5X memory. Up front, you get a 13-inch 3K touchscreen display with 100% DCI-P3 color coverage and Pantone validation, along with stylus support. ASUS has also added a dedicated GoPro hotkey for faster editing workflows, along with creative tools like ASUS DialPad, StoryCube AI for media organization, and MuseTree for AI-assisted idea mapping.

Weighing just 1.39 kg and featuring a 360-degree hinge, the PX13 is clearly built for portability. Buyers also get a hard-shell carry case and modular memory foam packaging designed to securely store GoPro accessories. As part of the launch, ASUS is offering a GoPro MAX2 bundle (worth ₹62,500) at a 35% discount for ProArt GoPro Edition buyers. The bundle includes the GoPro MAX2 360 camera, extension pole, two Enduro batteries, and a 64GB SanDisk microSD card.

ROG Flow Z13-KJP

Back design of the Asus ROG Flow Z13-KJP

The ROG Flow Z13-KJP is a limited-edition 2-in-1 gaming device created in collaboration with KOJIMA PRODUCTIONS and artist Yoji Shinkawa. Inspired by the Ludens sci-fi aesthetic, the device features CNC-milled aluminium, carbon fiber detailing, custom keycaps, laser-etched vents, and themed packaging. Under the hood, it packs an AMD Ryzen AI Max+ 395 processor with Radeon 8060S graphics and up to 128GB of LPDDR5X unified memory. The 13.4-inch 2.5K ROG Nebula display supports a 180Hz refresh rate, 3ms response time, 100% DCI-P3 color gamut, and 500 nits brightness.

Advanced vapor chamber cooling and dual Arc Flow fans should help sustain performance in its compact chassis. Buyers will also receive a complimentary PC game code for DEATH STRANDING 2: ON THE BEACH, redeemable via Armoury Crate. Pre-orders begin at 12 PM on February 26, 2026, with availability starting March 4. ASUS is also offering a 2-year warranty extension and 3 years of accidental damage protection (worth up to ₹27,299) for ₹1 as a pre-order benefit. The ROG Flow Z13-KJP starts at ₹3,79,990.

TUF Gaming A14 (2026)

Asus TUF Gaming A14 2026 image

The 2026 TUF Gaming A14 is positioned as a portable performance machine for entry- to mid-level creators and gamers. It is powered by the AMD Ryzen AI Max+ 392 processor with Radeon 8060S graphics and integrates a 12-core Zen 5 CPU with RDNA 3.5 graphics. It comes with 32GB LPDDR5X unified memory and a 1TB PCIe 4.0 SSD, with an additional 2280 NVMe slot supporting up to 2TB expansion. The 73Wh battery supports fast charging and Type-C charging for better portability.

Despite its power, the laptop weighs just 1.48 kg and measures 1.69 cm thin. The 14-inch 2.5K display features a 165Hz refresh rate, 3ms response time, 100% sRGB coverage, and AMD FreeSync Premium support. The device is MIL-STD-810H certified and includes ports such as USB 4 and a microSD card reader. The TUF Gaming A14 starts at ₹1,79,990 and will be available via ASUS Exclusive Stores, ASUS eShop, Flipkart, Amazon, Croma, Reliance Digital, and other retail outlets.

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Pricing & Availibility

Model Starting Price Availability
ROG Flow Z13-KJP Rs 3,79,990 Pre-orders from February 26, 2026; On shelf from March 4, 2026
ProArt GoPro Edition (PX13) Rs 3,34,990 Available from February 26, 2026
TUF Gaming A14 (2026) Rs. 1,79,990 Available from February 26, 2026

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ChatGPT sucks at being a real robot

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This story was originally published in The Highlight, Vox’s member-exclusive magazine. To get access to member-exclusive stories every month, join the Vox Membership program today.

There’s something sad about seeing a humanoid robot lying on the floor. Without any electricity, these bipedal machines can’t stand up, so if they’re powered down and not hanging from a winch, they’re sprawled out on the floor, staring up at you, helpless.

That’s how I met Atlas a couple of months ago. I’d seen the robot on YouTube a hundred times, running obstacle courses and doing backflips. Then I saw it on the floor of a lab at MIT. It was just lying there. The contrast is jarring, if only because humanoid robots have become so much more capable and ubiquitous since Atlas got famous on YouTube.

Across town at Boston Dynamics, the company that makes Atlas, a newer version of the humanoid robot had learned not only to walk but also to drop things and pick them back up instinctively, thanks to a single artificial intelligence model that controls its movement. Some of these next-generation Atlas robots will soon be working on factory floors — and may venture further. Thanks in part to AI, general-purpose humanoids of all types seem inevitable.

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“In Shenzhen, you can already see them walking down the street every once in a while,” Russ Tedrake told me back at MIT. “You’ll start seeing them in your life in places that are probably dull, dirty, and dangerous.”

Tedrake runs the Robot Locomotion Group at the MIT Computer Science and Artificial Intelligence Lab, also known as CSAIL, and he co-led the project that produced the latest AI-powered Atlas. Walking was once the hard thing for robots to learn, but not anymore. Tedrake’s group has shifted focus from teaching robots how to move to helping them understand and interact with the world through software, namely AI. They’re not the only ones.

In the United States, venture capital investment in robotics startups grew from $42.6 million in 2020 to nearly $2.8 billion in 2025. Morgan Stanley predicts the cumulative global sales of humanoids will reach 900,000 in 2030 and explode to more than 1 billion by 2050, the vast majority of which will be for industrial and commercial purposes. Some believe these robots will ultimately replace human labor, ushering in a new global economic order. After all, we designed the world for humans, so humanoids should be able to navigate it with ease and do what we do.

an illustration of one nervous person and three robots all transporting brown boxes together in a line

Janik Söllner for Vox

They won’t all be factory workers, if certain startups get their way. A company called X1 Technologies has started taking preorders for its $20,000 home robot, Neo, which wears clothes, does dishes, and fetches snacks from the fridge. Figure AI introduced its Figure 03 humanoid robot, which also does chores. Sunday Robotics said it would have fully autonomous robots making coffee in beta testers’ homes next year.

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So far, we’ve seen a lot of demos of these AI-powered home robots and promises from the industrial humanoid makers, but not much in the way of a new global economic order. Demos of home robots, like the X1 Neo, have relied on human operators, making these automatons, in practice, more like puppets. Reports suggest that Figure AI and Apptronik have only one or two robots on manufacturing floors at any given time, usually doing menial tasks. That’s a proof of concept, not a threat to the human work force.

“In order to make them better, we have to make AI better.”

You can think of all these robots as the physical embodiment of AI, or just embodied AI. This is what happens when you put AI into a physical system, enabling it to interact with the real world. Whether that’s in the form of a humanoid robot or an autonomous car, it’s the next frontier for hardware and, arguably, technological progress writ large.

Embodied AI is already transforming how farming works, how we move goods around the world, and what’s possible in surgical theaters. We might be just one or two breakthroughs away from walking, talking, thinking machines that can work alongside us, unlocking a whole new realm of possibilities. “Might” is the key word there.

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“If we’re looking for robots that will work side by side with us in the next couple of years, I don’t think it will be humanoids,” Daniela Rus, director of CSAIL, told me not long after I left Tedrake’s lab. “Humanoids are really complicated, and we have to make them better. And in order to make them better, we have to make AI better.”

So to understand the gap between the hype around humanoids and the technology’s real promise, you have to know what AI can and can’t do for robots. You also, unfortunately, have to try to understand what Elon Musk has been up to at Tesla for the past five years.

It’s still embarrassing to watch the part of the Tesla AI Day presentation in 2021 when a human person dressed in a robot costume appears on stage dancing to dubstep music. Musk eventually stops the dance and announces that Tesla, “a robotics company,” will have a prototype of a general-purpose humanoid robot, now known as Optimus, the following year. Not many people believed him, and now, years later, Tesla still has not delivered a fully functional Optimus. Never afraid to make a prediction, Musk told audiences at Davos in January 2026 that Tesla’s robot will go on sale next year.

“People took him seriously because he had a great track record,” said Ken Goldberg, a roboticist at the University of California-Berkeley and co-founder of Ambi Robotics. “I think people were inspired by that.”

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You can imagine why people got excited, though. With the Optimus robot, Elon Musk promised to eliminate poverty and offer shareholders “infinite” profits. He said engineers could effectively translate Tesla’s self-driving car technology into software that could power autonomous robots that could work in factories or help around the house. It’s a version of the same vision humanoid robotics startups are chasing today, albeit colored by several years of Musk’s unfulfilled promises.

We now know that Optimus struggles with a lot of the same problems as other attempts at general-purpose humanoids. It often requires humans to remotely operate it, and it struggles with dexterity and precision. The 1X Neo, likewise, needed a human’s help to open a refrigerator door and collapsed onto the floor in a demo for a New York Times journalist last year. The hardware seems capable enough. Optimus can dance, and Neo can fold clothes, albeit a bit clumsily. But they don’t yet understand physics. They don’t know how to plan or to improvise. They certainly can’t think.

“People in general get too excited by the idea of the robot and not the reality.”

“People in general get too excited by the idea of the robot and not the reality,” said Rodney Brooks, co-founder of iRobot, makers of the Roomba robot vacuum. Brooks, a former CSAIL director, has written extensively and skeptically about humanoid robots.

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Clearly, there’s a gap between what’s happening in research labs and what’s being deployed in the real world. Some of the optimism around humanoids is based on good science, though. In 2023, Tedrake coauthored a landmark paper with Tony Zhao, co-founder and CEO of Sunday Robotics, that outlined a novel method for training robots to move like humans. It involves humans performing the task wearing sensor-laden gloves that send data to an AI model that enables the robot to figure out how to do those tasks. This complemented work Tedrake was doing at the Toyota Research Institute that used the same kinds of methods AI models use to generate images to generate robot behavior. You’ve heard of large language models, or LLMs. Tedrake calls these large behavior models, or LBMs.

It makes sense. By watching humans do things over and over, these AI models collect enough data to generate new behaviors that can adapt to changing environments. Folding laundry, for example, is a popular example of a task that requires nimble hands and better brains. If a robot picks up a shirt and the fabric flops down in an unexpected way, it needs to figure out how to handle that uncertainty. You can’t simply program it to know what to do when there are so many variables. You can, however, teach it to learn.

That’s what makes the lemonade demo so impressive. Some of Rus’s students at CSAIL have been teaching a humanoid robot named Ruby to make lemonade — something that you might want a robot butler to do one day — by wearing sensors that measure not only the movements but the forces involved. It’s a combination of delicate movements, like pouring sugar, and strong ones, like lifting a jug of water. I watched Ruby do this without spilling a drop. It hadn’t been programmed to make lemonade. It had learned.

The real challenge is getting this method to scale. One way is simply to brute-force it: Employ thousands of humans to perform basic tasks, like folding laundry, to build foundation models for the physical world. Foundation models are the massive datasets that can be adapted to specific tasks like generating text, images, or in this case, robot behavior. You can also get humans to teleoperate countless robots in order to train these models. These so-called arm farms already exist in warehouses in Eastern Europe, and they’re about as dystopian as they sound.

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Another option is YouTube. There are a lot of how-to videos on YouTube, and some researchers think that feeding them all into an AI model will provide enough data to give robots a better understanding of how the world works. These two-dimensional videos are obviously limited, if only because they can’t tell us anything about the physics of the objects in the frame. The same goes for synthetic data, which involves a computer rapidly and repeatedly carrying out a task in a simulation. The upside here, of course, is more data, more quickly. The downside is that the data isn’t as good, especially when it comes to physical forces like friction and torque, which also happen to be the most important for robot dexterity.

“Physics is a tough task to master,” Brooks said. “And if you have a robot, which is not good with physics, in the presence of people, it doesn’t end well.”

an illustration of a robot butler tripping up some stairs. Food and drinks fly everywhere.

Janik Söllner for Vox

That’s not even taking into account the many other bottlenecks facing robotics right now. While components have gotten cheaper — you can buy a humanoid robot right now for less than $6,000, compared to the $75,000 it cost to buy Boston Dynamics’ small, four-legged robot Spot five years ago — batteries represent a major bottleneck for robotics, limiting the run time of most humanoids to two to four hours.

Then you have the problem with processing power. The AI models that can make humanoids more human require massive amounts of compute. If that’s done in the cloud, you’ve got latency issues, preventing the robot from reacting in real time. And inevitably, to tie a lot of other constraints into a tidy bundle, the AI is just not good enough.

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If you trace the history of AI and the history of robotics back to their origins, you’ll see a braided line. The two technologies have intersected time and again, since the birth of the term “artificial intelligence” at a Dartmouth summer research workshop in the summer of 1956. Then, half a century later, things started heating up on the AI front, when advances in machine learning and powerful processors called GPUs — the things that have now made Nvidia a $5 trillion company — ushered in the era of deep learning. I’m about to throw a few technical terms at you, so bear with me.

Machine learning is a type of AI. It’s when algorithms look for patterns in data and make decisions without being explicitly trained to do so. Deep learning takes it to another level with the help of a machine learning model called a neural network. You can think of a neural network, a concept that’s even older than AI, as a system loosely modeled on the human brain that’s made up of lots of artificial neurons that do math problems. Deep learning uses multilayered neural networks to learn from huge data sets and to make decisions and predictions. Among other accomplishments, neural networks have revolutionized computer vision to improve perception in robots.

There are different architectures for neural networks that can do different things, like recognize images or generate text. One is called a transformer. The “GPT” in ChatGPT stands for “generative pre-trained transformer,” which is a type of large language model, or LLM, that powers many generative AI chatbots. While you’d think LLMs would be good at making robots think, they really aren’t. Then there are diffusion models, which are often used for image generation and, more recently, making robots appear to think. The framework that Tedrake and his coauthors described in their 2023 research into using generative AI to train robots is based on diffusion.

“Under the hood, what’s actually going on should be something much more like our own brains.”

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Three things stand out in this very limited explanation of how AI and robots get along. One is that deep learning requires a massive amount of processing power and, as a result, a huge amount of energy. The other is that the latest AI models work with the help of stacks of neural networks whose millions or even billions of artificial neurons do their magic in mysterious and usually inefficient ways. The third thing is that, while LLMs are good at language, and diffusion models are good at images, we don’t have any models that are good enough at physics to send a 200-pound robot marching into a crowd to shake hands and make friends.

As Josh Tenenbaum, a computational cognitive scientist at MIT, explained to me recently, an LLM can make it easier to talk to a robot, but it’s hardly capable of being the robot’s brains. “You could imagine a system where there’s a language model, there’s a chatbot, you want to talk to your robot,” Tenenbaum said. “Under the hood, what’s actually going on should be something much more like our own brains and minds or other animals, not just humans in terms of how it’s embodied and deals with the world.”

So we need better AI for robots, if not in general. Scientists at CSAIL have been working on a couple of physics-inspired and brain-like technologies they’re calling liquid neural networks and linear optical networks. They both fall into the category of state-space models, which are emerging as an alternative or rival to transformer-based models. Whereas transformer-based models look at all available data to identify what’s important, state-space models are much more efficient, as they maintain a summary of the world that gets updated as new data comes in. It’s closer to how the human brain works.

To be perfectly honest, I’d never heard of state-space models until Rus, the CSAIL director, told me about them when we chatted in her office a few weeks ago. She pulled up a video to illustrate the difference between a liquid neural network and a traditional model used for self-driving cars. In it, you can see how the traditional model focuses its attention on everything but the road, while the newer state-space model only looks at the road. If I’m riding in that car, by the way, I want the AI that’s watching the road.

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“And instead of a hundred thousand neurons,” Rus says, referring to the traditional neural network, “I have only 19.” And here’s where it gets really compelling. She added, “And because I have only 19, I can actually figure out how these neurons fire and what the correlation is between these neurons and the action of the car.”

You may have already heard that we don’t really know how AI works. If newer approaches bring us a little bit closer to comprehension, it certainly seems worth taking them seriously, especially if we’re talking about the kinds of brains we’ll put in humanoid robots.

When a humanoid robot loses power, when electricity stops flowing to the motors that keep it upright, it collapses into a heap of heavy metal parts. This can happen for any number of reasons. Maybe it’s a bug in the code or a lost wifi connection. And when they’re on, humanoids are full of energy as their joints fight gravity or stand ready to bend. If you imagine being on the wrong side of that incredible mechanical power, it’s easy to doubt this technology.

Some companies that make humanoid robots also admit that they’re not very useful yet. They’re too unreliable to help out around the house, and they’re not efficient enough to be helpful in factories. Furthermore, most of the money being spent developing robots is being spent on making them safe around people. When it comes to deploying robots that can contribute to productivity, that can participate in the economy, it makes a lot more sense to make them highly specialized and not human-shaped.

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“Let’s not do open heart surgery right away with these things.”

The embodied AI that will transform the world in the near future is what’s already out there. In fact, it’s what’s been out there for years. Early self-driving cars date back to the 1980s, when Ernst Dickmanns put a vision-guided Mercedes van on the streets of Munich. Researchers from Carnegie Mellon University got a minivan to drive itself across the United States in 1995. Now, decades later, Waymo is operating its robotaxi service in a half-dozen American cities, and the company says its AI-powered cars actually make the roads safer for everyone.

Then there are the Roombas of the world, the robots that are designed to do one thing and keep getting better at it. You can include the vast array of increasingly intelligent manufacturing and warehouse robots in this camp too. By 2027, the year Elon Musk is on track to miss his deadline to start selling Optimus humanoids to the public, Amazon will reportedly replace more than 600,000 jobs with robots. These would probably be boring robots, but they’re safe and effective.

Science fiction promised us humanoids, however. Pick an era in human history, in fact, and someone was dreaming about an automaton that could move like us, talk like us, and do all our dirty work. Replicants, androids, the Mechanical Turk — all these humanoid fantasies imagined an intelligent synthetic self.

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Reality gave us package-toting platforms on wheels roving around Amazon warehouses or the sensor-heavy self-driving cars clogging San Francisco streets. In time, even the skeptics think that humanoids will be possible. Probably not in five years, but maybe in 50, we’ll get artificially intelligent companions who can walk alongside us. They’ll take baby steps.

“Good robots are going to be clumsy at first, and you have to find applications where it’s okay for the robot to make mistakes and then recover,” Tedrake said. “Let’s not do open-heart surgery right away with these things. This is more like folding laundry.”

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Designing for Precision: CAD Tips for Micro-Scale 3D Printing

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Micro-scale 3D printing demands a fundamentally different approach to CAD design compared to traditional macro-scale work. With feature sizes smaller than a strand of hair and tolerances measured in single-digit microns, the margin for error is virtually zero. Engineers working in medical devices, electronics, photonics, and microfluidics need to rethink how they handle tolerances, geometry, wall thickness, and support structures when designing at this scale. This whitepaper walks through practical, field-tested tips — from setting appropriate tolerances and reinforcing thin walls to designing functional microfluidic channels and choosing the right materials — so you can reduce failed prints, shorten iteration cycles, and move from concept to validated prototype with confidence.

 

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Tech Moves: Zillow names CPO; AWS leader retires; Microsoft hires AI expert from Apple

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Zillow Group’s new senior leadership team members, from left: Christopher Roberts, Jon Lim and Marissa Brooks. (Zillow Photos)

Zillow Group announced three promotions to its senior leadership team.

  • After nearly two decades with Zillow, Christopher Roberts is now chief product officer. Roberts helped build Zillow Rentals, which the company touts as the No. 1 platform among renters. His Seattle tech career started at Expedia as a senior vice president of engineering.
  • Jon Lim is moving from VP of product management to SVP of Rentals Product & Business Operations. Prior to Zillow, Lim worked in technical product management roles at Amazon for more than five years.
  • Marissa Brooks is now SVP of corporate affairs, having previously served as VP of communications. Brooks, who works from Scottsdale, Ariz., joined Zillow in 2017.

Earlier this month, Zillow reported its revenue grew 16% last year. Its quarterly revenue, which came in at $654 million, was at the upper end of Zillow’s guidance and slightly higher than investors’ projections.

Jeffrey Kratz. (LinkedIn Photo)

Jeffrey Kratz is retiring from Amazon Web Services after more than 13 years. He’s leaving the role of vice president of Worldwide Public Sector Industry international sales. Throughout his tenure at AWS, Kratz worked with public sector customers, whom he described on LinkedIn as “making the world a better place.”

Kratz previously was employed at crosstown rival Microsoft for two decades where he held a variety of leadership roles in enterprise and public sector sales.

“Now it’s time to recharge, take Luna-the-pup on leisurely walks, spend quality time with Beverly, Andrew, family, and friends,” Kratz wrote, adding that he would work on his golf swing, volunteering and “spending more time with Boards in areas I am passionate about.”

— In another Amazon departure, David Luan, who led the company’s San Francisco-based AGI Lab and oversaw one of its most important agentic AI initiatives, is leaving for an undisclosed new gig. Luan announced his exit on LinkedIn, saying he will leave at the end of the week. He joined Amazon through an acqui-hire deal targeting leaders at the startup Adept. More details are in this GeekWire story.

Manasa Hari. (LinkedIn Photo)

Microsoft nabbed Manasa Hari from Apple to join its California-based AI Super Intelligence program as a partner.

“I’ll be supporting to build the infrastructure for human-centric AI systems that are safe, useful, and aligned with human needs. Inspired by Mustafa Suleyman’s mission to build AI that amplifies human potential, I’m excited about its broad impact on enterprise,” Hari said on LinkedIn.

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Hari was previously head of product and program at Apple’s AIML Machine Learning Platform. She also serves on San Francisco State University’s Big Data Advisory Board, which provides input on course curriculum.

Craig Cincotta has moved to chief of staff for Microsoft’s Xbox division. He previously was a general manager of communications for cloud and AI. Cincotta has been with the Redmond, Wash.-tech giant for more than 17 years over two stretches of employment.

The company last week announced that Asha Sharma is taking the helm of Xbox and Microsoft Gaming, succeeding 38-year Microsoft veteran Phil Spencer. Cincotta and Sharma previously worked together at Seattle-based Porch.

Julie Keef. (LinkedIn Photo)

Julie Keef is leaving her role of VP of product at Redfin, the Seattle real estate platform that was acquired nearly a year ago by Rocket Companies. Keef joined Redfin in 2016 as the first hire on what would become the company’s content marketing team. She was promoted seven times to reach her VP position in which she oversaw a team of 50.

“We grew Redfin to the 3rd most visited real estate site, and held on to that spot despite competitors outspending us 5 to 1 on tech and advertising. And we had fun doing it. Even as the housing market turned and investment was hard to come by, the rabid squirrel spirit of Redfin persisted,” Keef said on LinkedIn.

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Keef did not disclose her next pursuit.

Ravi Doddivaripall. (BusinessWire Photo)

— Seattle’s DexCare named Ravi Doddivaripall as chief technology officer. Doddivaripall joins the company from XY Retail and has more than 25 years of senior platform and engineering experience. He is based in the San Francisco Bay Area.

DexCare’s software platform helps healthcare providers manage their system’s capacity and schedule appointments. The startup launched at Providence, spinning out from the healthcare network’s digital innovation group in 2021.

“Ravi brings the architectural depth and platform experience to accelerate what we’ve built to help more health systems treat more patients with the resources they already have,” said Matt Blosl, CEO of DexCare, in a statement.

Kelly Brooks. (LinkedIn Photo)

Kelly Brooks is now VP of sales for Read AI, a Seattle startup that sells enterprise productivity software tools using generative AI. Brooks joins from HubSpot where she worked for nearly nine years.

On LinkedIn, Brooks said she was attracted to the company after using its technology.

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“I saw immediate value from trialing the product, and got excited by the ways Read improves the transfer and access of information through organizations — perennial challenges I tackled as Chief of Staff at HubSpot,” Brooks wrote. “Inspired, I reached out to [CEO] David Shim to make a connection. The rest is history… or at least a story for another day :)”

— Serial entrepreneur and ShiftAI podcast host Boaz Ashkenazy is now senior director of AI infrastructure for Redapt, a Woodinville, Wash.-based IT company.

Ashkenazy is also co-founder of the legal tech startup Clause and co-founder and CEO of Augmented AI Labs, which builds and tests AI products. Ashkenazy additionally serves on the board of trustees for the Seattle Metropolitan Chamber of Commerce.

Jerome Johnson. (LinkedIn Photo)

Jerome Johnson has a new leadership role at Amazon Web Services, serving as director of its professional services business for U.S. federal, defense and aerospace customers. Johnson, who is based in Arlington, Virginia, has been with AWS for more than 12 years. His previous role was director of solutions architecture for national security and defense customers.

“While my focus expands from architecture leadership to business and delivery leadership, the mission remains the same: Serving customers by helping them solve their hardest problems with AWS,” Johnson wrote on LinkedIn.

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Jill Angelo is the new board chair of Special Olympics Washington. Angelo is the founder and past CEO of Gennev, a company billed as the first virtual menopause care provider in the U.S. The business was acquired by Unified Women’s Healthcare, where she served as president until last year.

Angelo is also currently VP of women’s health and commercial partnerships at the wellness startup Oura.

Frieda Chan has left her role as manager of innovation development at the University of Washington’s CoMotion, the institution’s collaborative entrepreneurial hub. Chan is now director business development at Yale Ventures.

Yoodli shared that Tom Craven is now the enterprise sales leader for the Seattle-based AI roleplay startup.

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William Bal is now VP of growth for EdgeRunner AI, a Seattle-based defense technology company that raised $12 million last year.

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