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Ford's sales of electrified vehicles — including hybrids and all-electric models — dropped 31% from April 2025, reports Electrek. "Hybrid sales fell 32% to 15,758 vehicles, while EV sales continued to crash with just 3,655 all-electric models sold last month, 25% fewer than in the year prior." After discontinuing the F-150 Lightning in December, sales of the electric pickup have been in free fall. Ford sold just 884 Lightnings last month, 49% less than it did last April. The Mustang Mach-E isn't doing much better. Sales fell another 9% year over year in April, to just 2,670 models last month. Through the first four months of 2026, Ford's EV sales have fallen 61% from last year, with F-150 Lightning and Mustang Mach-E sales down 67% and 50%, respectively. Ford has sold just over 10,500 electric vehicles in total so far this year... For comparison, Toyota sold just over 10,000 bZ models in the first quarter alone. That's more than Ford's total EV sales in Q1. April was Ford's fourth straight month of lower sales figures from 2025, the article points out. So Ford is bringing back "employee pricing" discounts on most new 2025 and 2026 Ford and Lincoln vehicles., while also offering "purchase incentives" of up to $9,000 for 2025 Lightning models and up to $6,000 for 2025 Mustang Mach-Es. "It's also offering EV buyers a free Level 2 home charger, 24/7 live support, and proactive roadside assistance through its Power Promise program."

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WHO, ME? Welcome to a fresh, tasty, instalment of Who, Me? It’s The Register’s reader-contributed column in which readers confess about things they did at work that probably deserve to remain a secret. This week, meet a reader we’ll Regomize as “Ray” who told us he once worked in a research lab repairing nucleonic instruments. We understand they’re gadgets that use very short half-life isotopes that emit just enough radiation it’s possible to measure the backscatter. According to the World Nuclear Association this is helpful to measure the level of coal in a hopper, or the thickness of paper! Like many workplaces, the lab Ray worked in had a microwave oven staff could use to warm their lunches, and a coffee machine too. The difference in this lab was that the appliances lived next to a sink used to wash the nucleonic kit. Ray’s manager decided that posed a risk to workers’ health – which it didn’t – so insisted the Microwave and coffee machine go elsewhere. Ray’s solution was to screenshot his PC’s desktop, print it onto A3 paper, and laminate it. “The screen looked very realistic without requiring a backlight,” he said. So Ray moved it into an unused office and put a keyboard and mouse in front of it. He also found the coffee machine a new home where the manager wouldn’t go looking. “They were both still in use when I retired three years later,” he told Who, Me? Have you found a way to defy the boss and got away with it? If so, click here to send us an email. We’d love the chance to expose readers to your story! ®

It’s not possible to operate a completely sovereign cloud outside of China or the USA, according to Douglas Toombs, a VP analyst at Gartner. Speaking at the analyst firm’s IT Infrastructure, Operations & Cloud Strategies Conference in Sydney today, Toombs said only the US and China make all the tech needed for a sovereign cloud. Buyers elsewhere can’t avoid relationships with foreign providers. Toombs said that while US-based cloud vendors have created products they say can meet the needs of organizations that need a cloud that doesn’t have legal entanglements outside their chosen jurisdiction, the fact they’re ultimately owned by American corporations means it’s not possible to be certain a cloud provider can promise complete sovereignty. Even on-prem clouds like AWS Outposts, Azure Local, or Oracle’s Dedicated Cloud Regions, “need to phone home,” he said. The analyst doesn’t think attempts to create sovereign clouds will succeed. He mentioned past French attempts to create sovereign clouds named “Andromeda”, “Numergy,” and “Gaia-X”, which he says went nowhere - but did produce some nice white papers. He also cited the The Rule of Three and Four, a maxim developed by Boston Consulting Group that asserts “A stable competitive market never has more than three significant competitors, the largest of which has no more than four times the market share of the smallest,” and argued that it predicts the cloud market has settled around AWS, Google, and Microsoft. Toombs allowed that some smaller clouds could thrive and will make it feasible to create sovereign SaaS providers and products. But he thinks that even aggressive moves to go on-prem won’t free organizations from dependency on US-owned clouds, an assertion he backed with the example of a Dutch healthcare provider that tried to build its own infrastructure but then experienced an outage when a supplier’s services went down along with a major cloud provider. If sovereign clouds fail to develop, it may be problematic because some European organizations are worried US-based cloud operators might leave the continent, forcing them into hasty and risky migration projects, according to Adrian Wong, a Gartner Director Analyst who also spoke in Sydney today. Wong said “heightened geopolitical tensions” are causing customers of major clouds to rethink their strategies, a decision he welcomes because he sees very few organizations bother to develop a cloud exit strategy. “Exit plans are overlooked,” he said, and users are “very much locked in” – especially when they use cloud-native services or platform-as-a-service. “Exiting within a timeframe of anything less than two years takes significant planning and investment,” he warned. “Exit strategies and plans are largely swept under the rug.” Wong says he is now seeing “the pendulum swing.” Not developing a cloud exit strategy is one of the ten big mistakes Wong sees users make. Also on his list are starting use of clouds with mission-critical and complex applications like ERP, assuming the cloud is appropriate for all applications, and expecting to get all the benefits of the cloud with every application. He also said it’s folly to assume that going multi-cloud will improve availability – unless users first tackle the more complex and expensive task of making applications portable. Wong said organizations that use multiple clouds should do so to access specific features of each, not to improve resilience. ®

The free/open source project OrcaSlicer is a popular fork of 3D printer slicing software from Bambu Lab. But Tuesday independent developer Pawel Jarczak shuttered the project "following legal threats from Bambu Lab," reports Tom's Hardware: Jarczak's fork of OrcaSlicer would have allowed users to bypass Bambu Connect, a middleware application that severely limits OrcaSlicer's access to remote printer functions in the name of security. Jarczak said in a note on GitHub that Bambu Lab threatened him with a cease and desist letter and accused him of reverse engineering its software in order to impersonate Bambu Studio. From Bambu Lab's blog post: Bambu Studio is an open-source project under the AGPL-3.0 license. Anyone can take its code, modify it, and distribute it... That's what OrcaSlicer does, and 734 other forks do as well. We have no issue with that and never have. At the same time, a license for code is not a pass to our cloud infrastructure... Our cloud is a private service. Access to it is governed by a user agreement, not the AGPL license... [T]he modification in question worked by injecting falsified identity metadata into network communication. In simple terms: it pretended to be the official Bambu Studio client when communicating with our servers... If this method were widely adopted or incorrectly configured, thousands of clients could simultaneously hit our servers while impersonating the official client. "User-Agent is not authentication," counters OrcaSlicer's developer. "It is only self-declared client metadata. Any program can set any User-Agent." And "the User-Agent construction comes directly from Bambu Lab's own public AGPL Bambu Studio code.... So on what basis can anyone claim that I am not allowed to use this specific part of AGPL-licensed code under the AGPL license...? My work was based on publicly available Bambu Studio source code together with my own integration layer." But the bottom line is that Bambu Lab "contacted me directly and demanded removal of the solution." I asked whether I could publish the private correspondence in full for transparency. That request was refused... They also referred to legal materials and stated that a cease and desist letter had been prepared... I removed the repository voluntarily. That removal should not be interpreted as an admission that all legal or technical allegations made against the project were correct. I removed it because I have no interest in maintaining a prolonged dispute around this particular implementation, and no interest in continuing to distribute it. YouTuber and right-to-repair advocate Louis Rossmann reviewed the correspondence from Bambu Lab — then pledged $10,000 for legal expenses if the developer returned his code online. ("I think that their legal claim is bullshit," Rossman said Saturday in a YouTube video for his 2.5 million subscribers. "I'm not a lawyer, but I'm willing to put my money where my mouth is.") The video now has over 129,000 views so far. "Rossman has not started a crowdfunding site yet," Tom's Hardware notes, "stating in the comments that he wants to prove to Jarczak that he has supporters willing to put their money where their mouth is. The video had over 129,000 views so far, with commenters vowing to back the case as requested."

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In Polymarket's prediction market, "most people end up losing money," reports the Washington Post — typically a few bucks. "Since Polymarket launched in 2022, a few thousand people have lost the bulk of the money... and an even smaller group — .05 percent of users — has gone home with most of the overall profits, according to a new analysis from finance researcher Pat Akey and colleagues." A lot of users aren't that good at predicting the future. They're losing money at roughly the same rate as online gamblers betting on sports and other real-life events at traditional sportsbooks, according to the U.K. gambling regulator's analysis of 2024 data. On Polymarket, the odds of making a profit are slightly higher on weather and tech markets — and a little lower on sports... On Polymarket, just 1,200 people took more than half the profits — $591 million, or more than $100,000 each. ["The top 1% of users capture 76.5% of all trading gains," the researchers write.] When you dabble in prediction markets, you're competing against these sophisticated players who consistently win. Most of those 1,200 big winners didn't place just a few smart bets. They appear to be pros making thousands of trades, mostly in the past year and a half, that were probably automated. One user made $3 million since January on more than a million trades about the Oscars, according to TRM Labs... The most profitable participants are also just good at picking what to bet on, Akey found, winning so often it was statistically unlikely to be dumb luck. They had some sort of edge — expertise, deep research or, perhaps, inside knowledge. "Our results suggest that the informational benefits of prediction markets come at a cost to unsophisticated participants," the researchers conclude.

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China’s Cyberspace Administration last week published draft regulations governing the behavior of AI agents and suggested humans should always retain the ability to review decisions taken by software. The draft expresses Beijing’s enthusiasm for AI agents with a call for efforts to develop datasets that accelerate development, along with security standards that make agents safe to use and ensure they behave ethically. There’s also a call to develop mandatory standards for how agents will behave “in fields such as healthcare, transportation, media, and public safety.” China also wants to participate in international fora that develop such standards. The draft calls for developers of AI agents to “clarify the reasonable boundaries and required authority for various decision-making methods, such as decisions limited to the user, decisions requiring user authorization, and autonomous decisions by the intelligent agent.” Those boundaries should “Ensure that users have the right to know and the final decision-making power regarding the autonomous decisions made by the intelligent agent, and that the intelligent agent's actions do not exceed the scope authorized by the user.” The draft identifies many tasks Beijing thinks agents might take on, including marking homework, analyzing medical images, evaluating employee performance and recommending promotions, helping disaster relief efforts, and even providing “intelligent management of the entire bidding and tendering process, ensuring standardization and efficiency throughout.” Samsung turns off its TV and appliance business in China Korean giant Samsung last week decided to quit China’s TV and appliance markets. “In response to the rapidly changing market environment, after careful consideration, Samsung Electronics has decided to cease sales of all home appliances, including televisions and monitors, in the Chinese mainland market,” states an “adjustment notice” on the Samsung China website. Samsung will honor warranties, and continue to provide after-sales service. The company hasn’t said why it’s quitting these markets in China. The Register expects the reasons have a lot to do with the rise and rise of Chinese consumer electronics companies, which can make a patriotic pitch in addition to pointing out the high quality of their products. Samsung’s not the first to decide it’s too tough to try trading televisions in China: Sony quit the country, too. Thailand approves giant TikTok datacenter The government of Thailand last week approved TikTok’s plan to spend ฿842 billion ($25 billion) on new datacenters in the country. Thailand’s Board of Investment said the project will see TikTok “install additional servers and expand data storage and processing infrastructure across Bangkok, Samut Prakan and Chachoengsao Province, supporting rising demand for digital services and strengthening Thailand’s role in regional digital infrastructure.” The Board also signed off on a 200 MW datacenter to be built by Skyline Data Center and Cloud Services Co, and a 134 MW facility from Bridge Data Centres. Baidu to float its chip biz Chinese web giant Baidu has filed paperwork to spin out its chip design business Kunlunxin. Baidu flagged its plan to do this in January, when it said the aim was to “independently showcase Kunlunxin's value, attract investors focused on the AI chip sector, and leverage its standalone listing to enhance its market profile, broaden financing channels, and better align management accountability with performance.” “This also supports the effort to unlock the value of Baidu's AI-powered businesses.” Kunlunxin’s chips suit inferencing and training workloads, but their performance can’t match Nvidia’s latest chips – or even four-year-old kit like the H100. That hasn’t stopped Baidu using the chips to power its own AI services, and major Chinese corporations also use the company’s chips. Japan and EU to improve tech interoperability The EU-Japan Digital Partnership Council recently convened its annual meeting and last week revealed that talks included “deepened discussions on the joint development and interoperability of data spaces” and promised to keep talking in a new “Data Strategy Working Group” that will “improve the interoperability of data policy frameworks.” The meeting also discussed a successful pilot on interoperable digital identities which apparently “showed that cross-border use is technically possible, even where governance frameworks and technical architectures differ. Using prototypes of digital identity wallets, the project demonstrated how interoperability can be achieved in practice between different systems.” As part of discussions, the EU and Japan agreed to begin working in new areas, including video games and audiovisual strategies. Humanoid robot becomes Buddhist monk Seoul’s Jogye Temple last week allowed a robot named Gabi to take the vows required of a Buddhist monk. Temple leaders reportedly decided to initiate the robot because they feel humanoid machines will soon become a part of everyday life. In February, the President of the Jogye Order, the Most Venerable Jinwoo, said “our lives have become ever more convenient thanks to cutting-edge science and AI. Yet the anxieties, anger, depression, and isolation—mental attachments and sufferings that science cannot resolve— are growing ever deeper.” “This does not mean that Buddhism withdraws from this vast technological civilization,” he said. “Rather, we aim to fearlessly lead the AI era and redirect its achievements toward the path of attaining peace of mind and enlightenment.” “In the age of AI and quantum science, peace of mind will be cultivated through Buddhism.” ®

Open-source PS3 emulator RPCS3 "has been around since 2011," Kotaku notes, and has made 70% of the PlayStation 3's library fully playable, "bolstered in part by the many users who contribute to its GitHub page." But their dev team "took to X today to very kindly and civilly request that users 'stop submitting AI slop code pull requests' to its GitHub page." Then they immediately proceeded to tell the AI-brain-rotted tech bros attempting to justify their vibe-coding nonsense to kick rocks in the replies, which is somewhat less civil but far more entertaining to read... My favorite one was when someone asked how the team was certain they weren't rejecting human-written code, to which RPCS3 replied: "You can't possibly handwrite the type of shit AI slop we have been seeing."

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KETTLE We've been experimenting with LLMs for a while here at The Register, and if you ask our systems editor Tobias Mann and senior reporter Tom Claburn, locally installed coding assistants have actually become so good they could relieve some of the compute load that's pushing AI companies to raise their prices. This week on The Kettle, host Brandon Vigliarolo is joined by Mann and Claburn to discuss their work with locally-hosted LLMs, why we're revisiting the topic at all, how to do local LLMs safely, and whether there's orbital relief coming for the compute crunch. You can listen to The Kettle here, as well as on Spotify and Apple Music, or read the full transcript of this episode below. ® --- Brandon (00:01) Welcome back to another episode of The Register's Kettle podcast. I'm Reg reporter Brandon Vigliarolo and with me this week are systems editor Tobias Mann and senior reporter Tom Claburn to talk about some experiments they've been doing with AI coding assistants, but not just any AI coding assistant mind you, we're talking about local ones that live right on your own machine. Guys, thanks for joining me this week. Tobias Mann (00:24) Good to be here. Thomas Claburn (00:25) Thank you. Brandon (00:29) So before we jump into what learned during these experiments and how effective local large language models actually are as coding assistants. Let's talk a bit about why we're having this discussion in the first place. And I understand that AI coding assistants are about to become way more expensive. And I think, Tom, these were stories that you wrote recently. So can you walk us through a bit what's going on with the current cloud-hosted ones? Thomas Claburn (00:52) Back in November, there was, I think around Opus 4.5, pretty much all the developers started to realize that these models were actually getting pretty good and there's no longer, vibe coding was less of a joke and more like, you know, maybe this will work. And then by the time, you know, around February with the OpenClaw craze, was a lot more demand for sort of coding agents and people would start running these for long periods of time. And it sort of caught Anthropic and others unaware, Google and open AI as well. There was a lot of capacity constraints, a lot more people were trying these things out and they ended up having to find ways to limit demand through session limits and made a lot of people unhappy but they basically just didn't have the compute available to serve capacity. And on top of that, they're serving a lot of these at a price that is loss-leading. They're trying to get people into the business, but these are unprofitable workloads for them. And if you look at something like Mythos, which came out, is their big security model, it was too good for anybody, but large companies with expensive payrolls to run. Brandon (02:08) Right, right. Thomas Claburn (02:10) It's clear that they're looking for ways to increase their revenue because they're investing a lot in the infrastructure to make this run, but they don't yet have the recurring revenue that justifies all this. The ramps look good. They're bringing more people on, but they invested a lot of money in this. Brandon (02:29) OpenAI famously has never actually turned a profit in its history. I don't know about Anthropic ⁓ personally, but I can't imagine they're doing a whole lot better. And so I understand the two specific examples you had was that Anthropic recently yanked Claude Code from Pro plans, but only for some people. Is that correct? Thomas Claburn (02:49) Yeah and they wrote that off as an A/B test. Basically they were doing live A/B testing and people noticed and they were saying, oh, well, no, that's doesn't apply to everyone. We're not going to change or take away from existing Pro users. But clearly there are someone there saying, hey, can we get away with charging this much but providing less service? And that doesn't happen unless you're trying to figure out a way to increase your revenue and reduce the demand on your services. Brandon (02:53) Okay. Totally. Did they backtrack on that at all or is that still, is that A/B test still going on? Thomas Claburn (03:23) I don't think it's still going. Tobias Mann (03:24) They do really do do a lot of A/B testing. I think I have a Claude Code Max subscription that is about, has a 50 % discount on it right now. So I'm a little hesitant to give it up because yeah, it's a hundred bucks a month and I don't use it nearly enough to justify that. But also if I cancel and decide I wanted it back, it'd be 200. Brandon (03:46) Yes, the reason I'm still an Nvidia GeForce Now gaming cloud subscriber, right? Because I was there in the beta test and I've never given that discount up, even if I haven't used it in a while. So I understand. Claude did that, Anthropic did that, and then GitHub also has just straight up jumped to metered billing for AI, think. Correct? Thomas Claburn (04:05) Yeah, and they were taking a huge loss on things because they would give you a flat rate, but then people would use the most expensive models. And of course, those things are billed at different rates and offering a flat rate versus these very inflated Opus 4.7 models, which also take a lot longer to process stuff, even if they're a little bit more efficient, they'll think for longer periods. It's just they're losing money. So everyone has to go to meter billing. And once that happens, it's going to cost people a lot of money. You can look at it now, even on a subscription plan, you'll write up a little widget and you look at the thing and it's, you know, $2 worth of whatever. You think, well, is that worth it? Maybe. And then if it's a more substantial project, you know, people spend, you know, hundreds of thousands of dollars on stuff. And if that's not returning you any revenue, are you still going to do that? So it's going to be interesting to see how this goes. Brandon (04:59) Maybe local LLMs like what we're here to talk about today are kind of the market control, right? I'm sure there are gonna be people who are using these paid services, or were at least, that are gonna say, I don't care what the justification is, whether they're trying to make more money, sure, they might deserve to, or whether they just need to reserve compute resources. Either way, I can't afford to pay for this, so I'm going local. Maybe that'll be the cost control, right? Maybe there'll be some balance that kind of equals out there between, we're losing customers, so we got to make this cheaper versus we need to actually get some return on our investment someday. But I guess either way, right, this discussion is kind of is indicative of why we're talking about using local LLMs. Specifically, I believe, coding assistants, which is what the two of you have been kind of spending some time working with. And I understand you've both had success in various ways with this. Let's talk a bit about I guess the one large story you wrote this week about local LLMs and just kind of more broadly what you guys think of them. Tobias Mann (06:05) Many of us on the team have been playing with local LLMs in some shape or fashion for a couple of years now. And probably within the last year, certainly in the last six months, the models that are small enough that you can run on consumer hardware – and I'm not talking cheap consumer hardware, I'm talking about high-end consumer GPUs, quasi-workstation mini PCs, higher-end MacBooks and Macs – the quality of those models have jumped from being kind of like toys, tech demonstrator,s to being really rather competent. At the same time, we've also seen the rise of these agentic coding frameworks. That's the other part of the equation. These are things like Claude Code . Claude Code is a framework thatconnects to models running in Anthropic's various data centers and cloud providers, and is what's actually orchestrating the generation of the code, the testing of the code, the validation of the code, and allowing developers to kind of use these as actually useful tools rather than just getting a code snippet that may or may not work out of a model as you might have done with ChatGPT four years ago. Right around the time that Microsoft was going to usage-based billing and Anthropic was toying around with kicking the $20 a month Pro users off of the Claude Code entirely to save on compute, Alibaba's Qwen team popped in with a relatively small 27 billion parameter LLM Brandon (08:05) Relatively small. I just think it's funny how quick the parameters have grown over the years. Tt's small. It's only a few billion, you know. Tobias Mann (08:08) Yeah,it's only 27 billion. You know, they popped in and they presented this as being frontier-quality coding out of a pretty small model. And so with all of the harnesses you need to do this and now a model that is supposedly competent, it was just kind of the perfect storm so to speak, to start looking into whether or not these small models could be a replacement for some part of the development flow, for the entire development flow. And it's surprising just how good these small models have gotten. Thomas Claburn (08:53) I was experimenting just recently with the Qwen 3.6 and it's like a, whatever, 35 billion parameters ... but it's like a mixture of experts, so it's actually only like 3 billion, I think, when it's running. And it's an 8-bit quantization. And it's actually, it's working pretty speedily. And I was doing a sort of comparison test to see whether it would do a drag-and-drop metadata removal app on a map, which is like a very particular kind of thing. And initially it kind of suggested some things that were wrong. And I sort of cross-checked that with Claude OpenAI and they both came up with things that were like not really right either and then when I sort of rephrased the question to it more carefully, they basically came up with the same answer with Claude. And what it tells me is to your point about the harnesses, I think a lot of the things that makes local coding work is how good the local harness is. And this was a point that came up yesterday in a piece I was working on about Mozilla when they were talking about all the bugs they fixed with Mythos. One of the people I was talking to, Davi Ottenheimer, argued pretty strongly that you can do Mythos-quality work with a much smaller model as long as you have a good harness. Unfortunately, a lot of the setup of that is very kind of...there's not a standard way to do it. So people will either figure out a way that makes it work or they'll set something up and it just doesn't work. But it's not really clear why that happens. And there's a lot of just sort of arcana about like what skills you have and what the pipeline looks like. People are still figuring it out. But I think that local is where it will go because there's nothing that beats the price of being able to run this for next to nothing excluding your very expensive hardware. Brandon (10:59) And it's improving to the point where it's not something that it would have like a while ago, was like, this doesn't really work. Now we're reaching the point where these local models are viable, right? Well, like you said, you've got to word things carefully. I mean, that feels like anything that was the early days of AI, right? It's like, OK, you got to word it carefully. But eventually, it's going to get better to the point where it's not going to have to be so particular. And you get the same results, hopefully. Tobias Mann (11:24) Yeah, there are two key technologies that I think have really helped these smaller models compete. The first is, as Tom mentioned, is mixture-of-expert models. They only use a subset of the total parameter count for each token generated, which reduces the barrier to entry for hardware. The larger the models get, the more memory bandwidth you need in a consumer or even workstation class of product. It gets absurdly expensive as your memory bandwidth requirements increase. Brandon (12:01) Even for doing some of the basic ones here, I think you wrote in your story that the things you need, you need an M5 Mac with 32 gigabytes of memory. Or 24 gigabytes with multiple GPUs. You need a beefy machine from a consumer perspective to run this stuff. I've got an M1 Mac I wonder if I could run some of these. My Mac's pretty fast. I haven't needed to think about upgrading it in several years. And I looked at them and there's no way. Tobias Mann (12:29) So older Macs can do it. You will run into issues where the prompt processing side of it, that's the, hit enter on your prompt and then you wait. It gets to be problematic. Like you're talking several minutes of waiting for it to start generating a response because older Macs lacked the matmul acceleration necessary for this. So they were brute-forcing a lot of the compute on the GPU. Starting with the M5 Max, they integrated the matmul acceleration into the GPU. It makes a huge, huge difference in terms of performance. That's why we recommended newer Macs. Tom and I, I think we're both testing on older M Series Macs. Yes, it can work and especially with the 35 billion parameter mixture-of-experts model, it's a little bit better, but the quality is generally worse than the dense 27 billion parameter model. Brandon (13:39) I guess I can understand that, right? mean, the more processing you can get done the faster, the better the response is. Tobias Mann (13:48) That's a really important part of this because the other piece, the thing that has changed that the models are that small models can be this competitive is something called test time scaling. We saw this first with DeepSeek and OpenAI o1, which is this, you you hit enter on your prompt and then you see the model thinking and the model can work through different paths and then choose which path it wants to present tto the user at the end. So you can, the idea behind test time scaling is that you can take a smaller model and have it think for longer in order to make up for the lack of parameters in that model. And so we have both of those things coming together in models like Qwen 3.6 27B or Qwen 3.6 35B. Brandon (14:30) Okay, cool. Now, I mean, for those who are interested in setting this up and go, okay, I've got some hardware that's beefy enough and I think I'm willing to give this a shot. This has also gotten a lot easier. I think in the past year, year and a half, two years, it's also gotten multiple factors of simplicity easier to actually set up one of these things and run them locally. Is that accurate to say? It seems like it's gotten a lot simpler to configure this. Thomas Claburn (15:10) People often use Ollama or Unsloth, I'm using OMLX, which uses the Mac MLX. And these are basically the model serving platforms. You can get your model from a variety of places. Hugging Face is a very common one. But a lot of the model platforms like Ollama will fetch the model for you and handle all the installation stuff. The trick is a lot of them have different formats. And if you're using Olamma CCP directly on your computer, which is the C-based model runner, it's going to have a different format than say something else. And they'll all talk to each other, but it tends to lock you into one particular way of doing it and you get used to it. There's not really a right way of doing it right now and that's part of the problem is everyone's kind of figuring out what's the right way to do this? Which one do I want to use, how do I configure it? Even just looking at the model and trying to decipher the quantization and the features it has, isn't always clear to everybody. That I think hopefully will become more standardized as you get sort of more common knowledge about, yeah, this one works really well for me. Throughout the forums every week there's someone saying, yeah, this model is great for XYZ and we'll try that out. I mean, that's really the experience you have to have is figure out what you're gonna use it for and try it and see what other people are doing. And you can probably arrive at something that's useful locally. Brandon (16:45) Useful locally, I guess, also implies the need to do some security legwork. Right. I know when we first started writing about local LLMs, things like OpenClaw right. I mean, the the going headline for any of those right was, this this local LLM has caused chaos for somebody again. Right. Is that I think, Tom, you wrote a couple of stories recently about running local LLMs safely. Has it gotten to the point where it's easier to do that safely or is that still going to be a big concern for anyone doing this? Thomas Claburn (17:17) It is easier to do. The setup can be pretty complicated for these anyway. I just spent an evening building a sandbox for the Py agent because Py is sort of a very permissive agent that comes out of the box in YOLO mode. It can sort of do anything. It has very limited command set, but it has very few limitations. And that's by design. It's sort of like in the same way Flask is a very open Python framework. It's not this sort of "batteries included" thing, know, compared to Django. Something like Claude will come with a bunch of sort of predefined ways to do things. Claude has its own sort of sandboxing system and you can add a lot of safety through things like hooks. You know, there people who will write hooks that will intercept dangerous commands like, you know, rm. So there's a lot of ways to do it. Docker has a sandboxing system. That's what I tried to build on is basically figure out a way to do a Docker sandbox that runs Py and it protects the local file system but leaves the internet space open and those are kind of the security decisions you have to make because if this thing is totally enclosed in a VM and there's no way out, it can't really do anything! I mean you can do anything that you stick in the VM, but if you wanted to work on a project on your own system, you have to break that boundary somehow to get the file across and give it access, and then if you need to update something you have to open it up to a code repo somewhere. So there are a lot of security decisions you have to make and for me biggest one was just like making sure it doesn't mess with my local files and that gives me little bit more confidence to run a model that I don't really know how well it will perform. Having my Claude for a long time I'm a little bit more confident that it behaved behaves well, but the risk is there for all of them. Tobias Mann (19:10) So we looked at, I think, three different agent harnesses in the piece. Claude Code, which you would think is for work with Anthropic's stuff, but it works just fine with local models. It's two additional commands and you're up and running. It's very heavy. The system prompt is enormous. And so if you have lesser hardware, you might struggle a little bit with it. We also looked at Cline, which is a VS code extension that is very easy to install, pretty fast to configure. And then we looked at PyCodingAgent, which Tom had suggested that we discuss as well. Out of the box, Cloud Code and Cline both default to user-in-the loop, deny-by-default kind of situations where it'll ask for permission before performing any commands or writing any code. It'll say, "I want to write this code. What do you think? Do you want to proceed?" But they can be made to go fully automatic and just say, you know, I'm not worried, YOLO, let's go. And so thatmodel is a different security model than what we saw with PyCodingAgent, which to Tom's point is just pure YOLO mode out of the box. And so the security models differ wildly depending on which agent harness you're using or which sandbox that you're trying to play in, so to speak. There are several kind of agent sandboxes that have emerged that default to blocking all outbound network activity, which really limits the capabilities of the agent and forces you to be deliberate about what you do and don't want it talking to. Others are just, you know, they're focused on isolating doing kind of limiting the blast radius if the agent decides to go AWOL and do rm, rf, you know, the root file structure and just take the whole thing out. That's fine if it's in the container and it destroys the container because you run two commands and you're back up and running again. It's less okay if you're running bare metal. Brandon (21:32) So security considerations, seems like the core is basically just know what you're working with, right? Like don't deploy an agent that you don't at least have some idea how the security apparatus built into it functions by default, right? And just what you can do with it. But I guess whether we think about security or not, a lot of the conversation around the need to run LLMs locally seems to boil down to compute resources and the cost to maintain them, the cost to operate them, the cost to serve them. And I guess, Anthropic, speaking of Claude, right? Anthropic's big longshot this week, I guess, was a plan or a partnership they signed with SpaceX to occupy some space on the fleet of orbital data centers that Elon Musk seems intent on building. Tom, so is that gonna happen? Thomas Claburn (22:27) [Laughs.] I don't know. I I would think that they would put them in the ocean before they would put them in space. And, you know, they talk about data centers, but I think that it's I'll wait and see if they actually build them on land first, because there's a lot of terrestrial construction that is planned and hasn't happened. And we'll see. Tobias Mann (22:49) Yeah, the whole idea is that in space, you put the satellites in a sun-synchronous orbit, then they have basically unlimited power. The problem is that you have to get them there in the first place, which you need a launch vehicle for, which, last I checked, Starship still does not work. Brandon (23:09) I was gonna say this seems awfully familiar to me if we just change orbital data centers to Mars colonization, right? Like same problem here. We gotta have a vehicle that can get us there yet and we do not. Thomas Claburn (23:21) The Hyperloop will be the way they'll take it out there. Brandon (23:24) Yeah, right. Tobias Mann (23:28) And once we get the orbital cluster in place, Elon wants to put a mass driver on the moon so that we can put even more of these things into deep space for reasons I guess. Brandon (23:42) It just seems like there's a lot of, I don't know, it feels like the idea that Anthropic is gonna get on board with these SpaceX data centers in orbit. It feels to me a lot like when a data center company is like, hey, we just signed a huge deal with this company that makes nuclear reactors that don't exist yet. And it's kind of like, cool guys, well, let us know when we've actually got a real solution for the compute crisis that you guys are dealing with right now that you caused. Thomas Claburn (24:05) I kind of interpret the whole space thing as like, we made a deal with SpaceX and we have to say something nice about their future plans. Brandon (24:18) Right. Yeah. Tobias Mann (24:19) This really boils down to Anthropic is getting access to Colossus One, this massive, what, 150-megawatt AI factory, purpose-built for GPU training and inference. And so I think really what they need is compute and they cannot get enough of it. The inflection point has hit and we're seeing adoption, which means we need compute for inference and we need more compute for inference than we've had in the past. And so I think really what this is, is we'll say whatever you want. We will say that we will ride along on your Starship into the heavens and live in your space data centers. Just give us access to Colossus, please, because we're dying for compute. Brandon (25:15) We need it now and it'd be great if it happened someday in orbit, right? So in the meantime, I guess, basically, have we reached the point where localized AI, local LLM coding agents, right? Are we at the point now where they might be able to ease some of the compute stress that these companies are feeling or is this still early days something that's going to have to be developed, not worth it for the average developer? Thomas Claburn (25:41) I think they're going to be useful for sort of prototyping stuff. One of the things I've done is, I'll run it through the local one and then I'll have Claude check it. You often get a lot of, you know, code fixes that way. So it is a way to offload some less important jobs. I mean, you don't need a frontier model for everything. Brandon (25:49) Right. Right. I think that was kind an argument you made to bias about, you know, using a massive data center to build an HTML page is not a good use of resources. Tobias Mann (26:09) Right, Using the biggest, baddest model to write some HTML is probably not the most efficient thing to do, and it's certainly well within the capabilities of these small models. The other thing I'll say is, if you look at how GPT-5 works, if you go to ChatGPT, not Codex, when you first enter a prompt, it gets routed to one of three models based on the complexity of that model. Conceivably, we could do the same thing with local models, where you sign into Codex, it does a check. If you have sufficient hardware, it will run some portion of that query through the local model, do a yes/no check on the big model in the cloud, and decide whether or not, at that point, whether or not it needs to be regenerated via the API, or it can move forward with what's generated locally. So there's definitely a path forward for local playing a bigger role in reducing the amount of compute required to scale it.... Brandon (27:25) I guess the only key caveat there would be that if you're gonna install local LLMs on people's machines to split your compute load, you should probably let them know first, right Google? Tobias Mann (27:38) You probably should. Brandon (27:43) Probably. Or you can just do it and ask for forgiveness later on. Who's gonna uninstall Chrome? You? Ha ha ha. Tobias Mann (27:49) Yeah, the other thing I would point out is that, while a 24- or 32-gigabyte GPU is very expensive, we're talking anywhere from $1,00 to, you know, $4,000 plus for GPUs with that memory, those GPUs could serve that model to an entire team, realistically. And so if you were thinking about this from an enterprise adoption standpoint, you could buy one machine that sits in the corner, basically silent, that could serve an entire dev team with this smaller model. Or you could spend a whole lot more, but still something that fits on a desktop in the corner that runs a big model, like a trillion-parameter model, locally on that system and for that team. We're not just limited to these small models. You and I might be, but from an enterprise standpoint, a $70,000 DGX Station, for example, is capable of running very large models, trillion-parameter scale models. And that's less than the cost of one developer for a year. Brandon (29:06) Yeah, so maybe that's the case now, right? Maybe we've just reached a point where there's enough value in these local models as a sort of prototyping testbed, as a entry level dev replacement to do the first work before someone more experienced or with more parameters reviews it. Yeah, so it might be there. That's interesting. I will be interested to see how the evolution of AI models and like you said, the kind of linking between cloud-based versus local. I'll be interested to see how that develops. It could be the next phase of the AI industry's evolution. We'll see. We'll see. Something's got to give with compute, right? No matter what it is, we are going to be sure we're here on The Register to write about it and here at the kettle to talk about it. And until then, we will see you next week on the next episode.

An anonymous reader shared this report from Electrek: A newly revealed Honda patent shows the company developing a simulated electronic clutch system for electric motorcycles, complete with torque-boost launches and even haptic feedback designed to mimic the feel of a combustion engine.... Instead of using a traditional mechanical clutch, the system uses electronics to alter how the motor responds based on clutch lever position. Pull the clutch halfway in, and the system proportionally reduces motor output. Pull it fully, and power is cut entirely, regardless of throttle position. But the more interesting part is how Honda intends to recreate the behavior riders actually use clutches for. According to the patent as reported by AMCN, riders could preload the throttle while holding in the clutch lever, then rapidly release the lever to trigger a burst of torque — essentially simulating the hard launches motocross riders rely on with gas bikes. Honda believes that could be useful in competitive riding situations where precise power modulation matters, especially on loose terrain or during aggressive starts. Honda also appears to be working on recreating the feel of a gas bike, not just the control inputs. The patent describes multiple vibration motors placed in the handlebars and near the clutch lever to provide haptic feedback that simulates engine vibration and even the "bite point" sensation of a clutch engaging. In other words, Honda may be trying to make an electric dirt bike feel mechanically alive, or at least the old-school idea of what a breathing dirt bike used to feel like.

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Major American cloud companies with data centers in the Persian Gulf "are channeling data out of the war zone through fiber-optic cables that an Iraqi telecom has strung alongside crude-oil pipelines," reports RestofWorld.org: The data centers serve customers in more than 190 countries, processing transactions, storing files, and running applications for businesses and individuals from Latin America to South Asia. When Iranian drones struck Amazon's facilities in the United Arab Emirates and Bahrain on March 1, the effects spread across the region. Apps of major banks in the UAE, including Abu Dhabi Commercial Bank, stopped working. Payment and delivery platforms went offline. Snowflake, a U.S. enterprise software company used by thousands of businesses globally, reported Middle East service disruptions tied directly to the Amazon Web Services outage. Amazon told its customers to migrate their workloads out of the Middle East... [Data from] banking, payment, and enterprise platforms normally travels to Europe through cables running under the Red Sea and the Strait of Hormuz, then connects onward to users across the world. The war has put those cables at risk. The overland route through Iraq is meant to serve as a backup if the sea cables are disabled. The overland route through Iraq is meant to serve as a backup if the sea cables are disabled... [Martin Frank, strategic adviser for IQ Networks, the company that built the network, told Rest of World this overland route is already carrying live traffic.] The company, based in Iraq's Kurdistan region, runs fiber from the southern tip of Iraq to the Turkish border. It is now extending the network through gas-pipeline corridors across Turkey to the European border, with the first link expected early next year, Frank said. When that extension is complete, cloud providers will — for the first time — have the option of an unbroken land-based fiber path from the Gulf into the European network, connecting onward to Frankfurt, Amsterdam, London, and Marseille, from where their data connects back to U.S. users. The advantage of this alternative route is that oil and gas pipelines come with their own security perimeters, access roads, and maintenance corridors already built around them, allowing a telecom company to lay fiber without digging new trenches through difficult terrain. Iraq avoided the fate of earlier overland routes that collapsed because of a sustained period of stability, and because existing pipeline infrastructure provided ready-made corridors for laying fiber, Doug Madory, director of internet analysis at network intelligence firm Kentik, told Rest of World... IQ Networks' route, called the Silk Route Transit, has been running since November 2023. The network currently carries enough data to stream about 400,000 high-definition videos simultaneously, Frank said. The land route is faster. Data traveling through submarine cables from the Gulf to Europe takes about 150 milliseconds. The Iraqi terrestrial route cuts that to roughly 70 milliseconds — a difference that matters for video calls, financial transactions, and applications that run on artificial intelligence, according to IQ Networks.

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This week Amazon opened up its parcel shipping, fulfillment, and distribution "to businesses of all types and sizes." Any business can now ship, store, and deliver "using the same supply chain that supports Amazon," according to Monday's announcement of "Amazon Supply Chain Services." The move sent shares of UPS and FedEx "tumbling" Monday writes GeekWire. And though both stocks bounced back as the week went on, GeekWire sees this as the latest example of Amazon "turning its internal capabilities into products and services for sale..." "Amazon had already surpassed both carriers to become the nation's largest parcel shipper by volume, according to parcel-analytics firm ShipMatrix." Initial customers include Procter & Gamble, which is using Amazon's freight network to transport raw materials; 3M, which is using it to move products to distribution centers; Lands' End, which is fulfilling orders across sales channels from Amazon's warehouses; and American Eagle Outfitters, which is using Amazon's parcel service for last-mile delivery. The service can fulfill orders placed through platforms that compete with Amazon's own marketplace, including Walmart, Shopify, TikTok, and others... Peter Larsen, vice president of Amazon Supply Chain Services, compared the launch to the origins of Amazon's cloud business... In addition to putting Amazon in competition with existing players in the logistics industry, the move also raises questions about data privacy. Amazon has faced accusations of using nonpublic seller data to compete against merchants on its marketplace, which it has denied. Larsen told the Wall Street Journal that the company prohibits using supply chain customer data for its own marketplace decisions, noting that hundreds of thousands of Amazon sellers already trust the company to fulfill orders placed on rival platforms. The article notes taht in his annual shareholder letter Amazon's CEO "said the company is also exploring selling its custom AI chips and robotics to outside customers."

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"General Motors sold the data of California drivers without their knowledge or consent," says California's attorney general, "and despite numerous statements reassuring drivers that it would not do so." In 2024, The New York Times "reported that automakers including GM were sharing information about their customers' driving behavior with insurance companies," remembers TechCrunch, "and that some customers were concerned that their insurance rates had gone up as a result." Now General Motors "has reached a privacy-related settlement with a group of law enforcement agencies led by California Attorney General Rob Bonta..." The settlement announcement from Bonta's office similarly alleges that GM sold "the names, contact information, geolocation data, and driving behavior data of hundreds of thousands of Californians" to Verisk Analytics and LexisNexis Risk Solutions, which are both data brokers. Bonta's office further alleges that this data was collected through GM's OnStar program, and that the company made roughly $20 million from data sales. However, Bonta's office also said the data did not lead to increased insurance prices in California, "likely because under California's insurance laws, insurers are prohibited from using driving data to set insurance rates." As part of the settlement, GM has agreed to pay $12.75 million in civil penalties and to stop selling driving data to any consumer reporting agencies for five years, Bonta's office said. GM has also agreed to delete any driver data that it still retains within 180 days (unless it obtains consent from customers), and to request that Lexis and Verisk delete that data. "This trove of information included precise and personal location data that could identify the everyday habits and movements of Californians," according to the attorney general's announcement. The settlement "requires General Motors to abandon these illegal practices, and underscores the importance of the data minimization in California's privacy law — companies can't just hold on to data and use it later for another purpose." "Modern cars are rolling data collection machines," said San Francisco District Attorney Brooke Jenkins. "Californians must have confidence that they know what data is being collected, how it is being used, and what their opt-out rights are... This case sends a strong message that law enforcement will take action when California privacy laws are not scrupulously followed."

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An anonymous reader shared this report from Futurism: In November, Amazon leaders sent an internal memo to employees, pushing them to use its in-house code generating tool, Kiro, over third-party alternatives from competitors. "While we continue to support existing tools in use today, we do not plan to support additional third party, AI development tools," the memo read, as quoted by Reuters at the time. "As part of our builder community, you all play a critical role shaping these products and we use your feedback to aggressively improve them." It was an unusual development, considering the tens of billions of dollars the e-commerce giant has invested in its competitors in the space, including Anthropic and OpenAI... Half a year later, Amazon is singing a dramatically different tune. As Business Insider reports, Amazon is officially throwing in the towel, succumbing to growing calls among employees for access to OpenAI's Codex and Anthropic's Claude... Given the unfortunate optics of opening the floodgates for Codex and Claude Code, an Amazon spokesperson told the publication in a statement that teams are still "primarily using" Kiro, claiming that 83 percent of engineers at the company are leaning on it.

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For just the first three months of 2026, Rocket Lab's launch business reports $63.7 million in revenue, reports CNBC — plus another $136.7 million from its space systems business. Besides beating Wall Street's expectations, Rocket Lab also announced that its backlog has more than doubled from a year ago to $2.2 billion, and that it's buying space robotics company Motiv Space Systems. Friday its stock price shot up 34% in one day... Rocket Lab's stock has more than quadrupled over the past year, benefiting from skyrocketing demand for businesses tied to the space economy ahead of SpaceX's hotly anticipated IPO later this year. Demand for space systems and satellites is also escalating as President Donald Trump pursues his ambitious Golden Dome missile defense project and NASA's crewed Artemis missions rev up. Rocket Lab said Thursday that it signed its largest contract ever with a confidential customer for its Neutron and Electron rockets through 2029, weeks after landing a $190 million deal for 20 hypersonic test flights... "The demand signal is clear," CEO Peter Beck said on an earnings call with analysts, calling the pace of new product releases from the company this year "relentless".... Rocket Lab's good news lifted other space companies. Firefly Aeropspace and Intuitive Machines both jumped more than 20, while Redwire gained 19%. Voyager Technologies rose 14%. "The company anticipates revenue between $225 million and $240 million during the second quarter."

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IT sector unemployment "increased to 3.8% in April from 3.6% in March," reports the Wall Street Journal. But they add that the increase reflects "an ongoing uncertainty in tech as AI continues to play havoc with hiring. That's according to analysis from consulting firm Janco Associates, which bases its findings on data from the U.S. Labor Department." On Friday, the department said the economy added 115,000 jobs, buoyed by gains in industries including retail, transportation and warehousing and healthcare. The unemployment rate was unchanged at 4.3%. But the information sector lost 13,000 jobs in April. While it's still too early to say exactly how AI is affecting employment overall, some businesses, especially in the tech industry, have said it's part of the reason they're cutting staff. In April, Meta Platforms said it would lay off 10% of its staff, or roughly 8,000 people, as it seeks to streamline operations and pay for its own massive investments in AI. Nike will reduce its workforce by roughly 1,400 workers, or about 2%, mostly in its tech department, as it simplifies global operations. And Snap is planning to eliminate 16% of its workforce, or about 1,000 positions, as it aims to boost efficiency. In other areas of IT, which includes telecommunications and data-processing, employment is now down 11%, or 342,000 jobs, from its most recent peak in November 2022. But there's not just AI to blame. Inflation and economic uncertainty linked to the Iran conflict is giving some chief executives and tech leaders reason to pull back or pause their IT hiring, said Janco Chief Executive Victor Janulaitis. The article even notes that postings for software developer jobs "are up 15% year-over-year on job-search platform Indeed, according to Hannah Calhoon, its vice president of AI". But employers do seem to be looking for experienced developers, which could pose a problem for recent college graduates.

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IT sector unemployment "increased to 3.8% in April from 3.6% in March," reports the Wall Street Journal. But they add that the increase reflects "an ongoing uncertainty in tech as AI continues to play havoc with hiring. That's according to analysis from consulting firm Janco Associates, which bases its findings on data from the U.S. Labor Department." On Friday, the department said the economy added 115,000 jobs, buoyed by gains in industries including retail, transportation and warehousing and healthcare. The unemployment rate was unchanged at 4.3%. But the information sector lost 13,000 jobs in April. While it's still too early to say exactly how AI is affecting employment overall, some businesses, especially in the tech industry, have said it's part of the reason they're cutting staff. In April, Meta Platforms said it would lay off 10% of its staff, or roughly 8,000 people, as it seeks to streamline operations and pay for its own massive investments in AI. Nike will reduce its workforce by roughly 1,400 workers, or about 2%, mostly in its tech department, as it simplifies global operations. And Snap is planning to eliminate 16% of its workforce, or about 1,000 positions, as it aims to boost efficiency. In other areas of IT, which includes telecommunications and data-processing, employment is now down 11%, or 342,000 jobs, from its most recent peak in November 2022. But there's not just AI to blame. Inflation and economic uncertainty linked to the Iran conflict is giving some chief executives and tech leaders reason to pull back or pause their IT hiring, said Janco Chief Executive Victor Janulaitis. The article even notes that postings for software developer jobs "are up 15% year-over-year on job-search platform Indeed, according to Hannah Calhoon, its vice president of AI". But employers do seem to be looking for experienced developers, which could pose a problem for recent college graduates.

Read more of this story at Slashdot.

In modern datacenters, storage can live anywhere — local to the machine, remotely accessed over the network, and/or shared between systems. The next generation of servers will treat system memory in much the same way. Systems will still have some local DDR5, but the bulk of it will be remotely accessed from what some have taken to calling the memory godbox. The ongoing DRAM shortage has created a perfect storm for the proliferation of the appliances, which not only allow for memory to be pooled, but also data stored in that memory to be shared by multiple machines simultaneously. In effect, memory becomes a fungible resource. More importantly, your next round of servers will probably support the tech, if they don't already. CXL finally has its moment to shine The technology at the heart of these memory godboxes isn’t new. Compute Express Link (CXL) has been slowly gaining traction since its introduction seven years ago. As a quick refresher, CXL defines a common, cache-coherent interface for connecting CPUs, memory, accelerators, and other peripherals. The technology comes in a couple of different flavors: CXL.mem, CXL.cache, and CXL.io, which, as a whole, have implications for disaggregated compute. Imagine a rack with a CPU node, GPU node, memory node, and storage node, which can talk to one another completely independently. That's the core idea behind CXL. CXL piggybacks off the PCIe standard, which means in theory it should be broadly compatible, but, up to this point, it's primarily been used with memory devices. The 1.0 spec opened the door to memory expansion modules, which allow you to add more memory by slotting them into a CXL-compatible PCIe slot. To the operating system — assuming you’re running Linux that is — the extra memory is largely transparent, showing up as if it were attached to another CPU socket, just one without any additional compute. The 2.0 spec, which showed up in 2020, added basic support for switching, which meant memory could be pooled and then allocated to any number of connected systems. AMD and Intel’s current crop of Epycs and Xeons already support these appliances. But while the memory can be partitioned and reallocated to different machines as needed, two machines can’t work on the same data simultaneously. Unless you were memory-constrained, the added complexity of CXL 2.0 didn’t offer much benefit over simply using higher capacity DIMMs in the first place. At least, not until memory prices went through the roof. Where things really get interesting is when the 3.0 spec arrives in AMD and Intel’s next-generation of Epycs and Xeons. In fact, from what we understand, Amazon’s Graviton5 CPUs we looked at in December already support the spec. CXL 3.0 introduces two key capabilities that make it particularly interesting for memory appliances. The first is support for larger topologies: Multiple CXL switches can be stitched together into a fabric. The second is support for memory sharing: Rather than partitioning memory into slices only accessible to one machine at a time, memory can be shared between machines. In theory this could allow two machines running the same set of workloads to use the memory closer to that of one. It’s a bit like deduplication for memory. In fact, we already do this in virtualized environments like KVM, but it now works across machines. There are security and performance implications to all of this. Thankfully in CXL 3.1 and later, the consortium introduced confidential computing capabilities into the spec, allowing for isolation where necessary. On the performance end of things, CXL 3.0 moves to PCIe 6.0 as a baseline, which provides 16 GB/s of bidirectional bandwidth per lane. Assuming 64 lanes of CXL per CPU, that works out to an additional 512 GB/s of bandwidth. So memory bandwidth shouldn’t be too much of an issue for most applications. Latency, on the other hand, is a different story. CXL-attached memory is going to add some latency. However, as we’ve previously discussed, the latency isn’t as bad as you’re probably thinking — on the order of a NUMA hop, or about 170 to 250 nanoseconds of round trip latency. Obviously, the farther the memory appliance is from the host CPU, the worse the latency is going to be. Late last year, the CXL consortium ratified the 4.0 spec, which among other things doubles the bandwidth from 16 GB/s per lane to 32 GB/s by re-basing on PCIe 7.0. However, it'll be a while before we see appliances based on the spec. Where’s my memory godbox? There are several companies developing hardware for these kinds of networked memory appliances. Panmnesia’s CXL 3.2-compatible PanSwitch is one of the most sophisticated examples. The switch features 256 lanes of connectivity for CXL memory modules, devices, or CPUs to connect, pool, or share resources. If you’re okay with memory pooling and don’t need the niceties of CXL 3.0, then there are already several memory appliances available that are compatible with the latest generation of Xeon 6 and Epyc Turin processors. Liqid’s composable memory platform, for example, can provide a pool of up to 100 TB of DDR5 to as many as 32 hosts. Meanwhile, UnifabriX Max systems provide CXL 1.1 or 2.0 connectivity to 16 or more systems with support for CXL 3.2 already in the works. We suspect that as more CXL 3.0 compatible CPUs and GPUs hit the market, more of these memory godboxes will appear. AI eats everything Don’t get too excited. While network attached memory has the potential to reduce an enterprise's infrastructure spend, those same qualities make it attractive for the very thing driving the memory shortage in the first place. AI adoption has driven demand for DRAM off the charts. In addition to the HBM used by GPUs, DDR5 is being used for key value cache offload during inference. These KV caches store model state and can chew significant amounts of memory — often more than the model itself — in multi-tenant serving scenarios. Rather than discard these caches and recompile them when the model state is restored, it’s more efficient to offload them to system memory and eventually flash storage. The problem with using flash storage is that it has a finite write endurance. After a while it wears out. Instead, CXL memory vendors are positioning the tech as a more resilient alternative. That’s bad news for enterprises looking to these memory godboxes for salvation from the RAMpocalypse. ®

Both Ubuntu and Fedora have made it official: support is coming soon for running local generative AI instances. An epic and still-growing thread in the Fedora forums states one of the goals for the next version: the Fedora AI Developer Desktop Objective. It is causing some discontent, and at least one Fedora contributor, SUSE’s Fernando Mancera, has resigned. Fedora Project Lead Jef Spaleta, who took over the role from Matthew Miller a year ago, remains resolute, saying: I have zero evidence in front of me that users are being driven away from Fedora because of AI. As far as Red Hat’s community distribution goes, while this may be controversial, this should not be a big shock. In October last year, The Register reported that the Fedora council approved a policy allowing AI-assisted contributions, and anyone following the IBM subsidiary’s movements will already know that last June’s RHEL 10 release includes access to an LLM-based online helper chatbot: we tried it out when the product was released. We also reported on the managers of Red Hat’s Global Engineering department being notably keen on the use of AI just last month. Since Red Hat has other offerings for slow-moving stable server OSes – and arguably because Debian, Ubuntu, and their many derivatives have the stable-desktop-distro space nicely covered already – Fedora has a strong focus on providing a distro for developers, and Spaleta’s announcement makes this clear. The goal is: to build a thriving community around AI technologies by focusing on three key areas: equipping developers with the necessary platforms, libraries, and frameworks; ensuring users experience painless deployment and usage of AI applications; and establishing a space to showcase the work being done on Fedora, connecting developers with a wider audience. He also spells out what it doesn’t want to do: Non-goals: The system image will not be pre-configured with applications that inspect or monitor how users interact with the system or otherwise place user privacy at risk. Tools and applications included in the AI Desktop will not be pre-configured to connect to remote AI services. AI tools will not be added to Fedora’s existing system images, Editions, etc, by the AI Desktop initiative. In other words, tools for developers, not for end-users, with a strong emphasis on models that run locally, and which preserve the user's privacy. It’s also worth pointing out that Fedora has had an AI-Assisted Contributions Policy in place for six months, and earlier this month, Fedora community architect Justin Wheeler explained in some detail Why the Fedora AI-Assisted Contributions Policy Matters for Open Source. Our impression is that the Fedora team feels that it needs to keep Fedora relevant for growing interest in LLM-bot assisted tooling, and that it can address concerns from hardcore FOSS types by ensuring that this means local models, built according to FOSS-respecting terms, deployed in privacy-respecting ways. Fedora is not alone in this, though. There are also ructions across the border in Ubuntuland. Right after the release of the Canonical’s new LTS version, Ubuntu 26.04 Resolute Raccoon, Canonical’s veep of engineering Jon Seager laid out the future of AI in Ubuntu. We interviewed Seager last year during the 25.10 Ubuntu Summit, and back in January this year, he published his views on Developing with AI on Ubuntu. Now the plans are firming up. Like Fedora, there’s a strong focus on local models and confidential, privacy-first deployments – and ensuring that the OS and the tools support GPU acceleration from the big hardware players in that space. However, unlike Red Hat, Canonical isn’t pushing its developers towards these tools. In what we see as a veiled jab, Seager’s announcement says: We are not setting shallow metrics on token usage, or percentages of code written with AI, but rather incentivising engineers to experiment and understand where AI tools add value. Initially, the focus is on users instead: AI features in Ubuntu features will come in two forms: first as a means of enhancing existing OS functionality with AI models in the background, and latterly in the form of “AI native” features and workflows for those who want them. As Fernando Marcela’s exit shows, an emphasis on what could be termed FOSS-friendly AI – open models, privacy-centric, local execution and so on – is not enough to placate those who are really strongly averse to these tools. The Reg FOSS desk counts himself firmly in this camp. Back in January, we reported on the rise, fall, and resurrection of OpenSlopware, a list of FOSS projects which contain LLM-generated code, integrate LLMs, or even show the traces of the use of LLM agents. Soon, it seems inevitable that Fedora and Ubuntu will both feature here. Resistance, though, is also rising. Stop Slopware tries to help explain why and how to avoid it, and there’s also The No-AI Software Directory for projects that have explicit LLM-free policies, whether they’re FOSS or not. Bootnote It amuses us to note that both the Ubuntu and Fedora forums use the same software, called Discourse. (It’s a sort of web forum as designed by people who have heard of mailing lists, but don’t know how to use them and find the idea of bottom-posting confusing.) Some could interpret this shared adoption as a sign of underlying similarities between the two projects. ®

CNBC reports: The European Union is considering rules that would restrict its member governments' use of U.S. cloud providers to handle sensitive data, sources familiar with the talks told CNBC. The European Commission — the EU's executive branch — is expected to present its "Tech Sovereignty Package" on May 27, which will include a range of measures aimed at bolstering the bloc's strategic autonomy in key digital areas. As part of preparations for that package, discussions are taking place within the Commission around limiting the exposure of sensitive public-sector data to cloud platforms provided by companies outside of the EU, two Commission officials, who asked to remain anonymous as they weren't authorized to discuss private talks, told CNBC... "The core idea is defining sectors that have to be hosted on European cloud capacity," one of the officials said. They added that companies providing cloud solutions from third countries, including the U.S., could be impacted. Proposals would not prohibit overseas companies' cloud platforms from government contracts entirely, but limit their use in processing sensitive data at public sector organizations, depending on the level of sensitivity, they added. The officials said that talks are ongoing and yet to be finalized... The officials told CNBC there are discussions around proposing that financial, judicial and health data processed by governments and public-sector organizations require high levels of sovereign cloud infrastructure.

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The early history of personal computers is stacked with systems such as the Apple II and the Commodore 64 that had the components living inside a keyboard. But as technology evolved, the keyboard became a peripheral and the PC itself was either in a separate box or the whole system was a laptop. Now, HP has a new spin on this decades-old idea. It embeds a full-fledged AI PC inside a 101-key keyboard you can carry with you from the office to home. Unlike ‘80s microcomputers or hobbyist-oriented products like the Raspberry Pi 500, the EliteBoard G1a is squarely targeted at business. The system is part of HP’s commercial lineup, alongside its EliteBook laptops, and, for better or worse, it comes with HP Wolf Security preinstalled. The company clearly hopes organizations will buy these in bulk. But to benefit from it, you really have to prefer a mobile keyboard to a traditional laptop, all money aside. Who’s it for? The EliteBoard G1a is trying to create a new niche. When we talked with product managers at HP, they suggested IT departments would buy these computers for two types of workers. The first group is so-called "dual deskers" - knowledge workers who have a desk with a monitor at work and another at home. The second group includes deep-pocketed call centers or environments where desk space is at a premium. From time immemorial, dual-deskers have carried laptops and closed their lids when they docked to a monitor at work. With the EliteBoard, they could simply schlep the keyboard, which weighs a mere 1.49 pounds – about half the weight of a lightweight laptop. To make this situation work in companies with managed systems, we have to assume that either the IT department would give out monitors to use at home or offer some reason (a subsidy? a mandate?) for employees to buy their own for home. The EliteBoard connects to monitors using its USB4 port, so its ideal monitor is one that has Thunderbolt or USB video connectivity built in. Less-expensive and older monitors don’t have this type of connectivity, but select configs of the EliteBoard come with an optional USB-to-HDMI adapter that you can use with other monitors, and it has a USB pass-through for power. That said, HP demonstrated the EliteBoard at numerous press events by showing how much desk space it saves by using a single USB cable to get power, video out, and connectivity to peripherals via the monitor. So if companies want employees to be able to take advantage of this scenario at home, that means shelling out another few hundred bucks for a modern monitor, or making employees do it. Today, companies with limited desk space for a call center or another cramped work area could just buy a tiny desktop to sit behind the monitor or next to it. However, building all of the PC’s guts into the keyboard makes a lot of sense for space savers, because a keyboard is something every PC needs and a desktop chassis is not. If a company wanted to, it could give each employee their own EliteBoard, have them plug it into a monitor during work time and then have them stick it in a drawer when they go off shift and someone else comes on. The problem for call centers is that the HP EliteBoard G1a is much more powerful and much more expensive than what they need. At press time, the G1a was priced at $1,499 for the lowest end config. And most companies probably don’t need employees to each have their own PC that they lock away after they punch out. “The call center angle is probably the stronger pitch, but those buyers are shopping entry-to-mid-market. They want something cheaper and simpler than a mini desktop, not a Copilot+ PC with up to 64GB of RAM,” Kieren Jessop, a research manager with analyst firm Omdia. “HP has built an impressive piece of engineering in search of a problem that most enterprises have already solved with a laptop — or will solve with a thin client.” Configurations HP makes the EliteBoard G1a in a variety of configurations that vary by market. Companies can get it with various AMD Ryzen CPUs, up to 64GB of RAM and an SSD up to 2TB in capacity. It comes with either a detachable or embedded cord, and optionally with a 32 WHr battery that promises up to 3.5 hours of endurance. Why would you need a battery on a product that demands to be used at a desk and plugged in? The most likely reason is to let the keyboard go into sleep mode when it’s in your bag. Employees could also hook the EliteBoard G1a up to a portable monitor and use it unplugged that way, but then why not just buy them a laptop? At press time, prices ranged from $1,499 to $3,423 in the US. The lowest-end config has a Ryzen AI 5 Pro 340, 16GB of RAM, an integrated cable, and a 256GB SSD. Fifty bucks more will get you the same configuration with a 512GB SSD, as per HP.com. The highest-end config listed comes with a Ryzen AI 7 Pro 350 CPU, 32GB of RAM, and a 512GB SSD, and sells for only $1,999 at B&H but a whopping $3,423 at HP.com. Our review config, which sports 64GB of RAM, a Ryzen AI 7 Pro 350 CPU, and a 2TB SSD, has not been listed for sale in the US, and HP didn't answer when we asked how much it would cost. However, we’d assume that it would cost a lot more than $1,999. Price vs a Laptop If all you do is dock your PC at home and at work, you might think, “why pay for a laptop when I don’t need a built-in screen?” But it’s hard to make that argument when the laptop is actually less expensive. Right now, you can get an HP EliteBook 6 G1aN with the same AMD Ryzen AI 7 350 CPU, along with 24GB of RAM and a 512GB SSD, for just $1,299 – that's actually less than the cheapest EliteBoard. A custom configured HP EliteBook 8 G1a with the Ryzen AI 7 Pro 350 CPU, 32GB of RAM, and a 512GB SSD is just $1,799. If you’re comparing the total cost of ownership versus a laptop, also consider the price of a monitor if your users don’t already have one. While you could use an adapter, the ideal use case involves a USB-C monitor that transmits data and power over a single wire. The cheapest HP-branded USB-C monitor I could find at press time was the HP E27k 4K monitor, which was selling for $504. However, I saw a Dell-branded USB-C monitor, the S2725DC, on sale for just $236 at Amazon. If you’re an IT department and you’re kitting out someone for home and office use, you might need to buy them two monitors. Design At 14.1 x 4.7 x 0.7 inches, the EliteBoard G1a is the size of a typical, full-size keyboard complete with numpad. It’s a boring but office-friendly dark gray color with a very thin bezel around the keys. At first glance, there aren’t many ways to know that this is more than just a keyboard. There’s a power button / fingerprint reader that’s located in the upper right corner of the keyboard, though you might easily mistake it for just another key, until you press it and see the blue light turn on. Turn the keyboard around and on the back lip and you’ll notice a thin vent for airflow. This computer definitely has a fan and you can hear it quite prominently at times. There are also two USB-C ports, a USB4 40 Gbps port and a 10 Gbps port, unless you have the embedded cable, in which case, you just have the 10 Gbps port. Clearly, the 40 Gbps port is the one you’ll want to use for docking, but you can use the 10 Gbps port to connect the dongle for the included wireless mouse or other peripherals. There’s also a security cable lock slot on the left side. So if you want to chain this to a desk, you can, but we’d argue that defeats the point of the machine. But how well does it type? Since this is a computer-in-a-keyboard, the most obvious question we need to answer is “how’s the typing experience?” Pretty decent. On the bright side, the EliteBoard G1a has a generous 2 mm of travel, which is more than you’ll find on most laptops, where even 1.5 mm is deep. The keys feel pretty snappy and are in the same feedback league as those on my Lenovo ThinkPad X1 Carbon, but the ThinkPad’s keys have a more curved shape, which is better than the flat tops on the EliteBoard. If you’re burning the midnight oil, there’s a built-in backlight which you can enable by hitting the F9 key. It has two different brightness settings so you can decide just how much you want it to shine through. The layout is pretty standard for a full-size keyboard with a numpad. However, I don’t like how small the arrow keys are, and the Pg Up and Pg Dn are just tiny. There’s no empty space around these keys, which I use a lot when editing documents, so it’s far too easy to miss them. Even on most laptops, these keys are larger. Another downer is the lack of flip-up feet on its bottom. I like to angle my keyboard up at a 15 to 30 degree angle, but this one is short and flat to the desk. To save my wrists, I always use a gel-filled wrist rest when I type and, without feet to elevate the keyboard, I’m typing down onto the keys because it’s so much lower than the gel pad. This won’t be as much of an issue for folks who don’t use wrist rests. In short, if you’re used to laptop keyboards or the low-cost keyboards that come with most desktop computers, the EliteBoard G1a will probably seem like a nice step up. However, if you want the best possible typing experience, there’s an entire ecosystem of mechanical keyboards out there with much deeper travel and more feedback. If you’re not a gamer and you want the best possible typing experience, I recommend a mechanical keyboard with either clicky or tactile switches. Unless you go for a low-profile keyboard, you’ll be getting between 3.6 and 4 mm of travel, so you won’t bottom out as easily when typing. I prefer clicky switches like the Kailh Box White (my favorite) or Cherry MX Blue, but those make some noise so, if you like quiet, Cherry MX Brown switches will do the trick. To see the difference between my daily driver mechanical keyboard, an Akko 3098N with Kailh Box White switches, and the EliteBoard G1a, I performed the 10fastfingers.com typing test on both. On HP’s keyboard, I managed a strong 96 wpm, which is at the lower end of typical for me, with a six percent error rate. On my daily driver, the numbers were a better 101 wpm with a two percent error rate. Your mileage will vary. Speaker and Microphone The EliteBoard G1a has both built-in bottom-facing speakers and a microphone array. In our tests, the speaker was more than loud enough and it was clear enough for voice calls, though we wouldn’t recommend listening to music on it for too long. The drums in AC/DC’s Back in Black sounded a little tinny, though there was a clear separation of sound with the vocals appearing to come from one side while the percussion came from another. The dual-array microphone was also passable, but not good enough for podcasts. When we talked to a coworker using the built-in mic, she said our voice was clearly audible but a little echoey. In the box and preloaded Depending on which config you get, your HP EliteBoard G1a may come with a variety of different accessories in the box. All versions come standard with an HP wireless 675M mouse that connects either by Bluetooth or by an included USB-C wireless 5-GHz dongle. It is not a particularly fancy mouse but it has a couple of side buttons and a scroll wheel. I found myself using my Logitech MX Master 3 mouse instead, because it’s ergonomically shaped and highly programmable. My review unit also came with the optional soft canvas cover sleeve you can use to protect the EliteBoard G1a while you’re carrying it around. I found this add-on to be about as useful as a laptop sleeve. It might offer some protection and padding for when you stick the EliteBoard G1a in an existing backpack, but it’s not going to replace your briefcase or your backpack when you’re commuting. I also got the optional HDMI multiport hub, which is a must-have if you don’t already have a Thunderbolt or USB4 docking station or a monitor with that kind of connectivity built in. The hub connects to the USB4 40 Gbps port on the EliteBoard and features two USB-C ports (one for power, one for connectivity), an HDMI out cable for connecting to a monitor, an Ethernet port for wired networking, and an HDMI-in port for a second monitor. There’s an optional, slim 65W USB-C power adapter that’s helpful if you aren’t connecting to a monitor or docking station that supplies power. If you don’t get one in the box, it’s easy enough to find one for $15 to $30 on Amazon. Also, if your EliteBoard does not have an embedded cable — mine did not — you get a braided USB cable in the box. The less-expensive configs of the EliteBoard all have embedded cables, but we recommend getting a model without one because it’s easier to carry around without a cable hanging off of it. HP does not preload a lot of software onto the EliteBoard but it does come with a three-year subscription to HP Wolf Security, which normally costs $36 a year for individual subscriptions. HP Wolf has a malware/virus scanner, a threat containment feature, a secure browser, OS resiliency (for recovering from corruption and doing a reinstall), and application persistence, which prevents unwanted changes to security software like HP Wolf itself. Since it has an NPU (neural processing unit) that’s capable of more than 45 trillion operations per second (TOPS), the EliteBoard G1a qualifies as one of Microsoft’s Copilot+ PCs. This means that it has some added local AI features that not every PC gets from Windows 11, including Cocreate image generation in Paint, Windows Studio Effects handled locally for your webcam, translated Live Captions from any audio input, and Recall, a controversial feature that takes screenshots of all your work to help you “remember” what you were doing at any given time. Fortunately, Recall is disabled by default. Performance Equipped with an AMD Ryzen AI 7 Pro 350 CPU, 64GB of RAM, and a 2TB SSD, our review configuration of the EliteBoard G1a handled everything I threw at it. I used the system on and off as my daily driver PC for work for a period of several weeks and it was always smooth and responsive, even as I had dozens of Chrome tabs open and Slack running across two 4K monitors I had connected via Thunderbolt 3 docking station. I should note that, no matter what I was doing, the fan on the EliteBoard G1a was frequently running and was often quite audible. It’s no louder than most notebooks I’ve tested, but if you’re expecting total quiet, look elsewhere. My editorial workload is not nearly as demanding as some folks’ day jobs so, to see how the EliteBoard G1a stacks up, I ran it through a series of benchmarks and compared the results to those from two laptops I had access to: a Lenovo Yoga Slim 7x with a Qualcomm Snapdragon X Elite X1E-78-100 CPU, and a Lenovo ThinkPad X1 Carbon with an Intel “Meteor Lake” Core Ultra 7 165U processor. The Ryzen AI 7 PRO in the EliteBoard debuted in 2025 with 8 cores, 16 threads, and a maximum boost clock of 5 GHz. It features built-in AMD Radeon 860M graphics and a Neural Processing Unit (NPU) that’s capable of achieving 50 TOPS for better local AI. Its DDR5 RAM runs at 5,600 MHz. Released in 2024, the Snapdragon X Elite X1E-78-100 has 12 cores and threads with a boost clock that goes up to 3.4 GHz, along with an NPU that does 45 TOPS. It’s an Arm processor so the laptop that runs it uses Windows on Arm. The Yoga Slim 7x laptop that we tested had 16 GB of LPDDR5x RAM running at 8448 MHz. The oldest of our test group, vintage 2023, the Intel Core Ultra 7 165U has 12 cores and 14 threads, but only two of those cores are performance cores that can boost up to 4.9 GHz, while the others are a mix of efficient cores and low-power efficient cores that boost up to 3.8 and 2.1 GHz respectively. The ThinkPad X1 Carbon we tested with it had 64GB of LPDDR5x RAM running at 6400 MHz. In our tests, the EliteBoard G1a always eclipsed the ThinkPad X1 Carbon, which is not a surprise considering its much-older processor. However, the Snapdragon-enabled Yoga Slim 7x outpaced it on some benchmarks. Primesieve This test counts the prime numbers under one trillion and returns a result in millions of prime numbers per second. The benchmark is particularly heavy on SIMD instructions like AVX-512 or Arm’s Neon and SVE vector extensions, making it a good proxy for some of the more workstation-centric tests we’ll look at shortly. It runs across both single thread and multi-thread workloads, with big performance boosts for parallel processing. Using just a single thread, the EliteBoard edged out the competition with 415 million primes per second (MPS), compared to the Slim 7x’s 352. However, the Slim 7x slightly outperformed it when using multithreading, delivering 2,686 MPS to the EliteBoard’s 2,145. One thing to note is that, while the EliteBoard has more threads, it has fewer actual cores. The X1 Carbon wasn’t even in the same ballpark. This will become a theme across our test suite. Blender 3D rendering is always a challenge and, to be honest, it’s hard to imagine somebody buying an EliteBoard for this purpose. However, it’s always worth noting what the system can do. We ran Blender, a very popular 3D modeling app, using three scenes: Monster, Junkshop, and Classroom. As you can see, the Slim 7x and its 12-core Snapdragon processor were anywhere from 34 to 75 percent quicker, depending on the content. Still, the EliteBoard turned in respectable scores on something you wouldn’t expect it to do. Handbrake x265 Video transcoding is another resource-intensive task and one that occurs in many scenarios, including game streaming, video editing, and even video conferencing. To test how the EliteBoard handled video transcoding, we used Handbrake to convert a 4K 60 fps video to 1080p using an x265 encoder at the medium preset with a constant quality of 18. Our results are measured in frames per second (fps). Again, the EliteBoard was far superior to the ThinkPad, but was a good 45 percent behind the Yoga Slim 7x. Still, this is solid performance that’s more than workable. Llama.cpp One local AI task you might want to conduct is running an open-source model as a chatbot on your PC rather than sourcing it from the cloud. This will give you more privacy than using OpenAI, Claude, or Copilot on the web and it’s completely free. So we ran the GPT-OSS 20B open weights model using Llama.cpp as our client and timed the amount of milliseconds it took to generate the first token. Here we see that the Snapdragon processor and faster RAM on the Yoga Slim 7x gave it a definite advantage, taking 39 percent less time than the EliteBoard to get there. The EliteBoard also generated about half as many tokens per second. However, it beat the pants off the ThinkPad X1 Carbon, getting to the first token more than twice as quickly while generating 30 percent more tokens per second. It’s worth noting that these tests were run on the CPU cores and didn’t harness the chip’s integrated GPUs or NPUs. Whisper.cpp One common local AI workload a business person might use is transcription. Let’s say you had an audio file and you wanted to convert it into readable and editable text. You might use a tool based on Whisper, a popular free model from OpenAI. For testing, we used Whisper.cpp, an implementation of Whisper written in C++, with the Whisper Medium EN model transcribing a 10-minute audio clip. Here, the EliteBoard transcribed the audio at 2.4x real-time speed, while the Yoga Slim 7x was faster at 3.4x. Those extra cores are doing a lot of heavy lifting here. That said, if you’re converting 10 minutes of audio in less than five minutes, that’s pretty good. LLVM Compile For those using the EliteBoard for programming, compile times matter. So, we compiled the LLVM toolchain from its source and measured the time. This isn’t a trivial compile job and therefore represents a worst case scenario for developers considering the EliteBoard. Here it took a modest 19 minutes and 44 seconds, which was more than double the time it took the Yoga Slim 7x. On high-end desktop workstation hardware, this same workload can be completed in under five minutes, so if your day job regularly requires compiling large projects, you might want to spring for something more capable, or perhaps not. “My code is compiling” is a pretty good excuse for taking a 20 minute break. 7-Zip Compression and decompression are very taxing on a CPU and are very common scenarios we see today. So we fire up 7zip and measure its ability to do both tasks in both single-threaded and multi-threaded scenarios. With a single thread, the Slim 7x and the EliteBoard basically tie at compression, while HP’s computer holds the edge in decompression. However, when we move to multi-threaded scenarios, the Snapdragon X Elite’s 12 physical cores easily beat out the AMD Ryzen AI 7 Pro 350’s eight cores and 16 threads. LibreOffice: ODT to PDF Conversion We tested how long it takes LibreOffice to convert 50 image-heavy ODT files into PDFs. This workload is lightly threaded so it favors higher clock speeds over more cores. The results bear this out as the EliteBoard, with its Ryzen AI 7’s higher performing cores, beat out the Slim 7x by 22 percent. Despite its older processor, the ThinkPad actually manages to tie the Slim 7x in this test. Repairability For IT departments that do their own service, the EliteBoard G1a has plenty to offer. Its back surface is held on by just four screws and pops off easily. Underneath, you get full access to the motherboard and a number of easily-removable components, including the DDR5 SODIMM RAM, the M.2 SSD, the WLAN card, the fan, the optional battery, and the speakers. You can even replace the keyboard itself and leave the computer part intact. Bottom line The HP EliteBoard G1a delivers strong performance in a unique and compact form factor that saves desk space and reduces the weight you carry back and forth. If you don’t want a laptop but do want a portable computer, this is your best choice. It provides a better typing experience than most laptops and a more space-efficient design than most desktops. However, in the current marketplace, this device does not represent a significant savings over a similarly configured laptop. Depending on what laptop you choose to compare against, you might save a few hundred dollars, but when you add the cost of the monitors you need to pair with it - if you need to purchase those - it’s a wash. HP has set out to make a unique product with the EliteBoard G1a and it has succeeded in building a very competent and capable computer-in-a-keyboard. If you’re an IT decision maker, you’d buy this device for folks who work out of one or two distinct locations (home and office or multiple offices) and never need to get online from the road or from a conference room. Whether that’s a common scenario in your workplace will determine if this product is right for you or your fleet. ®