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"We may have accidentally detected dark matter back in 2019," writes ScienceAlert. "What if instead of trying to see dark matter, scientists attempted to hear it instead?" asks Space.com: New research suggests dark matter could leave a tiny but discernible imprint in the cacophony of ripples in spacetime called "gravitational waves" that ring through the cosmos when two black holes slam together and merge... Fortunately, when it comes to detecting gravitational waves from colliding black holes, humanity's instruments, such as LIGO (Laser Interferometer Gravitational-Wave Observatory), are getting more and more sensitive all the time... Vicente and colleagues searched through data gathered by LIGO and its fellow gravitational wave detectors, KAGRA (Kamioka Gravitational Wave Detector) and Virgo, focusing on 28 of the clearest signals from merging black holes. Of these, 27 appeared to have come from mergers that occurred in the relative vacuum of space. One signal, however, GW190728, first heard on July 19, 2019, and the result of merging binary black holes with a combined mass of 20 times that of the sun and located an estimated 8 billion light-years away, seemed to carry the telltale trace of this merger occurring in a region of dense, "buttery" dark matter. The team behind this research is quick to point out that this can't be considered a positive detection of dark matter, but does say it gives us a hint at what to look for and thus where to direct follow-up investigations... "We know that dark matter is around us. It just has to be dense enough for us to see its effects," said team leader Josu Aurrekoetxea, of the Massachusetts Institute of Technology (MIT) Department of Physics. "Black holes provide a mechanism to enhance this density, which we can now search for by analyzing the gravitational waves emitted when they merge." They published their results this week in the journal Physical Review Letters.

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Test scores "are lower than they were a decade ago in school districts across the U.S.," reports Times magazine, citing new data released Wednesday by Stanford researchers. "Reading scores were down roughly 0.6 grades in 2025 compared to 2015, and math scores were down about 0.4 grades. This means that students were 60% of one school year behind where their peers were in reading a decade earlier and 40% of one school year behind in math." But Stanford's announcement notes that America's schools "were in a 'learning recession' for seven years before the COVID-19 pandemic, with student test scores in math and reading on a steady decline since 2013." This reversal ended two decades of progress, according to Sean Reardon, the Professor of Poverty and Inequality at Stanford Graduate School of Education, whose data forms the backbone of the new research... The study reframes the narrative of pandemic-era learning loss, arguing that the crisis of the last few years was an acceleration of a problem that was already underway. "The pandemic was the mudslide that followed seven years of erosion in student achievement," said Professor Tom Kane, faculty director of the Center for Education Policy Research at Harvard University, and a lead author of the report... The study found that the slowdown in learning coincided with two major shifts in American childhood and education policy: the widespread dismantling of test-based accountability systems that defined the No Child Left Behind era and the rise of social media use among young people. Reading scores, in particular, suffered consistently, with the average annual loss in the years just before the pandemic being just as large as the loss during it... Today, 8th-grade reading scores on national assessments are at their lowest point since 1990. Compounding the problem, chronic student absenteeism remains a major obstacle to improving learning. Though down from its pandemic peak, 23 percent of students were chronically absent in the 2024-25 school year, far above the pre-pandemic rate of 15 percent. More context from Time magazine: Reading scores were down roughly 0.6 grades in 2025 compared to 2015, and math scores were down about 0.4 grades. This means that students were 60% of one school year behind where their peers were in reading a decade earlier and 40% of one school year behind in math... "The decline started around the time that social media's use among teens was exploding, and this was also occurring in a number of other countries," says Thomas Kane, one of the authors of the Educational Scorecard report and a professor at Harvard University... [H]e maintains that it is at the core of the decline in reading achievement. He points out that social media use was shown to be heaviest among the lowest achieving students. "Some states and school districts are making progress," notes the Associated Press, "largely by shifting toward phonics-based instruction and providing extra support for struggling readers." And "The picture is also brighter in math. Almost every state in the analysis saw improvements in math test scores from 2022 to 2025."

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An anonymous reader shared this report from Electrek: When Fisker Inc. filed for Chapter 11 bankruptcy in June 2024, it left roughly 11,000 Ocean SUV owners holding the keys to vehicles that cost them anywhere from $40,000 to $70,000 — and that were rapidly losing the software brains that made them work. No more over-the-air updates. No more connected services. No more warranty. The manufacturer was dead. What happened next is one of the most remarkable stories in the history of the electric vehicle industry. Instead of accepting that their cars would become rolling paperweights, Fisker Ocean owners organized, reverse-engineered their vehicles' proprietary software, hacked into CAN bus networks, built open-source tools on GitHub, and effectively stood up a volunteer-run open-sourced car company from the ashes of Fisker... Within months of the bankruptcy filing, thousands of Ocean owners formed the Fisker Owners Association (FOA) — a nonprofit that quickly grew to 4,000 members and began operating as something between a car club, a tech startup, and an independent automaker. The FOA hired independent tech experts who began reverse-engineering Fisker's proprietary software patches. Members taught each other how to flash firmware. They organized bulk purchases of replacement parts — negotiating the price of key fobs down from roughly $1,000 each to a fraction of that through coordinated group buys. They hosted free global key fob pairing events, saving each owner $100 to $250... What started as desperate troubleshooting has evolved into a genuine open-source ecosystem around the Fisker Ocean. On GitHub, a developer named MichaelOE reverse-engineered the API behind Fisker's official "My Fisker" mobile app and built a Home Assistant integration that exposes every cloud API value as a sensor — with all the app's buttons available as Home Assistant controls... [Community members have also been systematically mapping CAN bus files.] The article noes this "is not an isolated incident. Nikola also filed for bankruptcy, leaving its owners in a similar bind. Canoo and Arrival are headed for liquidation auctions..." Consumer advocates are now pushing for structural changes: mandatory software escrow funds that would keep vehicle software running even if the manufacturer disappears, open-source mandates in bankruptcy proceedings, and shared repair data requirements... European automakers, meanwhile, are moving in a different direction entirely — Volkswagen, BMW, Mercedes-Benz, and eight suppliers signed a memorandum in 2025 to develop a shared open-source automotive software platform.... The Fisker Owners Association has proven that a dedicated community can keep orphaned EVs on the road. But they shouldn't have had to... [O]wners shouldn't need to become hackers and parts brokers and quasi-manufacturers just to keep driving the cars they already paid for.

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Long-time Slashdot reader internet-redstar shares an interestging response to "the recent wave of Linux kernel privilege escalation vulnerabilities like 'Copy Fail' and 'Dirty Frag'": Belgian Linux sysadmin and Tesla Hacker "Jasper Nuyens" got tired of the idea of manually blacklisting dozens or even hundreds of obscure kernel modules across large fleets of Linux systems in the near future. So he wrote ModuleJail, a GPLv3 shell script that scans a running Linux system and automatically blacklists currently unused kernel modules, reducing kernel attack surface without requiring a reboot. The idea is simple: many modern Linux privilege escalation bugs target obscure or rarely used kernel functionality that is still enabled by default on servers that do not actually need it. ModuleJail works across major distributions including Debian, Ubuntu, RHEL, Fedora, AlmaLinux and Arch Linux, generating 1 modprobe blacklist rules file while preserving commonly-used modules. Nuyens argues that the increasing speed of AI-assisted vulnerability discovery will likely turn kernel hardening and attack surface reduction into a much bigger operational priority for sysadmins over the next few weeks and months.

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After nearly four years and hundreds of billions burned building smarter and more capable models, folks understandably would like to see them do something more than run a chatbot. In this respect, OpenClaw served like blood in the water, demonstrating that, in spite of its seemingly endless supply of security flaws, LLMs really can be used to automate complex tasks. Since then, you've probably noticed the term "harness" coming up more frequently to describe agentic AI frameworks, and for good reason. You don't need a harness to interact with a chatbot – local tools like Ollama send API calls directly to the LLMs – but to do today's advanced work, they are essential. On their face, AI harnesses are just a bit of code that wraps around an LLM's API endpoint, orchestrates tool calls, and manages context. OpenClaw, Claude Code, Codex, and Pi Coding Agent are all examples of code-focused harnesses you may already be familiar with. As simple as all this sounds, harnesses are changing the way we think about everything from training new models to how we build and run them at scale. LLM inference on its own is pretty dumb – not the models so much as the way we interact with them. The OpenAI-compatible API calls that have become the de facto standard are transactional. With most early chatbots, you made a request and the API would supply a response. A harness, by comparison, orchestrates those API calls, breaking down one request into multiple. If you were to ask a code agent to build an app that parses logs, the harness might make one request to plan things out, another to review the log directory, a third to generate and execute that code in an interpreter, and a fourth to debug and fix any errors. This multi-step loop would continue until the work is done or the harness cuts it short to ask for user input. At least for coding, these harnesses are getting good enough to be useful. In fact, a harness may have a bigger impact on whether the code assistant will be successful than the model itself. Even Qwen3.6-27B, a small-to-medium-sized LLM, proved to be a surprisingly effective alternative to larger paid models when paired with harnesses like Anthropic’s Claude Code or Cline. And yes, if you didn’t know, Claude Code works with any model you like. In fact, the realization that small models with well-designed harnesses can now automate complex tasks has contributed to a shortage of Mac Minis, as AI enthusiasts race to self-host OpenClaw and LLMs on them. Changing the way we build models Training dominated the first two years of the AI boom. OpenAI, Google, Microsoft and others raced to build smarter models using as much data as they could harvest. But by the end of 2024, the payoff of building ever larger models started to taper off, as the extra parameters only engendered small gains in intelligence. DeepSeek R1 brought “reasoning” models and test-time scaling to the mainstream. To be clear, these models don’t actually reason, but instead trade time and tokens for higher quality answers and a lower propensity to make stuff up (aka "hallucinate," although we at El Reg try to avoid anthropomorphizing AI). It wasn’t the first. OpenAI’s o1 beat them to it, but R1 was the first widely adopted open weights model that used reinforcement learning (RL) to teach the model new skills, like chain-of-thought reasoning. Over the past year, agentic code assistants have steadily gained traction. Consequently, people are increasingly using RL to teach models to use the tools and resources that agent harnesses expose to them. If you look at many of the recent model releases on Hugging Face, you’ll notice a strong emphasis on agentic tool calling and long-context reasoning. If you want a model to work effectively with an agent harness, it needs to execute tool calls reliably. And since those tool calls can return large quantities of information, you also need the model not to lose track of that information. While these qualities make for better agentic models, they also require a very different set of hardware. CPUs take center stage Compute to run these agent harnesses is in high demand. After living in the shadow of high-end GPUs and AI accelerators for the past few years, CPUs are back in the limelight. Intel Xeon processors are selling faster than Intel can make them. Meta is buying up every chip it can get from Arm and Nvidia, and renting boatloads of Amazon’s Graviton CPUs while it awaits delivery. This is happening because agent harnesses don’t run on GPUs. Even with enough CPU cores to execute these tasks at scale, the number of requests is also reshaping the way we run models. If you haven’t noticed, inference costs have been on the rise. OpenAI recently raised the price of GPT-5.5, Microsoft moved GitHub Copilot to a purely usage-based pricing model, and Anthropic could soon force Claude Code users onto its pricier “Max” subscriptions. Some of this is because of increased demand. Like it or not, vibe coding is catching on and probably isn’t going away. However, we suspect some of it may be down to the fact that these models are running on hardware that was originally built for training and is now having to play double duty for inference. Only in the last year and a half have we started to see inference-optimized systems like Nvidia’s NVL72 racks hit the market. AWS, AMD, and others are now racing to catch up with rack-scale compute platforms of their own. But it turns out that even these systems aren’t enough on their own. If agentic code harnesses are making dozens of requests, each generating hundreds of lines of code, inference performance becomes a major bottleneck. In the early days of ChatGPT, it might have been enough to churn out tokens faster than the average person could read. Remove the meatbag from the equation and speed becomes everything. GPUs are incredibly compute-dense parallel processors, but their memory isn’t great for the kind of auto-regressive large models these harnesses are being saddled to. Groq and Cerebras get their moment under the AI sun Faced with these challenges, infrastructure providers have adopted new compute architectures that combine GPUs with specialized AI accelerators. Nvidia’s acquihire of Groq is a prime example. Late last year, Nvidia dropped $20 billion to license the AI chipmaker’s language processing unit (LPU) chip tech and hire away its engineering staff. As we wrote at the time, Nvidia could have built its own SRAM-heavy decode accelerator, if it wanted to, but because it was faster to use someone else’s. By combining its compute heavy GPUs with Groq’s high-bandwidth LPUs, Nvidia was able to churn out more tokens faster and, in theory, improve the economics for AI agents. Higher interactivity is key for agentic workloads because they can now serve more requests in the same amount of time, or “think” about the information that’s been provided to them for longer. We’ve previously explored Nvidia’s new Groq-based LPXs back at GTC as well as the market dynamics behind the multi-rack architecture. AWS is using recently public Cerebras Systems' wafer-scale AI accelerators in much the same way, while Intel is now working with SambaNova on its own disaggregated compute architecture. The pendulum swings Given the sheer amount of compute these agent harnesses require, there’s a good chance we’ll start to see hyperscalers cut costs by offloading some of the work onto client devices. Because of the way these harnesses work, simpler requests like planning could be run on small models running locally on the user’s PC. In fact, Google appears to be doing just that. As we reported earlier this month, Google quietly began shipping as part of Chrome a small LLM that will eat up 4 GB of disk space, and presumably just as much memory when in operation. The model appears to power basic functionality like “help me write” functionally, scam detection, and other AI-assisted functions which have steadily invaded our browsers as of late. It’s not hard to imagine code agents doing something similar. A small local model could be used to draft and test code snippets while the larger cloud-hosted model is used to debug and correct errors, shifting much of the load off datacenters and onto client devices. For that to work, we’re going to need systems with a whole lot more high-speed memory, which poses a bit of a problem in light of the DRAM and NAND shortage. While user-facing agent harnesses could be used to shove some of the computational load onto customer devices, many still want to see agents carrying out entire departments' worth of work. Take the human out of the loop, and these agents wouldn’t be constrained by limitations of their fleshy masters and could work orders of magnitude faster given enough compute resources. So, just like the rise of PCs didn’t spell the end of mainframes, local AI will no is unlikely to end investors' obsession with ever hotter and more power hungry bit barns any time soon. ®

Are statistical programmers coalescing around a handful of popular languages? That's the question asked by the CEO of software assessment site TIOBE, which every month estimates the popularity of programming languages based on their frequency in search results: This month, the programming language R matched its all-time high by reaching position #8 in the TIOBE index once again. This is not a coincidence. The statistical programming language market is clearly undergoing a major consolidation. The biggest winners are Python and R, while many long-established alternatives continue to lose momentum. The era in which the statistical computing landscape was fragmented across many niche languages and platforms appears to be coming to an end. Several established players are steadily declining: — MATLAB is close to dropping out of the TIOBE top 20. — SAS is about to leave the top 30 for the first time since the TIOBE index began. — Wolfram/Mathematica remains well below its historical peak and is losing further ground. — SPSS dropped out of the top 100 last month.... Elsewhere in the index, Java and C++ swapped positions this month. Java gained momentum following the successful release of Java 26. Another notable riser is Zig, which is approaching the TIOBE top 30 for the first time. Zig's growing popularity appears to be driven by its rare combination of low-level performance, straightforward tooling, and relative ease of use compared to traditional systems programming languages. Their estimate for the most popular programming languages in May: PythonCJavaC++C#JavaScriptVisual BasicRSQLDelphi/Object Pascal The five next most popular languages on their rankings are Fortran, Scratch, Perl, PHP, and then Rust at #15. Rust is up for positions from May of 2025 — while Go has dropped to #16, seven ranks lower than its May 2025 position of #7.

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Chris Willis, chief design officer and futurist for data platform biz Domo, wonders why people aren't more annoyed with AI companies. Willis said he was in San Francisco a few weeks ago and he couldn't fathom the lack of resentment. "Why aren't people more resentful that these companies have pushed this technology upon them and now everyone is feeling a tremendous amount of anxiety," he told The Register in an interview. "I'm sure you've seen the surveys and the research. Everyone from the C-suite on down feels like the clock is ticking and their careers are on the line." San Francisco is the home of OpenAI and Anthropic. Google, Microsoft, and Amazon are also in town. So there's a lot of self-interested AI enthusiasm in the city by the bay. The resentment is there if you look beyond the billboard evangelism shouting its way down the US 101 corridor that connects the city to Silicon Valley proper. But the existential dread behind Stop AI, Pause AI, Poison Fountain, and the firebombing of OpenAI CEO's Sam Altman's home isn't quite what Willis has in mind. He's concerned with the way AI has been marketed through fear – act now or be left behind by this technology that might just take everyone's job and enable DIY biological weapons, now that LLMs can more reliably count the number of "r"s in "strawberry." "Fear," he said, "is not a durable strategy for innovating." The problem as Willis sees it begins with the fact that AI models are a product without a spec. "When you're trying to create a product and you're trying to figure out how that product fits in the market, you have to figure out who it's for and what it's going to do and what it's not going to do," he said. "And these large language models, essentially the feature spec is: 'It'll do anything for anyone, anyway, anyhow, in any language.'" So it's not surprising, he said, that there's some confusion. "From a leadership perspective, we've seen many times the pattern where there is a lot of pressure for companies to suddenly innovate with a technology that's not well understood," he said. "And so organizations are spending a lot on buying these AI tools and then expecting innovation to just happen. And that's not usually how innovation works." What company leaders face, he said, is not an innovation problem but an impatience problem. "They're thinking, 'we have to do something now," he said, "and so AI in many ways is becoming a sort of theater. We have to show that we're doing something." The phenomenon known as "tokenmaxxing" – buying access to AI models and directing or expecting employees to use them as much as possible – illustrates the lack of strategy, Willis said. "In certain organizations where AI is theater and impatience is driving rather than innovation, tokenmaxxing is a convenient way to feed that narrative," he said. "But it doesn't change anything. The research does suggest that you might have people putting through a lot of tokens and maybe they are personally becoming more productive. But it's not changing the bottom line." The deeper problem, he said, is that companies are treating AI itself as a solution rather than as a tool to help power the solution. The result is a lot of proof-of-concept projects that lack what's required to make them durable, trustworthy, and deployable at scale. Starting with business needs first is essential, Willis argues. "If you don't understand the process and the automations and the workflows in your business, you run the risk of putting in a very powerful engine that's going to drive your business way faster, but with the lights off, at night," he said. Willis suggests companies should not set moonshot goals for AI, and start with something simple, like automating processes tied to a spreadsheet. He described work done with one customer that involved developing an app to go through company invoices, check for discrepancies, and surface anomalies for review by a person. The clients were thrilled. Understanding where human judgement is required and where decisions can be verified and hence automated, is key, he said. "Usually that question is not asked." Failing to ask questions like that invites problems. Willis pointed to the way that Swedish fintech biz Klarna replaced customer service staff with AI, only to return to replace the AI with people. "It's very enticing to say we're just going to replace everything with a chatbot," he said. "Frankly, no customer ever just wants to talk to your chatbot." Willis said there's no magic for innovating. Companies need to do the hard work of understanding how AI may or may not be useful for the desired outcome. "There will be a reckoning when it comes to budgets around these things," he said, "because CFOs are starting to as 'Why are we spending all this money and not gaining anything?'" ®

xAI has launched Grok Build, "a coding agent of its own to serve as competitor to its rivals' products, such as Anthropic's Claude Code," reports Engadget: As Bloomberg notes, xAI has been trying to catch up to its rival companies like Anthropic and OpenAI. Elon Musk, the company's founder and CEO, previously admitted that it has fallen behind its competitors when it comes to coding. A couple of months ago, Musk said he was rebuilding xAI "from the foundations up" after several co-founders had left the company. One of the company's executives reportedly told staffers to work on getting Grok to match Claude's performance across various tasks. More details from PCMag: Grok Build is currently available in beta to those with a SuperGrok Heavy subscription, which starts at $300 per month. Just download it from the xAI website and log in. It's described as "a powerful new coding agent and CLI for professional software engineering and complex coding work." In its early version, xAI is seeking feedback and looking to fix any bugs... Only a few features have been highlighted, including a plan mode that lets you review, edit, and approve a plan before execution, and support for existing plug-ins and workflows.

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For those who miss what Windows looked like in 2009, Classic 7 is a heavily modified version of Windows 10 IoT LTSC, reworked to make it look as much as possible like Windows 7, while still being in support and receiving updates. This has been accomplished thanks to a large compilation of skins, themes, add-ons, tweaks, and so on – some of which are real components from older versions of Windows, adapted and modified to run on Windows 10. We were not sure whether to cover Classic 7, because while it is impressive and fun, we are not at all sure it is legitimate to use. But we can see a target audience. This isn't just a layer of makeup; it's more like a face transplant. It includes some real binaries from Windows 7, and indeed earlier versions, adapted and grafted onto Windows 10. One component is the Windows Media Center from Windows XP, which was cut from Windows 10 before release. The specific version of Windows 10 that it's modified is significant. It's Windows 10 IoT LTSC. We talked about this specific edition in April 2025 because it's the last version of Windows 10 that is still in support and receiving updates. The standard Windows 10 Enterprise LTSC release will continue to receive updates until 2027, and the IoT edition, which is only available in US English, will get updates until 2032 – so this is the longest-lived version of Windows 10. At the bottom of our story on Windows 10 LTSC, we mentioned the slightly shady world of third-party modified editions of Windows. Classic 7 is one; it's a modified version of an Enterprise edition of Windows, one that's only available for legitimate licensing via a Volume License Agreement. Unless you have appropriate volume licensing for the underlying Windows edition and have paid the fairly hefty fee, this is an unlicensed copy of Windows. So we have to spell out that this is not for production use, and you should not use it in any working environment. It's an interesting hack, though, and it might be a bit of fun for a home gaming machine or something like that. As an aside, one of the most widely used tools for activating unauthorized copies of Windows and Office, MassGrave, is in fact hosted on GitHub. In other words, Microsoft itself is hosting tools to activate unlicensed copies of Windows and Office. Whether that counts as tacit approval, we wouldn't like to say. Classic 7 has been under construction for over a year and a half, and it's the sequel to an earlier project called Reunion7 – also hosted on GitHub, as it happens. As its list of credits shows, Classic 7 is in part a compilation of a lot of existing tools. Some of them are relatively well known, such as Winaero Tweaker, which can run on any copy of Windows and, among lots of other options, allows some of the less desirable changes in the Windows UI to be undone – for instance, switching to the hidden Aero Lite theme. Classic 7 includes this and a lot more besides. We could identify some of the couple of dozen credited projects, such as the Aero11 theme, itself a port of Aero10 to Windows 11. This works alongside OpenGlass, which brings Aero-style transparency to Windows 10. There's also the Windows NT Modding Utility, and another hack that lets you change the Windows version number reported on the command-line, called Custom CMD Version Text. Multiple sub-components come from the Windhawk mods collection, some credited to a developer called ImSwordQueen, whose themes can be seen on DeviantArt. Other components are more than just cosmetic. For instance, the remarkable description of Explorer7: "explorer7 is a wrapper library that allows Windows 7's explorer.exe to run properly on modern Windows versions, aiming to resurrect the original Windows 7 shell experience." So this is not merely a theme for Windows 10 Explorer: as far as we can tell, it's the real Windows 7 Explorer, but running on top of 10. The same appears to apply to Control Panel as well, thanks to the Control Panel Restoration Pack. Thanks to the Windows Media Center (Modern Hardware) effort, this is the real XP version, which an on-screen message says replaced the Windows 8 version used in an older build. We tried Classic 7 in VMware, and the experience is quite uncanny. We did hit some glitches: our first installation failed when we let it do its own disk partitioning. Deleting all the partitions, manually creating a single large C: drive, and telling the installer to use that worked. A few error messages did appear here and there. Trying to change screen resolution went badly awry until we installed the VMware guest additions. Opening Windows Update just threw an error. Overall, though, it is genuinely remarkable. It looks and feels like Windows 7 – but in principle, you can run the latest apps and drivers and they should work. It even includes your choice of older Firefox versions, including version 115 ESR, skinned to look exactly like Internet Explorer – an effort called BeautyFox. Last year, we wrote a piece on running Windows 7 in 2025 and it really reminded us how great the 2009 release looked compared to anything that's come since. Apparently, that late-noughties translucent look is now known as Frutiger Aero, and frankly we miss it. In all honesty, we feel Classic 7 goes too far. We don't want Help/About dialog boxes, and even the winver tool and the ver command to lie to us. We'd prefer something that told the truth, but looked pretty while doing it. But as we wrote last year, some personal friends are still running Windows 7 by choice, and compatibility is starting to become a problem. If you want a recent Firefox, well, you're out of luck. Firefox 115 from 2023 still works, and remarkably, it's still getting security fixes now: the March end-of-life has been postponed again, and it's currently August 2026. The Irish Sea wing of Vulture Towers is still running it on OS X 10.13 and it works flawlessly. This is a way out: to keep the 17-year-old vintage look, while running a codebase that still has another five years in it. If you're that determined, it's an option… and it's undeniably an attractive GUI. Whether this unauthorized rebuild of an unlicensed OS is an attractive option, though – you must decide that for yourself. ®

England's Department for Education is advertising a role paying up to £200,000 a year to lead a new digital and infrastructure group overseeing school buildings and maintenance, as well as technology and data. Its Director General, Digital and Infrastructure, will lead the technology function of around 1,800 staff, develop a new strategy covering digital services, data, and artificial intelligence, and lead work on a unique identifier for children and other learners in England. Scotland, Wales, and Northern Ireland run education services on a devolved basis. The successful candidate will also implement a new strategy for "the education estate" of schools, colleges, nurseries, and children's homes. The job ad warns the function "carries some of the highest levels of risk and accountability in the department - including life-and-death decisions on safety," citing ongoing work to remove unsafe reinforced autoclaved aerated concrete (RAAC) from schools. "I am looking for a leader who is motivated by impact - someone who is able to combine their digital and data expertise with their drive to improve outcomes for children and young people," writes the department’s permanent secretary, Susan Acland-Hood, in a briefing document with the advert. "Whilst you do not need to be an expert on education policy, you need to be curious and committed to rapidly building your understanding of the latest evidence, system, and policy landscape." The department is willing to base the job in Bristol, Cambridge, Coventry, Darlington, London, Manchester, Nottingham, or Sheffield, although those who do not work in the capital will need to go there frequently. Applications close on June 1. Several other departments have recently advertised digital director-general posts, the civil service job category just below permanent secretary (equivalent to chief executive). In January, England's Department of Health and Social Care advertised the role of director general for technology, digital and data with a salary of up to £285,000 a year. In February, the Ministry of Defence offered £270,000 to £300,000 for its chief digital and information officer job. And in April, the Department for Science, Innovation and Technology advertised for three directors-general, one paid £174,000 and the other two paying between £200,000 and £260,000 annually. ®

Computer Weekly reports on "the long-awaited reform of Britain's outdated Computer Misuse Act of 1990 — which has hamstrung the work of the nation's cyber security professionals and researchers for years." The Computer Misuse Act was passed 35 years ago in response to a high-profile hacking incident involving no less than the King's father, the late Duke of Edinburgh. It defined the offence of unauthorised access to a computer — which has been used successfully in countless cyber crime prosecutions over the years. However, as the cyber security landscape has developed into its current form, this language has become increasingly vague and for some years now, a growing number of bona fide security professionals have been arguing that it potentially criminalises their work because from time to time, they may need to gain covert access to IT systems in the course of legitimate research. Speaking to Computer Weekly in 2025, Belfast-based security consultant Simon Whittaker described how the police showed up at his front door after his research was erroneously implicated in the infamous WannaCry incident of 2017... Sabeen Malik, vice-president for global government affairs and public policy at Rapid7, added: "As AI-driven vulnerability discovery scales, defenders need to run automated scanning, agentic red-teaming, and large-scale vuln research at machine speed — activities the 1990 Computer Misuse Act's broad unauthorised-access provisions were never designed to accommodate, leaving UK researchers exposed to criminal risk for work their adversaries face no equivalent friction performing." The reforms are part of a new bill that's "enhancing the powers available to law enforcement and the security services," according to the article. It points out that the U.K. government also intends "to create a Cyber Crime Risk Order that can be applied to control the behaviour of cyber criminals, and new abilities to search people believed to be concealing evidence on behalf of suspected offenders." It's all part of a proposed bill "designed to make the UK a harder target for hostile foreign states and other dangerous groups to attack."

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Today Amazon ends support for first- and second-generation versions of Kindles and Kindle Fire tablets, along with the Kindle Touch, the 9.7-inch Kindle DX, and other devices released in 2012 or earlier. Owners can continue reading ebooks that they've already downloaded, and they can also still sideload books using a USB cable (from, for example, Project Gutenberg). And PCMag points out that "There are plenty of e-stores where you can buy DRM-free novels legally, such as ebook.com and Smashwords. If you want to try this process for free, public-domain repositories such as the one at Standard Ebooks are a great place to start." (eBook files can be converted for the Kindle with the open source tool Calibre.) New ebooks can no longer be purchased directly from Amazon. But most of Amazon's affected devices "have not received firmware updates for over a decade," notes the blog OMG Ubuntu, "and most lost on-device access the Kindle Store." Some Kindle owners are taking things even further: You can unlock the firmware of older devices to add extra functionality (custom screensavers, epub support) or run entirely different software. On the hardware hacks side, some choose to turn old Kindles into photo frames or online dashboards. TechCrunch offers some caveats about jailbreaking: This process allows users to install custom fonts, new screensavers, alternative reading apps, and even third-party tools that expand the Kindle's functionality... [I]t's important to note that jailbreaking a Kindle might violate Amazon's terms of service. In many jurisdictions, jailbreaking isn't considered a criminal offense for personal use, but it may become a crime if it involves copyright infringement, illegal software distribution, or the sale of modified devices. Many Kindle owners who opt to jailbreak view it as a method to gain control over a device they purchased that is still functional, rather than being forced to buy a new device. However, jailbreaking is technical and carries risks, including the possibility of rendering the device unusable if something goes wrong. It also isn't possible on every Kindle model or firmware version, so before proceeding, Kindle owners should first spend some time researching if their device is compatible. Alternately, PCMag notes, "If you're feeling particularly virtuous, you can donate your old Kindle to a local library or send it back to Amazon free of charge via its electronic recycling program."

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An anonymous reader shared this report from Electrek: A Nevada utility just told 49,000 Lake Tahoe residents that it's redirecting 75% of their electricity supply to data centers, and they have less than a year to find a new power source. It's one of the starkest examples yet of the AI boom's impact on everyday Americans... NV Energy needs the capacity for data centers being built by Google, Apple, and Microsoft around the Tahoe-Reno Industrial Center east of Reno, according to Fortune... Data centers drove half of all US electricity demand growth last year.... That dynamic — small residential customers losing out to massive industrial electricity buyers — is exactly what's driving the broader shift to distributed solar and storage. When the grid becomes unreliable or unaffordable because of data center demand, the homeowners who have solar panels and a battery in the garage are the ones with options. "The shift is measurable," they argue: Third-party ownership models (leases and power purchase agreements), which still qualify for the [U.S.] commercial investment tax credit through 2027, are projected to grow 25% in 2026 and capture up to 69% of residential installations, up from roughly 45% in 2025. Homeowners aren't waiting for incentives to come back — they're finding new ways to get solar on their roofs... [A] battery that can store cheap solar energy and deploy it during peak hours is increasingly essential. California utility customers alone are adding roughly 8,000 new home batteries per month — about 100 MW of new storage capacity. Municipal programs are accelerating the trend. Ann Arbor, Michigan, recently became the first US city to directly deploy solar and battery systems on 150 homes through its city-owned utility. Vermont's Green Mountain Power is offering home batteries at little to no upfront cost. These programs signal that utilities themselves recognize the value of distributed energy.

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"Every link leads to an entry that does not exist yet," explains the GitHub page for a Wikipedia-like site called Halupedia. "Until you click it, at which point an LLM pretends it has always existed and writes it for you, in the deadpan register of a 19th-century scholarly press..." Every article is invented on demand. The footnotes are also lies... The hardest problem with an infinite, on-demand encyclopedia is internal contradiction... When the LLM writes an article, it is required to add a context="..." attribute on every <a> it inserts, summarising the future article it is linking to (e.g. context="19th-century clerk who formalized footnote drift, Pellbrick's mentor")... When that target article is later requested for the first time, the worker loads the accumulated hints and injects them into the system prompt as "PRIOR REFERENCES — these are CANON". The LLM is instructed that the encyclopedia is hallucinated and absurd, but it must not contradict itself. Fast Company reports that Halupedia was created by software developer BartÅomiej Strama, who confessed in a Reddit comment that the site came about after a drunk night with a friend. In the week since launch, he says Halupedia has amassed more than 150,000 users." Beyond indulging in silly alternate histories, what's the point of using Halupedia? Strama hinted at one larger purpose in a reply to a donor on his Buy Me a Coffee page: "Your contribution towards polluting LLM training data will surely benefit society!" he wrote. The site is licensed as free software under the GPL-3.0 license. Thanks to long-time Slashdot reader schwit1 for sharing the news.

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Former Microsoft programmer Keith Curtis "wrote and self-published After the Software Wars to explain the caliber of free and open source software," according to his entry on Wikipedia, "and why he believes Linux is technically superior to any proprietary OS." He's also KeithCu (long-time Slashdot reader #925,649), and has written a blog post on "How I added an LLM-based grammar checking + TeX math import to LibreOffice." : At Microsoft, I spent five years working on the text components RichEdit and Quill, and came to understand the "physics" of word processing: the file formats, data structures, and algorithms that provided fast access to text and properties, independent of the length of the file. Selecting one million characters to make them bold took about the same time as changing one character, because of the clever data structures (piece tables) and algorithms in these engines... When I decided to add a real-time AI grammar checker to [LibreOffice plugin] WriterAgent, I knew what I was getting into, but I underestimated the trickery of LibreOffice's UNO. His site shares the surprises he encountered, one by one. (Starting with "the office suite throws a bunch of initialization variables at your constructor. If your Python __init__ method doesn't handle them, the code fails to map the call, the stack misaligns, and the program dies.") There's sentence casing issues, duplicate words, and foreign-language syntax — all culminating in new features for "a LibreOffice extension (Python + UNO) that adds generative AI editing to Writer, Calc, and Draw..." "If you want to try it out, the repo is here... Let's make LibreOffice and the free desktop AI-native!"

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Bloomberg reports that Apple's two-year-old partnership with OpenAI "has become strained, according to people familiar with the matter." Bloomberg describes OpenAI as "failing to see the expected benefits from the deal and now preparing possible legal action." OpenAI lawyers are actively working with an outside legal firm on a range of options that could be formally executed in the near future, said the people, who asked not to be identified because the deliberations are private. That could include sending the iPhone maker a notice alleging breach of contract without necessarily filing a full lawsuit at the outset, according to the people... OpenAI believed that the companies' partnership, which wove ChatGPT into Apple software, would coax more users into subscribing to the chatbot. It also expected deeper integration across more Apple apps and prime placement within the Siri assistant. Instead, Apple's use of OpenAI technology across its operating systems remains limited, and features can be hard to find... Apple has had its own concerns about OpenAI, including whether the company does enough to protect user privacy. And a recent push [by OpenAI] to make devices — an effort overseen by former Apple executives — has rankled the iPhone maker. Any legal move by OpenAI likely wouldn't come until after the conclusion of the Musk trial, according to the people. No final decisions have been made, and OpenAI still hopes to resolve its issues with Apple outside of court. The article points out that OpenAI "initially believed the deal could generate billions of dollars per year in subscriptions — something that hasn't come close to happening." An OpenAI executive argues to Bloomberg that from a product perspective Apple hasn't done everything they could, "and worse, they haven't even made an honest effort."

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"Most of the plastic waste in California is about to lose the recycling symbol," writes the Washington Post's "climate coach." The "chasing arrows" symbol, created in 1970 by a college student inspired by the burgeoning environmental movement, has been stamped indiscriminately on plastic bottles, clamshell takeout containers, chip bags and more for decades. The majority of the items emblazoned with the mark have been virtually impossible to recycle for most people. California lawmakers say they want to end the charade: Under what's known as the Truth in Recycling law, plastics cannot use the symbol if they aren't collected by curbside programs serving 60% of Californians and sorted by facilities serving 60% of the state's recycling programs (with some additional requirements). If the law goes into effect as scheduled on October 4, more than half of the types of plastic packaging and products sold in the state can no longer carry the chasing arrows logo. That will affect plastic films, foam, PVC and mixed plastics... Food and packaging groups have sued the state of California, calling the law a form of censorship whose vague restrictions violate the First Amendment and due process rights.... Advocates of the law counter that corporations deliberately misled the public by turning the recycling symbol into a marketing device that masks the fact that only a small fraction of plastic packaging is ultimately recycled... The mark was originally intended to informwaste processors what polymers a plastic item was made from. But the public reasonably assumed anything stamped with the symbol was recyclable. Millions of tons of worthless plastic trash have since poured into recycling facilities unable to process it.... States are now taking action. Seven have passed laws shifting the cost of recycling onto packaging makers. Oregon and Washington have lifted requirements that plastic containers carry the chasing arrows symbol. The article notes that Norway already recovers 97% of beverage bottles, while Slovakia recycles 60% of plastic packaging. "But the U.S. only recovers about a third of its PET and HDPE bottles, and just 13% of plastic packaging, according to U.S. Plastics Pact, an industry-led forum. "It won't be easy for the U.S. to reach higher levels of recycling: The necessary infrastructure and incentives are chronically underfunded, no federal mandate exists for minimum-recycled-content that would create demand and a mix of mostly unrecyclable hydrocarbons still dominates the waste stream."

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"Most of the plastic waste in California is about to lose the recycling symbol," writes the Washington Post's "climate coach." The "chasing arrows" symbol, created in 1970 by a college student inspired by the burgeoning environmental movement, has been stamped indiscriminately on plastic bottles, clamshell takeout containers, chip bags and more for decades. The majority of the items emblazoned with the mark have been virtually impossible to recycle for most people. California lawmakers say they want to end the charade: Under what's known as the Truth in Recycling law, plastics cannot use the symbol if they aren't collected by curbside programs serving 60% of Californians and sorted by facilities serving 60% of the state's recycling programs (with some additional requirements). If the law goes into effect as scheduled on October 4, more than half of the types of plastic packaging and products sold in the state can no longer carry the chasing arrows logo. That will affect plastic films, foam, PVC and mixed plastics... Food and packaging groups have sued the state of California, calling the law a form of censorship whose vague restrictions violate the First Amendment and due process rights.... Advocates of the law counter that corporations deliberately misled the public by turning the recycling symbol into a marketing device that masks the fact that only a small fraction of plastic packaging is ultimately recycled... The mark was originally intended to informwaste processors what polymers a plastic item was made from. But the public reasonably assumed anything stamped with the symbol was recyclable. Millions of tons of worthless plastic trash have since poured into recycling facilities unable to process it.... States are now taking action. Seven have passed laws shifting the cost of recycling onto packaging makers. Oregon and Washington have lifted requirements that plastic containers carry the chasing arrows symbol. The article notes that Norway already recovers 97% of beverage bottles, while Slovakia recycles 60% of plastic packaging. "But the U.S. only recovers about a third of its PET and HDPE bottles, and just 13% of plastic packaging, according to U.S. Plastics Pact, an industry-led forum. "It won't be easy for the U.S. to reach higher levels of recycling: The necessary infrastructure and incentives are chronically underfunded, no federal mandate exists for minimum-recycled-content that would create demand and a mix of mostly unrecyclable hydrocarbons still dominates the waste stream."

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"The vulnerability is simple in practice," writes Tom's Hardware: "run a command as a standard user and gain root (administrator) access to the machine." And it was Mythos Preview that helped the security researchers at Palo Alto-based Calif bypass a five-year Apple security effort in just five days. The blog 9to5Mac reports: Last year, Apple introduced Memory Integrity Enforcement (MIE), a hardware-assisted memory safety system designed to make memory corruption exploits much harder to execute... [The researchers note it's built into Apple all models of the iPhone 17 and iPhone Air, and some MacBooks] They explain they have a 55-page technical report on the hack, but they won't release it until Apple ships a fix for the exploit. But they do note in broad terms that Anthropic's Mythos Preview model helped them identify the bugs and assisted them throughout the entire collaborative exploit development process. "Mythos Preview is powerful: once it has learned how to attack a class of problems, it generalizes to nearly any problem in that class. Mythos discovered the bugs quickly because they belong to known bug classes. But MIE is a new best-in-class mitigation, so autonomously bypassing it can be tricky. This is where human expertise comes in. Part of our motivation was to test what's possible when the best models are paired with experts. Landing a kernel memory corruption exploit against the best protections in a week is noteworthy, and says something strong about this pairing...." [I]n a time when even small teams, with the help of AI, can make discoveries such as this one, "we're about to learn how the best mitigation technology on Earth holds up during the first AI bugmageddon."

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Variety reports: Amazon MGM Studios started auditioning actors for the part of 007 in the past few weeks, Variety has learned... The next James Bond film will be directed by Denis Villeneuve, the filmmaker behind the "Dune" franchise, "Arrival" and "Sicario." Amy Pascal of the "Spider-Man" films and David Heyman of the "Harry Potter" series will produce the picture, which will feature a script from "Peaky Blinders" creator Steven Knight. Tanya Lapointe ("Dune") is executive producing the film. The BBC notes it's been five full years since the release of the last Bond film No Time To Die, and 15 months "since Amazon MGM Studios took control of the Bond franchise." But they also offer this list of "the current bookmakers' favourites" for who will become the seventh actor to play the gadget-loving super spy in the franchise's 64-year history: Callum Turner — the 36-year-old actor is the current bookies' frontrunner. He has been in the Fantastic Beasts franchise, was nominated for a Bafta for TV drama The Capture, and starred in Apple TV's Masters of the Air... Jacob Elordi — the Australian actor, 28, made his name in TV's Euphoria and cult hit film Saltburn, and was nominated for an Oscar this year for playing the monster in Frankenstein. The Rest Is Entertainment host Marina Hyde recently said she'd heard from a number of well-placed sources that he's now "in pole position" to be Bond. Harris Dickinson — the 29-year-old is playing John Lennon in the forthcoming major Beatles biopics, and has previously appeared in Maleficent, The King's Man, Where the Crawdads Sing and Babygirl, and received a Bafta TV Award nomination for A Murder at the End of the World. Henry Cavill — the Superman, The Witcher and Mission: Impossible actor is a fan favourite and was widely regarded to have been the runner-up when Craig landed the part. But at 43, is he now too old to start a lengthy stint as 007? Aaron Taylor-Johnson — the Bafta-nominated 35-year-old, known for films like Kick-Ass, Kraven the Hunter and 28 Years Later, is a perennial contender, and would fit the bill. Theo James — the suitably suave star, 41, made his name in the Divergent films and has since built his reputation in The Time Traveler's Wife, The White Lotus and The Gentlemen. ...Or producers could well go for one of the many other names who have been touted for the role, or an unexpected choice.

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