Sometimes a visually compelling metaphor is all you need to get an otherwise complicated idea across. In the summer of 2001, a Tulane physics professor named John P. Perdew came up with a banger. He wanted to convey the hierarchy of computational complexity inherent in the behavior of electrons in materials. He called it “Jacob’s Ladder.” He was appropriating an idea from the Book of Genesis, in which Jacob dreamed of a ladder “set up on the earth, and the top of it reached to heaven. And behold the angels of God ascending and descending on it.”
Jacob’s Ladder represented a gradient and so too did Perdew’s ladder, not of spirit but of computation. At the lowest rung, the math was the simplest and least computationally draining, with materials represented as a smoothed-over, cartoon version of the atomic realm. As you climbed the ladder, using increasingly more intensive mathematics and compute power, descriptions of atomic reality became more precise. And at the very top, nature was perfectly described via impossibly intensive computation—something like what God might see.
With this metaphor in mind, we propose to extend Jacob’s Ladder beyond Perdew’s version, to encompass all computational approaches to simulating the behavior of electrons. And instead of climbing rung by rung toward an unreachable summit, we have an idea to bend the ladder so that even the very top lies within our grasp. Specifically, we at Microsoft envision a hybrid approach. It starts with using quantum computers to generate exquisitely accurate data about the behavior of electrons—data that would be prohibitively expensive to compute classically. This quantum-generated data will then train AI models running on classical machines, which can predict the properties of materials with remarkable speed. By combining quantum accuracy with AI-driven speed, we can ascend Jacob’s Ladder faster, designing new materials with novel properties and at a fraction of the cost.
At the base of Jacob’s Ladder are classical models that treat atoms as simple balls connected by springs—fast enough to handle millions of atoms over long times but with the lowest precision. Moving up along the black line, semiempirical methods add some quantum mechanical calculations. Next are approximations based on Hartree-Fock (HF) and density functional theory (DFT), which include full quantum behavior of individual electrons but model their interactions in an averaged way. The greater accuracy requires significant computing power, which limits them to simulating molecules with no more than a few hundred atoms. At the top are coupled-cluster and full configuration interaction (FCI) methods—exquisitely accurate but, at the moment, restricted to tiny molecules or subsets of electrons due to the large computational costs involved. Quantum computing can bend the accuracy-versus-cost curve at the top of Jacob’s Ladder [orange line], making highly accurate calculations feasible for large systems. AI, trained on this quantum-accurate data, can flatten this curve [purple line], enabling rapid predictions for similar systems at a fraction of the cost of classical computing.Source: Microsoft Quantum
In our approach, the base of Jacob’s Ladder still starts with classical models that treat atoms as simple balls connected by springs—models that are fast enough to handle millions of atoms over long times, but with the lowest precision. As we ascend the ladder, some quantum mechanical calculations are added to semiempirical methods. Eventually, we’ll get to the full quantum behavior of individual electrons but with their interactions modeled in an averaged way; this greater accuracy requires significant compute power, which means you can only simulate molecules of no more than a few hundred atoms. At the top will be the most computationally intensive methods—prohibitively expensive on classical computers but tractable on quantum computers.
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In the coming years, quantum computing and AI will become critical tools in the pursuit of new materials science and chemistry. When combined, their forces will multiply. We believe that by using quantum computers to train AI on quantum data, the result will be hyperaccurate AI models that can reach ever higher rungs of computational complexity without the prohibitive computational costs.
This powerful combination of quantum computing and AI could unlock unprecedented advances in chemical discovery, materials design, and our understanding of complex reaction mechanisms. Chemical and materials innovations already play a vital—if often invisible—role in our daily lives. These discoveries shape the modern world: new drugs to help treat disease more effectively, improving health and extending life expectancy; everyday products like toothpaste, sunscreen, and cleaning supplies that are safe and effective; cleaner fuels and longer-lasting batteries; improved fertilizers and pesticides to boost global food production; and biodegradable plastics and recyclable materials to shrink our environmental footprint. In short, chemical discovery is a behind-the-scenes force that greatly enhances our everyday lives.
The potential is vast. Anywhere AI is already in use, this new quantum-enhanced AI could drastically improve results. These models could, for instance, scan for previously unknown catalysts that could fix atmospheric carbon and so mitigate climate change. They could discover novel chemical reactions to turn waste plastics into useful raw materials and remove toxic “forever chemicals” from the environment. They could uncover new battery chemistries for safer, more compact energy storage. They could supercharge drug discovery for personalized medicine.
And that would just be the beginning. We believe quantum-enhanced AI will open up new frontiers in materials science and reshape our ability to understand and manipulate matter at its most fundamental level. Here’s how.
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How Quantum Computing Will Revolutionize Chemistry
To understand how quantum computing and AI could help bend Jacob’s Ladder, it’s useful to look at the classical approximation techniques that are currently used in chemistry. In atoms and molecules, electrons interact with one another in complex ways called electron correlations. These correlations are crucial for accurately describing chemical systems. Many computational methods, such as density functional theory (DFT) or the Hartree-Fock method, simplify these interactions by replacing the intricate correlations with averaged ones, assuming that each electron moves within an average field created by all other electrons. Such approximations work in many cases, but they can’t provide a full description of the system.
A joint project between Microsoft and Pacific Northwest National Laboratory used AI and high-performance computing to identify potential materials for battery electrolytes. The most promising were synthesized [top and middle] and tested [bottom] at PNNL. Dan DeLong/Microsoft
Electron correlation is particularly important in systems where the electrons are strongly interacting—as in materials with unusual electronic properties, like high-temperature superconductors—or when there are many possible arrangements of electrons with similar energies—such as compounds containing certain metal atoms that are crucial for catalytic processes.
In these cases, the simplified approach of DFT or Hartree-Fock breaks down, and more sophisticated methods are needed. As the number of possible electron configurations increases, we quickly reach an “exponential wall” in computational complexity, beyond which classical methods become infeasible.
Enter the quantum computer. Unlike classical bits, which are either on or off, qubits can exist in superpositions—effectively coexisting in multiple states simultaneously. This should allow them to represent many electron configurations at once, mirroring the complex quantum behavior of correlated electrons. Because quantum computers operate on the same principles as the electron systems they will simulate, they will be able to accurately simulate even strongly correlated systems—where electrons are so interdependent that their behavior must be calculated collectively.
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AI’s Role in Advancing Computational Chemistry
At present, even the computationally cheap methods at the bottom of Jacob’s Ladder are slow, and the ones higher up the ladder are slower still. AI models have emerged as powerful accelerators to such calculations because they can serve as emulators that predict simulation outcomes without running the full calculations. The models can speed up the time it takes to solve problems up and down the ladder by orders of magnitude.
This acceleration opens up entirely new scales of scientific exploration. In 2023 and 2024, we collaborated with researchers at Pacific Northwest National Laboratory (PNNL) on using advanced AI models to evaluate over 32 million potential battery materials, looking for safer, cheaper, and more environmentally friendly options. This enormous pool of candidates would have taken about 20 years to explore using traditional methods. And yet, within less than a week, that list was narrowed to 500,000 stable materials and then to 800 highly promising candidates. Throughout the evaluation, the AI models replaced expensive and time-consuming quantum chemistry calculations, in some cases delivering insights half a million times as fast as would otherwise have been the case.
We then used high-performance computing (HPC) to validate the most promising materials with DFT and AI-accelerated molecular dynamics simulations. The PNNL team then spent about nine months synthesizing and testing one of the candidates—a solid-state electrolyte that uses sodium, which is cheap and abundant, and some other materials, with 70 percent less lithium than conventional lithium-ion designs. The team then built a prototype solid-state battery that they tested over a range of temperatures.
This potential battery breakthrough isn’t unique. AI models have also dramatically accelerated research in climate science, fluid dynamics, astrophysics, protein design, and chemical and biological discovery. By replacing traditional simulations that can take days or weeks to run, AI is reshaping the pace and scope of scientific research across disciplines.
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However, these AI models are only as good as the quality and diversity of their training data. Whether sourced from high-fidelity simulations or carefully curated experimental results, these data must accurately represent the underlying physical phenomena to ensure reliable predictions. Poor or biased data can lead to misleading outcomes. By contrast, high-quality, diverse datasets—such as those full-accuracy quantum simulations—enable models to generalize across systems and uncover new scientific insights. This is the promise of using quantum computing for training AI models.
How to Accelerate Chemical Discovery
The real breakthrough will come from strategically combining quantum computing’s and AI’s unique strengths. AI already excels at learning patterns and making rapid predictions. Quantum computers, which are still being scaled up to be practically useful, will excel at capturing electron correlations that classical computers can only approximate. So if you train classical models on quantum-generated data, you’ll get the best of both worlds: the accuracy of quantum delivered at the speed of AI.
As we learned from the Microsoft-PNNL collaboration on electrolytes, AI models alone can greatly speed up chemical discovery. In the future, quantum-accurate AI models will tackle even bigger challenges. Consider the basic discovery process, which we can think of as a funnel. Scientists begin with a vast pool of candidate molecules or materials at the wide-mouthed top, narrowing them down using filters based on desired properties—such as boiling point, conductivity, viscosity, or reactivity. Crucially, the effectiveness of this screening process depends heavily on the accuracy of the models used to predict these properties. Inaccurate predictions can create a “leaky” funnel, where promising candidates are mistakenly discarded or poor ones are mistakenly advanced.
Quantum-accurate AI models will dramatically improve the precision of chemical-property predictions. They’ll be able to help identify “first-time right” candidates, sending only the most promising molecules to the lab for synthesis and testing—which will save both time and cost.
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Another key aspect of the discovery process is understanding the chemical reactions that govern how new substances are formed and behave. Think of these reactions as a network of roads winding through a mountainous landscape, where each road represents a possible reaction step, from starting materials to final products. The outcome of a reaction depends on how quickly it travels down each path, which in turn is determined by the energy barriers along the way—like mountain passes that must be crossed. To find the most efficient route, we need accurate calculations of these barrier heights, so that we can identify the lowest passes and chart the fastest path through the reaction landscape.
Even small errors in estimating these barriers can lead to incorrect predictions about which products will form. Case in point: A slight miscalculation in the energy barrier of an environmental reaction could mean the difference between labeling a compound a “forever chemical” or one that safely degrades over time.
Accurate modeling of reaction rates is also essential for designing catalysts—substances that speed up and steer reactions in desired directions. Catalysts are crucial in industrial chemical production, carbon capture, and biological processes, among many other things. Here, too, quantum-accurate AI models can play a transformative role by providing the high-fidelity data needed to predict reaction outcomes and design better catalysts.
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Once trained, these AI models, powered by quantum-accurate data, will revolutionize computational chemistry by delivering quantum-level precision. And once the AI models, which run on classical computers, are trained with quantum computing data, researchers will be able to run high-accuracy simulations on laptops or desktop computers, rather than relying on massive supercomputers or future quantum hardware. By making advanced chemical modeling more accessible, these tools will democratize discovery and empower a broader community of scientists to tackle some of the most pressing challenges in health, energy, and sustainability.
Remaining Challenges for AI and Quantum Computing
By now, you’re probably wondering: When will this transformative future arrive? It’s true thatquantum computers still struggle with error rates and limited lifetimes of usable qubits. And they still need to scale to the size required for meaningful chemistry simulations. Meaningful chemistry simulations beyond the reach of classical computation will require hundreds to thousands of high-quality qubits with error rates of around 10-15, or one error in a quadrillion operations. Achieving this level of reliability will require fault tolerance through redundant encoding of quantum information in logical qubits, each consisting of hundreds of physical qubits, thus requiring a total of about a million physical qubits. Current AI models for chemical-property predictions may not have to be fully redesigned. We expect that it will be sufficient to start with models pretrained on classical data and then fine-tune them with a few results from quantum computers.
Despite some open questions, the potential rewards in terms of scientific understanding and technological breakthroughs make our proposal a compelling direction for the field. The quantum computing industry has begun to move beyond the early noisy prototypes, and high-fidelity quantum computers with low error rates could be possible within a decade.
Realizing the full potential of quantum-enhanced AI for chemical discovery will require focused collaboration between chemists and materials scientists who understand the target problems, experts in quantum computing who are building the hardware, and AI researchers who are developing the algorithms. Done right, quantum-enhanced AI could start to tackle the world’s toughest challenges—from climate change to disease—years ahead of anyone’s expectations.
Amid the backlash, the business—touted as S’pore’s largest 24-hour spa—closes its pools
When House+ Bubble announced its arrival in Singapore, it quickly became one of the most talked-about spa openings here.
Touted as Singapore’s largest 24-hour spa—it will span nearly 100,000 sqft once completed—the new S$45 million wellness destination in Jurong East promised an all-in-one experience: soaking pools, therapy rooms, a cinema, an e-sports room, and round-the-clock access.
But that hype appears to have been short-lived.
Just a week into its soft-opening, during which guests could access the spa, massage services, pools, and dining areas for S$49, House+ Bubble closed its bathing pools in both the male and female sections indefinitely, citing “internal facility adjustments.”
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A statement from House+ Bubble./ Image Credit: House+ Bubble
This move comes amid mounting complaints online. Google Reviews and visitor feedback have flagged hygiene concerns, inconsistent pool temperatures, and other operational issues, raising questions about whether the spa can live up to its lofty promises.
A slew of negative reviews
When Vulcan Post combed through House+ Bubble’s Google Reviews, bathrooms and toilets were described as “dirty,” and shared amenities raised concerns: combs reportedly had visible dandruff, while communal skincare bottles contained stray hairs.
A Google Review accompanying photos shows wet floors and towels left on the ground. The user also claimed that toilet bowls were clogged, and urinals were broken with “water running non-stop,” and a lack of toilet paper or paper towels./ Image Credit: Google Maps
Others pointed out that, despite being marketed as a 24-hour spa, not all facilities actually operate around the clock. The on-site restaurant closes at 12:30 AM, while massage services end at 10:30PM.
In response to one reviewer, the management of House+ Bubble said that it “is taking action to address these issues” and would elevate its cleaning standards.
Some visitors also highlighted hidden costs and misleading advertising. Despite claims of “unlimited massages,” the S$49 soft-opening fee only covered the massage chairs. A proper massage would reportedly cost between S$150 and S$250 per hour.
Alleged staffing issues
Allegedly, staffing issues may be compounding the spa’s operational problems.
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A Reddit post claims that several employees left after short stints due to “poor management” and “poor staff treatment.”
Staff reportedly received only a 30-minute unpaid meal break for a nine-hour shift, despite being told they would get an hour. The post adds that the spa is now facing manpower shortages as a result.
Vulcan Post has reached out to House+ Bubble for comment on these claims but has yet to receive a response.
A S$45 million spa ambition
House+ Bubble is a S$45 million project.
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Some of the facilities shown on the House+ Bubble website include private pools and even an esports room./ Image Credit: House+ Bubble
Its first opening phase, spanning approximately 49,000 sqft, was slated for an official launch on Mar 14, though it remains unclear if this will proceed as planned.
The second phase will look to add about 50,000 sqft, and is targeted for completion at the end of the year, subject to regulatory approvals.
Currently, visitors can still access House+ Bubble, but following the closure of the bathing pools, the trial operating fee has been reduced from S$49 to S$39 for three hours, excluding pool access.
Read other articles we’ve written on Singaporean businesses here.
Featured Image Credit: House+ Bubble/ Screengrab from Google Reviews
You may or may not be reading this on a smartphone, but odds are that even if you aren’t, you own one. Well, possess one, anyway — it’s debatable if the locked-down, one-way relationships we have with our addiction slabs counts as ownership. [LuckyBor], aka [Breezy], on the other hand — fully owns his 4G smartphone, because he made it himself.
OK, sure, it’s only rocking a 4G modem, not 5G. But with an ESP32-S3 for a brain, that’s probably going to provide plenty of bandwidth. It does what you expect from a phone: thanks to its A7682E simcom modem, it can call and text. The OV2640 Arducam module allows it to take pictures, and yes, it surfs the web. It even has features certain flagship phones lack, like a 3.5 mm audio jack, and with its 3.5″ touchscreen, the ability to fit in your pocket. Well, once it gets a case, anyway.
It talks, it texts, it… does not julienne fry, but that’s arguably a good thing.
This is just an alpha version, a brick of layered modules. [LuckyBor] plans on fitting everything into a slimmer form factor with a four-layer PCB that will also include an SD-card adapter, and will open-source the design at that time, both hardware and software. Since [LuckyBor] has also promised the world documentation, we don’t mind waiting a few months.
It’s always good to see another open-source option, and this one has us especially chuffed. Sure, we’ve written about Postmarket OS and other Linux options like Nix, and someone even put the rust-based Redox OS on a phone, but those are still on the same potentially-backdoored commercial hardware. That’s why this project is so great, even if its performance is decidedly weak compared to flagship phones that have as more horsepower as some of our laptops.
We very much hope [LuckyBor] carries through with the aforementioned promise to open source the design.
We may receive a commission on purchases made from links.
With streaming being such an integral part of modern entertainment, it’s no wonder we’re all looking for ways to optimize our experience. Beyond owning smart TVs, this also means investing in additional devices, such as the Amazon Fire TV Stick, to enhance the viewing experience. In fact, it includes numerous useful remote shortcuts that Amazon doesn’t advertise, letting you do everything from switching display resolutions to enabling accessibility features.
Unlike our smart TVs, which usually stay firmly in our homes, you can also easily travel with your Fire TV Stick and enjoy your streaming content as long as you have access to a compatible TV and Wi-Fi. Not to mention, you can play games on television screens without lugging around huge gaming laptops or bringing extra handheld consoles.
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Owning an Amazon Fire TV Stick also opens many connectivity options, especially with Bluetooth-enabled devices. But take note: while there are a ton of devices you can connect to your Fire TV Stick to see your exact options, you first need to find your model number by either referencing the receipt, the box it came with, or the device itself when in use. If you’ve forgotten what model of Fire TV stick you own, you can launch it, open the Settings menu, and select “My Fire TV.”
To pair any compatible Bluetooth device, launch your Amazon Fire TV Stick and navigate to Settings. Afterward, select Controllers & Bluetooth Devices, choose the device category you want to pair, and follow the pairing instructions on the screen.
Here are the gadgets you can connect to the Fire TV Stick via Bluetooth.
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1. Speakers
Valeriia Sakhno/Shutterstock
Many modern smart television sets will probably already let you hook up your speakers directly via Bluetooth. However, there are reasons why you might still want to do it through the Fire TV Stick. For example, you can easily adjust the volume with the Fire TV Stick remote, so you have fewer things to fiddle with. If you tend to use your television only with your Fire TV Stick, this can also streamline audio processing and reduce the risk of audio issues when streaming your favorite shows or movies. These days, there’s no shortage of Bluetooth speakers worth buying that can work with your Fire TV Stick, such as the Anker Soundcore 2, Marshall Stanmore III, and Sonos Move 2. With this, you can get better sound than your TV speakers, and you can also move your speakers to your preferred location.
For those who are already invested in the Amazon smart home ecosystem, you can hook up the Fire TV Stick to your Alexa-powered Echo speakers. With this alone, it introduces a ton of additional possibilities for your integrated smart home experience. Apart from voice control options, it can be used as a component in creating automated scenes that work with other Alexa-compatible devices, such as light bulbs, scent machines, and smart switches. For example, some Alexa automations compatible with your Fire TV Stick can optimize your bedtime routine or turn everything off after movie nights.
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2. Headphones and earbuds
Yuganov Konstantin/Shutterstock
While some people are lucky enough to live in places where they can turn on the loudspeakers freely while accessing their favorite content, others need to be more mindful of their viewing habits. Thankfully, just because you’re watching from a TV doesn’t mean the whole neighborhood has to watch with you. Whether you want some privacy or just to avoid an angry neighbor knocking on your door, you can pair your Bluetooth headphones with your Amazon Fire TV devices. In recent times, there is no shortage of multi-point Bluetooth Headphones and Earbuds that can work with your Amazon Fire TV Stick. For example, Apple users will be relieved to know that its AirPods, AirPods Pro, and AirPods Max all work with it.
But take note: the same issues other devices have with Bluetooth headphones and earphones apply, such as audio latency, which you’ll need to resolve using AV Sync Tuning. Not to mention, apart from commercial headphones and earbuds, some Amazon Fire TV devices also work with hearing aids, including several of its TV offerings and the Fire TV Cube (2nd- and 3rd-generation models). Among compatible hearing aids, it lists Starkey, Widex, and Cochlear hearing devices. Although you may need to check with your specific model. In 2025, Amazon released a few new features that make its Fire TV devices more accessible, such as the Dual Audio option, which allows hearing aid users and others to listen to audio at adjusted loudness levels simultaneously.
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3. Bluetooth game controllers
Even though many smart TVs can perform the same functions, the Amazon Fire TV Stick still does a lot of things better, such as navigation, software experiences, and cloud gaming. In 2020, Amazon launched Luna Cloud Gaming, which lets people run its library of games on Amazon’s remote servers. Depending on your preferences, you can choose a subscription model that suits the kinds of games you play most often.
According to Amazon, certified Luna-compatible controllers include the official Luna Controller, PlayStation 4 DualShock 4 Wireless Controller, Xbox One Controller, and the Google Stadia Controller. Additionally, owners of the PS5’s DualSense Controllers have been able to use them effectively. Although some people may claim that their 3rd-party controllers from other manufacturers work with their Fire TV Stick, it’s important to note that you will not have the same protection, assurance, or expected longevity as with official ones.
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Regardless of which model you choose, you’ll still want to make sure you have the right network and device settings to enjoy your Bluetooth controllers. Apart from having a fast enough connection, you’ll also want to turn on Game Mode when possible. Not to mention, compatibility isn’t entirely guaranteed for everything and still depends on the specific game you are playing. In a jiffy, you can opt to use the Luna Controller app on your mobile phone instead.
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4. Bluetooth mice and keyboards
Devices like the Fire TV Stick solve many problems, but they also introduce new ones. One of the most annoying, yet somewhat universal, experiences for anyone who has used a streaming device is finding it difficult to navigate with the remote. In fact, while the Amazon Fire TV Stick lets you browse the internet with your TV using Amazon Silk, it can be a nightmare to type all the website names and click all the right buttons.
If you want a sleek-looking wireless keyboard, something like the Logitech K380 Multi-Device Bluetooth Keyboard lets you pair with up to three devices, so you don’t have to unpair it from your computer to use it with your Fire TV Stick. But if you’re looking for something more ergonomic, there are even Bluetooth mice with side-scrolling, like the Logitech MX Master 3S, Keychron M6 Wireless Mouse, and Razer Basilisk V3 Pro.
If you don’t own a Bluetooth keyboard or mouse, all hope is not lost. As we’ve mentioned before, you can use a micro-USB OTG splitter to plug in a wired keyboard or mouse to your Fire TV Stick. So, if you still prefer using a wired peripheral or have already maxed out the number of devices you can connect to your Fire TV stick, this is a possible alternative.
According to new technical analyses from Google and mobile security firm iVerify, Coruna’s technical core comprises five complete exploit chains and 23 distinct iOS vulnerabilities that bypass most of the major software defenses Apple has shipped in versions 13 through 17.2.1, effectively turning a web page into a silent infection… Read Entire Article Source link
“People want something that lasts them a long time, that is quality, that is useful,” says Google senior director Alexander Kuscher. “Eventually, when it breaks or when you lose it, you get a new one because you feel taken care of. So I think that builds trust, and the trust is important.”
Flex started as an enterprise service for businesses; Google offered companies worried about security vulnerabilities on aging hardware a way to easily update to a more secure operating system. Or, at least, one that still received updates. After a while, other users started to get ahold of the software, downloading and installing it on their own USB sticks for their personal machines. “We didn’t make it particularly easy at the time,” Kuscher says. “But people did it.”
What led to the more consumer-oriented push of ChromeOS Flex—like this partnership with Back Market—was the end of software support for Microsoft’s Windows 10 operating system last fall. While the OS still technically works, it stopped receiving security updates, and Microsoft has encouraged users to update to Windows 11. But Windows 11 has specific hardware requirements, and it may not be a simple upgrade on certain machines. Google saw this as a moment to provide a cheaper alternative to the “Windows 10 cliff,” as Kuscher puts it. Back Market agreed.
“Ultimately, [Microsoft is] saying that people need to throw away their existing laptop to buy another one,” Hug de Larauze says. “And we say politely, no.”
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If you’re tech-savvy, you can forgo Back Market’s $3 stick and download ChromeOS Flex onto a USB drive you have lying around right now.
Buying Refurb
Back Market has done very well for itself despite economic turmoil. As devices become more expensive, people turn to cheaper, refurbished options. He compares the device market to the auto industry.
“Ninety percent of cars are being sold pre-owned,” Hug de Larauze says. “The new normal is to purchase them pre-owned because it’s almost dumb to buy a new one.”
When US president Donald Trump announced sweeping tariffs last year, Hug de Larauze says Back Market sales tripled afterwards. Even after the dust settled a little and it became clear that tariffs would not directly affect smartphones or computers, Hug de Larauze says sales stayed around twice what they’d been before. Back Market made $3.8 billion in 2025, making the company profitable for the first time. While Hug de Larauze says these kinds of economic fluctuations may be good for sending more people to Back Market, he hopes it will shift buyer mindsets to buying refurbished tech writ large.
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“We have one planet, and resources are limited,” Hug de Larauze says. “We need to do more with what we already have in every sector. Fashion is the same, transportation is the same, energy is the same, it’s the same for everything.”
If you’re looking to pre-order Apple’s new Studio Display XDR monitor today but have an older Mac, beware of some potential issues. According to the compatibility list spotted by Apple Insider, the new display will only work at 60Hz and not at its full 120Hz refresh rate on some older and less powerful Silicon models. Moreover, support for older Intel Macs isn’t mentioned at all for either the Studio Display XDR or cheaper Studio Display.
All Apple Silicon Macs will work with both monitors, including those with the oldest M1 chips, according to the support pages. However, the compatibility list for the Studio Display XDR includes this nugget: “Mac models with M1, M1 Pro, M1 Max, M1 Ultra, M2, and M3 support Studio Display XDR at up to 60Hz. All other Studio Display XDR features are supported.” So even if you have a hotrod M1 Ultra-based Mac, the Studio Display XDR’s refresh rate is capped at 60Hz — despite the fact that the chip can drive third-party monitors at 120Hz.
Similarly, only the iPad Pro M5 supports the Studio Display XDR at 120Hz, with all other compatible models (in the iPad Pro and iPad Air family) limited to 60Hz.
Intel Mac support isn’t mentioned at all in the compatibility list for either display, though they may function in some limited manner when connected. Intel Macs just received their last new OS update with macOS Tahoe (and only three more years of security updates), but it’s still surprising that they’re not compatible with Apple’s latest monitors.
Self-driving cars often struggle with with situations that are commonplace for human drivers. When confronted with construction zones, school buses, power outages, or misbehaving pedestrians, these vehicles often behave unpredictably, leading to crashes or freezing events, causing significant disruption to local traffic and possibly blocking first responders from doing their jobs. Because self-driving cars cannot successfully handle such routine problems, self-driving companies use human babysitters to remotely supervise them and intervene when necessary.
This idea—humans supervising autonomous vehicles from a distance—is not new. The U.S. military has been doing it since the 1980s with unmanned aerial vehicles (UAVs). In those early years, the military experienced numerous accidents due to poorly designed control stations, lack of training, and communication delays.
As a Navy fighter pilot in the 1990s, I was one of the first researchers to examine how to improve the UAV remote supervision interfaces. The thousands of hours I and others have spent working on and observing these systems generated a deep body of knowledge about how to safely manage remote operations. With recent revelations that U.S. commercial self-driving car remote operations are handled by operators in the Philippines, it is clear that self-driving companies have not learned the hard-earned military lessons that would promote safer use of self-driving cars today.
While stationed in the Western Pacific during the Gulf War, I spent a significant amount of time in air operations centers, learning how military strikes were planned, implemented and then replanned when the original plan inevitably fell apart. After obtaining my PhD, I leveraged this experience to begin research on the remote control of UAVs for all three branches of the U.S. military. Sitting shoulder-to-shoulder in tiny trailers with operators flying UAVs in local exercises or from 4000 miles away, my job was to learn about the pain points for the remote operators as well as identify possible improvements as they executed supervisory control over UAVs that might be flying halfway around the world.
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Supervisory control refers to situations where humans monitor and support autonomous systems, stepping in when needed. For self-driving cars, this oversight can take several forms. The first is teleoperation, wherea human remotely controls the car’s speed and steering from afar. Operators sit at a console with a steering wheel and pedals, similar to a racing simulator. Because this method relies on real-time control, it is extremely sensitive to communication delays.
The second form of supervisory control is remote assistance. Instead of driving the car in real time, a human gives higher-level guidance. For example, an operator might click a path on a map (called laying “breadcrumbs”) to show the car where to go, or interpret information the AI cannot understand, such as hand signals from a construction worker. This method tolerates more delay than teleoperation but is still time-sensitive.
Five Lessons From Military Drone Operations
Over 35 years of UAV operations, the military consistently encountered five major challenges during drone operations which provide valuable lessons for self-driving cars.
Latency
Latency—delays in sending and receiving information due to distance or poor network quality—is the single most important challenge for remote vehicle control. Humans also have their own built-in delay: neuromuscular lag. Even under perfect conditions, people cannot reliably respond to new information in less than 200–500 milliseconds. In remote operations, where communication lag already exists, this makes real-time control even more difficult.
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In early drone operations, U.S. Air Force pilots in Las Vegas (the primary U.S. UAV operations center) attempted to take off and land drones in the Middle East using teleoperation. With at least a two-second delay between command and response, the accident rate was 16 times that of fighter jets conducting the same missions . The military switched to local line-of-sight operators and eventually to fully automated takeoffs and landings. When I interviewed the pilots of these UAVs, they all stressed how difficult it was to control the aircraft with significant time lag.
Self-driving car companies typically rely on cellphone networks to deliver commands. These networks are unreliable in cities and prone to delays. This is one reason many companies prefer remote assistance instead of full teleoperation. But even remote assistance can go wrong. In one incident, a Waymo operator instructed a car to turn left when a traffic light appeared yellow in the remote video feed—but the network latency meant that the light had already turned red in the real world. After moving its remote operations center from the U.S. to the Philippines, Waymo’s latency increased even further. It is imperative that control not be so remote, both to resolve the latency issue but also increase oversight for security vulnerabilities.
Workstation Design
Poor interface design has caused many drone accidents. The military learned the hard way that confusing controls, difficult-to-read displays, and unclear autonomy modes can have disastrous consequences. Depending on the specific UAV platform, the FAA attributed between 20% and 100% of Army and Air Force UAV crashes caused by human error through 2004 to poor interface design.
UAV crashes (1986-2004) caused by human factors problems, including poor interface and procedure design. These two categories do not sum to 100% because both factors could be present in an accident.
The self-driving industry reveals hints of comparable issues. Some autonomous shuttles use off-the-shelf gaming controllers, which—while inexpensive—were never designed for vehicle control. The off-label use of such controllers can lead to mode confusion, which was a factor in a recent shuttle crash. Significant human-in-the-loop testing is needed to avoid such problems, not only prior to system deployment, but also after major software upgrades.
Operator Workload
Drone missions typically include long periods of surveillance and information gathering, occasionally ending with a missile strike. These missions can sometimes last for days; for example, while the military waits for the person of interest to emerge from a building. As a result, the remote operators experience extreme swings in workload: sometimes overwhelming intensity, sometimes crushing boredom. Both conditions can lead to errors.
When operators teleoperate drones, workload is high and fatigue can quickly set in. But when onboard autonomy handles most of the work, operators can become bored, complacent, and less alert. This pattern is well documented in UAV research.
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Self-driving car operators are likely experiencing similar issues for tasks ranging from interpreting confusing signs to helping cars escape dead ends. In simple scenarios, operators may be bored; in emergencies—like driving into a flood zone or responding during a citywide power outage—they can become quickly overwhelmed.
The military has tried for years to have one person supervise many drones at once, because it is far more cost effective. However, cognitive switching costs (regaining awareness of a situation after switching control between drones) result in workload spikes and high stress. That coupled with increasingly complex interfaces and communication delays have made this extremely difficult.
Self-driving car companies likely face the same roadblocks. They will need to model operator workloads and be able to reliably predict what staffing should be and how many vehicles a single person can effectively supervise, especially during emergency operations. If every self-driving car turns out to need a dedicated human to pay close attention, such operations would no longer be cost-effective.
Training
Early drone programs lacked formal training requirements, with training programs designed by pilots, for pilots. Unfortunately, supervising a drone is more akin to air traffic control than actually flying an aircraft, so the military often placed drone operators in critical roles with inadequate preparation. This caused many accidents. Only years later did the military conduct a proper analysis of the knowledge, skills, and abilities needed to conduct safe remote operations, and changed their training program.
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Self-driving companies do not publicly share their training standards, and no regulations currently govern the qualifications for remote operators. On-road safety depends heavily on these operators, yet very little is known about how they are selected or taught. If commercial aviation dispatchers are required to have formal training overseen by the FAA, which are very similar to self-driving remote operators, we should hold commercial self-driving companies to similar standards.
Contingency Planning
Aviation has strong protocols for emergencies including predefined procedures for lost communication, backup ground control stations, and highly reliable onboard behaviors when autonomy fails. In the military, drones may fly themselves to safe areas or land autonomously if contact is lost. Systems are designed with cybersecurity threats—like GPS spoofing—in mind.
Self-driving cars appear far less prepared. The 2025 San Francisco power outage left Waymo vehicles frozen in traffic lanes, blocking first responders and creating hazards. These vehicles are supposed to perform “minimum-risk maneuvers” such as pulling to the side—but many of them didn’t. This suggests gaps in contingency planning and basic fail-safe design.
The history of military drone operations offers crucial lessons for the self-driving car industry. Decades of experience show that remote supervision demands extremely low latency, carefully designed control stations, manageable operator workload, rigorous, well-designed training programs, and strong contingency planning.
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Self-driving companies appear to be repeating many of the early mistakes made in drone programs. Remote operations are treated as a support feature rather than a mission-critical safety system. But as long as AI struggles with uncertainty, which will be the case for the foreseeable future, remote human supervision will remain essential. The military learned these lessons through painful trial and error, yet the self-driving community appears to be ignoring them. The self-driving industry has the chance—and the responsibility—to learn from our mistakes in combat settings before it harms road users everywhere.
As the US administration proceeds to drop Anthropic as a supplier, many are rallying around the AI company’s relatively ethical stance, creating ‘unprecedented demand’ for Claude.
Anthropic’s Claude has been fast becoming the darling of the AI enthusiasts, for development, research and enterprise work. Now it is facing the might of the US administration which is threatening to drop it entirely as a supplier after a falling out with the Pentagon over so-called “red lines” it would not pass.
With many in Silicon Valley supporting its relatively principled stand, and general users sending it to the top of the US Apple charts in recent days for free downloads – beating OpenAI’s ChatGPT for the first time – its flagship Claude.ai and Claude Code apps went down for around three hours on Monday (2 March), causing many to bemoan its absence. There are already reports of further outages as we write, although its latest update says “a fix has been implemented and we are monitoring the results”.
In a nostalgic post on LinkedIn yesterday, regular contributor to Silicon Republic, AI aficionado Jonathan McCrea wrote: “I now feel the same way about Claude being down as I used to about Twitter being down.”
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De facto boycott
Last night, treasury secretary Scott Bessent added his voice to the de facto US administration boycott of Anthropic products saying in a post on X that his department would terminate use of Anthropic products.
It follows a directive from president Donald Trump ordering US agencies to “phase out” their use of the AI company’s products, and his defence department labelling Anthropic a “supply-chain risk”, an allocation normally reserved for foreign suppliers from non-friendly states. Anthropic has been quick to say that this is a “legally unsound’ designation, and is expected to challenge the move in the courts.
Reuters is also reporting that it has seen memos to employees at the Department of Health and Human Services, asking them to switch to other AI platforms such as ChatGPT and Gemini, and at the State Department saying it was switching the model powering its in-house chatbot – StateChat – to OpenAI from Anthropic.
Financially it will surely deal a serious blow to Anthropic in the short term, but some commentators are arguing that it could be a pivotal moment for Anthropic as it may be seen by many as the relatively ethical choice when it comes to the AI giants.
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The recent Grok scandal has put a major question mark over xAI’s credentials and OpenAI’s Sam Altman clearly sees the reputational risk as he has been quick to claim that it is ensuring some guardrails in its contract with the Pentagon.
On X yesterday Altman claimed that these guardrails would ensure OpenAI would not be “intentionally used for domestic surveillance of US persons and nationals”.
The backstory
If you haven’t been following, Anthropic drew the ire of the US administration after a standoff with the Pentagon, where Anthropic refused to change its safeguards related to using its AI for fully autonomous weapons, or for mass surveillance of US citizens.
On Thursday (February 27), Anthropic’s Dario Amodei released an official statement saying Anthropic believed that in “a narrow set of cases, we believe AI can undermine, rather than defend, democratic values”.
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“Some uses are also simply outside the bounds of what today’s technology can safely and reliably do,” he said. “Two such use cases have never been included in our contracts with the Department of War, and we believe they should not be included.
“We support the use of AI for lawful foreign intelligence and counterintelligence missions. But using these systems for mass domestic surveillance is incompatible with democratic values.”
Amodei went on to say that partially autonomous weapons, like those used today in Ukraine, are vital to the defense of democracy. “But today, frontier AI systems are simply not reliable enough to power fully autonomous weapons. We will not knowingly provide a product that puts America’s warfighters and civilians at risk.”
It’s a debacle that is likely to roll on in coming days, and it remains to be seen whether Anthropic can withstand the unprecedented onslaught from its own government and rely on the support of users for its principled stand. In the short term, its challenge appears to be to meet the current demand on its systems.
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If your iPhone is running an outdated version of iOS, you may have 23 vulnerabilities that can be exploited by highly sophisticated toolkit being sold to bad actors.
Update to iOS 26 to avoid a sophisticated hacking toolkit
It is well known that law enforcement agencies and government entities rely on hardware like GrayKey to attempt a bypass of iPhone security. It seems that the United States Government may have created a monstrous exploit tool that is now being sold and spread to bad actors. A Wiredreport details data shared by Google’s Threat Intelligence Group and iVerify. Google explains how the exploit toolkit, named “Coruna,” spread, while iVerify shared its findings tying its origins to the US government. Continue Reading on AppleInsider | Discuss on our Forums
As my fellow pet parents will know, it’s amazing how quickly even the tiniest of dogs can demolish their toys and treat stash. We love and spoil them nonetheless. When you subscribe to BarkBox a fresh batch of cleverly themed treats and toys arrives at your doorstep. The costs of pet ownership can stack up quickly, especially if you’re buying your pooch a random gift box that goes well beyond the essentials. That’s why we have Barkbox promo codes and discount options ready to go for you.
Barkbox Promo: Enjoy a Free Toy for a Year at Barkbox
When your monthly Barkbox arrives, it’s like Christmas morning for your dogs. I watch as my two dogs, Rosi and Randy, shake their little Chihuahua mix bodies with barely restrained excitement. They’re never gentle on their toys but the stimulation that comes from textures and chewing is good for their little brains. With Barkbox you get a steady supply of two unique toys and two bags of all-natural treats every month. If you want to see how your dogs react, this Barkbox coupon is good for new Barkbox subscription customers and adds an additional toy in your box every month for a year.
Save 50% on Your First Barkbox Food Subscription With a Barkbox Coupon Code
Another reason why Barkbox is the best dog subscription box is how easy the company makes it to keep your pantry stocked with your dog’s food. Use this Barkbox coupon to save 50% off your first Barkbox food subscription, so you won’t have to end up running out to the grocery store in the middle of the night when your scooper scrapes across the bottom of an empty kibble bin.
Fly Travel Stress-Free With Your Dog and Get $300 Off BARK Air Flights
If you live in a Barkbox flight hub destination, please know I am insanely jealous of you. It’s no secret that flying is stressful and can be very dangerous for pets, especially if they have to ride in a cargo hold. Barkbox makes them the VIP with BARK Air, letting them ride in the cabin with you and get doted on, so things are a lot less scary. This is another perk of having a BarkBox subscription, with the opportunity to save $300 off BARK Air Flights.
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Support Your Dog’s Dental Health and Get $10 Off With a Barkbox Coupon
Dental health is crucial for dogs, as it can prevent disease not just in their mouths, but their vital organs. Don’t forget to schedule your yearly cleaning with your vet, but in the meantime, use this BarkBox discount code to get $10 off a special BarkBox Dental kit.
Get an Extra Premium Toy in Every BarkBox With the Extra Toy Club
For having such tiny mouths, my dogs can gnaw through toys with surprising speed. If you’re also buried in a pile of shredded fluff and squeakers from disemboweled toys, the Extra Toy Club can help. This subscription includes dog toys for aggressive chewers of all ages, breeds, and sizes, offering extra durable toys meant to last longer. So far, so good at my house. To upgrade to this subscription box, it’s an extra $9 per month.
Get Exclusive BarkBox Discounts: Join the Email List
If you assume that the punchy branding and witty lingo extend to Barkbox’s email subscribers and not just the box subscription, you’d be correct. As a bonus, you can get exclusive BarkBox discount codes when you sign up to receive these emails. Who also doesn’t love a furry face and reminder of their pet in between work subject lines and bill payment reminders, too?
At the base of Jacob’s Ladder are classical models that treat atoms as simple balls connected by springs—fast enough to handle millions of atoms over long times but with the lowest precision. Moving up along the black line, semiempirical methods add some quantum mechanical calculations. Next are approximations based on Hartree-Fock (HF) and density functional theory (DFT), which include full quantum behavior of individual electrons but model their interactions in an averaged way. The greater accuracy requires significant computing power, which limits them to simulating molecules with no more than a few hundred atoms. At the top are coupled-cluster and full configuration interaction (FCI) methods—exquisitely accurate but, at the moment, restricted to tiny molecules or subsets of electrons due to the large computational costs involved. Quantum computing can bend the accuracy-versus-cost curve at the top of Jacob’s Ladder [orange line], making highly accurate calculations feasible for large systems. AI, trained on this quantum-accurate data, can flatten this curve [purple line], enabling rapid predictions for similar systems at a fraction of the cost of classical computing.Source: Microsoft Quantum
A joint project between Microsoft and Pacific Northwest National Laboratory used AI and high-performance computing to identify potential materials for battery electrolytes. The most promising were synthesized [top and middle] and tested [bottom] at PNNL. Dan DeLong/Microsoft
