A new 'Reaper' jet-powered drone project just unofficially broke the previous drone speed world record of 465 mph by hitting 491 mph. Tomas Salvo is an aerospace engineering student at Georgia Tech, and his project was assembled in his dorm room. The drone is comprised of a custom-molded carbon fiber shell, laser-cut internal structure, and a 250N jet engine with a titanium 3D-printed nozzle, costing $13,000 to build. Tomas Salvo aims to win the $800,000 Boom Prize posted by Boom Supersonic CEO Blake Scholl.

Tomas Salvo’s Reaper project, which aimed to beat Niels Herbrich’s world record for the fastest jet-powered RC aircraft, went viral and caught the attention of Boom Supersonic CEO Blake Scholl, who sponsors the $800,000 Boom Prize ($750,000 cash, $50,000 Boom stock). The prize’s conditions are: First, pass Mach 1 for at least 5 seconds while climbing or level, not descending. Second, it must be an air-breathing aircraft with no on-board oxidizers or rockets for propulsion. Third, it must land intact, and lastly, the same aircraft (wings, fuselage, engine, and flight computer) must fly twice in one day. The participants must be individuals or teams, not corporations or people backed by venture capitalists or government funding; they want garage hobbyists.

We recently covered Mach Initiative’s Kingchaser, a 3D-printed jet-powered RC aircraft also aimed at beating Herbrich’s world record. They were beaten by Tomas Salvo, but Tomas’ run was not officially recorded by Guinness. The Kingchaser project was inspired by the Boom Prize; however, since they are not US citizens, they are not eligible to win it, but are still aiming to have an officially recorded Guinness World Record. The Reaper has a smaller engine than the Kingchaser at only 250N, compared to the Kingchaser’s 300N, but achieved a flight speed of 491 mph, beating the Kingchaser’s test flight of only 273 mph, as the latter did not have enough space to accelerate. However, Mach Initiative estimates a top speed of 550 mph if they had the space.

While the Reaper isn’t made out of PETG like the Kingchaser, it still utilizes some 3D printing, albeit in a much more expensive fashion — it has a titanium 3D-printed nozzle for its engine that replaces the stock nozzle, allowing the engine to sit further forward, improving the center of gravity without adding ballast on the front, weighing in at a total of 11 pounds. It’s made out of custom-molded carbon fiber on the outside with a laser-cut internal structure, all designed by Tomas Salvo and assembled in his dorm.

The Reaper was able to achieve its 491 mph speed while carrying a 1.2kg payload, with Tomas’ post further explaining that it could hold the speed for four minutes on a full tank, climb to 6,500 feet in under 20 seconds, and fly for a distance of 31 miles at full speed.

Before the Boom Prize, Tomas was able to land a few sponsorships that got him a JetCat 250N engine; however, we’re not sure how this fits with the Boom Prize’s rules barring corporate sponsorship. Since the Boom Prize's announcement, however, Tomas has resorted to crowdfunding, asking for $50,000 in August after raising $20,000, the source of which he did not disclose.

Both the Kingchaser and the Reaper are aiming at Herbrich’s world record, but they are still far from the 1,234.8 km/h or 767.3 mph Mach 1 speed that they need to surpass in order to win the Boom Prize. Mach Initiative responded to Blake Scholl’s X post about the Boom Prize, saying “challenge accepted,” but the rules may make them ineligible for the prize, making the Reaper a real contender for the $800,000, as it currently sits unrivaled by any other US project.

France has approved a proposed tax that aims to tax the amount of internet data per gigabyte sent to French users in the country, with major companies like Google, Netflix, TikTok, and Meta now in the crosshairs. According to minutes released by the National Assembly, members voted 11-10 in support of the motion on October 30 — which now means the new law has been added to the country’s next budget, despite major opposition from the government side and the finance committee rapporteur. The latest amendment, introduced by socialist lawmaker Laurent Lhardit, could see content providers pay €0.055 (roughly $0.06) to the government for every gigabyte of content distributed to French users.

With approximately 154 million users and counting in France, the proposal’s authors say such a tax could generate around €10 billion ($11.2 billion) in annual revenue for the government. “This amendment proposes a tax on digital data flows emitted by content providers, to be borne by the providers themselves. Its revenue is estimated at €10 billion.” Said Laurent Lhardit. The said proposal, though yet to be passed as it must survive further parliamentary scrutiny, aims at companies that generate substantial internet traffic as opposed to taxing their advertising revenue, profits and subscriptions. Its explanatory notes particularly mention Google, Meta, Netflix, Amazon, and content delivery network Akamai as major potential contributors. Notably, TikTok and other similar platforms could fall under this broader scope depending on how these rules are applied.

Internet service providers, including Orange Free and Liad, would be required to measure the volume of data and identify the companies responsible for sending it to French users. The Data will then be collected and submitted quarterly to France’s tax administration under the supervision of telecommunications watchdog Arcep. Aside from the €0.055 per gigabyte levy, the proposal also includes a cap of 15% of a company’s French revenue or 2% of its worldwide revenue. It also accounts for content delivery networks (CDNs), which store copies of online content closer to users to reduce loading times and network congestion.

Approximately 200 billion GB of data pass through French internet providers annually, with about five major companies identified in the amendment accounting for about half of that traffic. Lhardit argued that tax would primarily affect global technology companies because they generate most of the data traffic reaching French subscribers. He also disputed that those platforms would merely push additional costs to users, using Facebook, Instagram, and TikTok, which provide free access, as examples. “The rapporteur keeps arguing that the taxes will be passed on to consumers. However, I fail to see how a tax can be passed on to users of Facebook, Instagram, or TikTok since these services are free,” He stated.

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Lawmakers opposed to the proposal, however, argue that it is likely to endanger user privacy. Government Minister David Amiel questioned whether the internet traffic could be measured and attributed to content providers without introducing complicated monitoring requirements. Those concerns extend to potential consequences for French business. Lhardit, however, noted that businesses would pay relatively modest costs, citing TF1 as an example, which requires broadcasters to contribute between €200,000 and €300,000.

Several European countries already impose digital service taxes, generally based on revenue from Ads, marketplaces and other digital activities. In March, Meta announced it would introduce location-based fees of between 2% and 55 for advertisers to offset digital services taxes and other government levies in several countries, including France, Italy, and Spain, from July 1, 2026 . Previously, the European Commission has rolled out major enforcement actions against major technology companies, including a €5 billion fine against Google over Android antitrust violations, Facebook’s €100 million fine for misleading European regulators, and the EU’s investigation into Facebook’s ability to link WhatsApp accounts.

Last year, Apple and Meta were also fined €500 million and €200 million, respectively, a move that strained the relationship between Brussels and Washington, with the U.S. calling the penalties ‘a novel form of economic extortion’ that it will not tolerate. That said, the amendments' adoption in parliament is only the first step. If it can clear the remaining stages of France’s 2027 budget process, it can become law, with parliament scheduled to vote on the budget’s first section in early November.

The U.S. Department of Justice (DOJ) just announced indictments resulting from Operation Ghostbusters, which investigated widespread student loan fraud. According to the agency, bad actors used stolen identities to sign up for student loans and used AI to complete the application, making it appear that the fake student was attending online classes.

While the schools receive the majority of the proceeds of the fraudulent student loan, the remaining amount is funneled into the bank accounts of the fraudsters, who then use mules to move the money from U.S. bank accounts to overseas conspirators. On the other hand, unwitting victims who have skipped college and instead directly joined the workforce after high school are saddled with student debt for a degree they never even signed up for.

Multiple community colleges and universities across the United States started suspecting fraud in the Federal Student Aid (FAFSA) program in 2021. Estimates suggest the U.S. government lost over $200 million because of this activity, and the U.S. Department of Education Office of the Inspector General referred the case to the FBI Richmond Field Office. Currently, more than 40 open fraud cases involve 26 FBI field offices and 20 U.S. Attorney’s offices. The DOJ called it Operation Ghostbusters, which has already resulted in the indictment of five individuals.

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“Today, we are announcing the first results of Operation Ghostbusters — a nationwide effort to stop a major fraud scheme targeting federal student aid, low-income Americans, and the American taxpayer,” Assistant Attorney General Colin M. McDonald of the Justice Department’s National Fraud Enforcement Division said in the statement.

“The Department is charging five defendants and announcing the seizure of more than $750,000 in cryptocurrency tied to this scheme through a network associated with Kenyan nationals. This is the first international seizure of its kind in Kenya. If you steal American identities, if you steal taxpayer dollars, if you exploit programs meant to help students build a future — we will find you. Whether you are in Nebraska, New Jersey, Virginia, or Nairobi, we will pursue you.”

While online fraud has existed since the birth of the internet, AI has let scammers become more sophisticated and target bigger payouts. One example is a scammer who used an AI-generated image of Brad Pitt to scam a 53-year-old woman out of $850,000. However, larger groups also use AI tools for their money-making schemes, such as North Korea, which has reportedly used AI to help its workers get jobs in Western companies and earn money to fund the nation’s coffers. The U.S. is taking steps to shut down student loan fraud by deploying automated fraud detection systems, but the added burden of identification and false positives is negatively affecting the country's most vulnerable students.

Darkbloom, a decentralized AI inference network that aggregates and sells spare compute capacity from Macs, told its members to expect revenues to fall as token prices continue to drop. According to The Register, the price per million tokens for Anthropic dropped by 77% in under five months, falling to $0.62 in October 2026 from $2.65 in May.

“Data centers have become cheaper. Output per NVIDIA cluster is up ~20x this year, and their margins are wide enough to cut prices 90 percent and still profit at 50 percent,” said Kydo, a researcher from Eigen Labs, the developer of the compute platform. “The market caught up to our price. We could go lower, but then most of you would earn even less [than] nothing.”

It’s estimated that someone running an M5 Pro Mac Mini with 64GB of Unified Memory could earn as much as $192 per month. This is the best-case scenario, though; the median earnings of the top 50 operators on Darkbloom’s leaderboard are about $159.86 per month. This translates to about $5.26 daily or $1,918.50 annually. However, if we account for Anthropic’s 77% token price drop, earnings will fall to a measly $36.77 per month, or about $1.23 per day.

AI token prices are falling so fast that they've even outpaced Moore’s Law, with some reports saying AI is becoming 50% cheaper every quarter and up to 13 times more affordable every year. News of companies blowing through their annual AI budgets in months has less to do with per-token price and more with the number of tokens, as agentic AI use outpaces token price drops. Aside from that, OpenRouter, Darkbloom’s primary client, has started prioritizing decode speed, quantization, and prefill speed, putting users without a top-of-the-line Max or Ultra Apple chip at a disadvantage.

Despite that, Darkbloom says it will not sit idly by and let some users lose out because of these changes. The network is analyzing model demand to advise members on which models are in higher demand right now. It’s also looking at other kinds of workload beyond inference, including distributed CI/CD jobs, improving network performance to better compete against other computer providers/aggregators, and finding other clients outside of OpenRouter.

In case you're not yet subscribed to our Tom's Hardware Premium subscription service, here's what you've been missing out on this week. We detail our thoughts on Anthropic's filings pre-IPO, Cadence's new suite of LLMs to power its EDA tools, how Nvidia is securing its entire supply chain to serve AI data center demand, how Vera CPUs are landing in the cloud, an update to the ongoing TP-Link saga, and more. Remember, to access any of the articles listed here, you'll need to arm yourself with a subscription.

We've been hard at work behind the scenes to determine exactly how locally-run AI models and workloads stress consumer-level components. While throughput and tokens per second are indeed important factors, our resident CPU expert Jake Roach has begun laying out his vision for how Agentic workloads tax CPUs, and how we're building benchmarks in this newfangled AI world that the PC hardware space finds itself increasingly entrenched in. It's not often that readers get the chance to look at our behind-the-scenes work, and this piece offers a look at some of the foundational exploration we're doing around how to define what an agentic AI CPU benchmark might look like in the future.

• Our first step at defining a CPU benchmark for agentic AI — how we can measure throughput of agentic AI workloads without any GPU involved

As the rise of Artificial Intelligence increasingly becomes attached to powerful, locally-run hardware, you might encounter terminology and nomenclature that you may otherwise not be familiar with. So, we've published a primer on all of the basic terminology that you need to know moving forward. Currently, you get breakdowns of how inference works, details on quantization, context, KV cache, and more. We're striving to update this page as our exploration with local AI grows, to serve as a handy bookmark. It's not a mere dictionary either, as we contextualize how each of these things relates to the larger overall model, how it might be served, or where you might encounter it when trying to run an AI model of your own.

• A primer on key AI LLM concepts — KV cache, weights, quantization, and how inference actually works

Nvidia's Vera CPUs were a salient part of a swath of Oracle announcements this week. The chip, usually found alongside Nvidia's NVL72 rack, is now generally available, but curiously, standalone Vera instances are also available on OCI superclusters. The CPU is specifically designed with Agentic AI workloads in mind, and it just so happens that we've benchmarked an Nvidia Vera server just this week to highlight the efficacy of how well Vera might perform under certain workloads.

• Standalone Nvidia Vera CPUs land in Oracle Cloud's multi-gigawatt deployment — our independent benchmarking of a local server reveals Vera's massive memory bandwidth lead over dual Xeon 6 server

Last week, we covered TP-Link's proposal to allow the router manufacturer to sell its Wi-Fi 8 products in the U.S. Now, according to a new report, it's alleged by the FCC that TP-Link's own reports contain "false or incorrect information in its applications" related to equipment authorizations for four different models of Wi-Fi 8 routers that the company hopes to sell stateside. As the saga continues to develop, we attained a statement from TP-Link on the subject, as the situation continues to develop.

• FCC rebuffs TP-Link again as it attempts to sell Wi-Fi 8 gear in the US — FCC alleges inaccurate documentation and out-of-spec power levels for routers

Anthropic's large and sprawling S1 filing made headlines this week as the company prepares for an IPO. Within the document, it details many (but not all) of the numbers driving one of the world's foremost AI labs. Once you get past the mind-bogglingly high $800 billion in GPU and TPU contracts, the company has shown that most of its compute capacity isn't actually served to customers, and is instead dedicated to R&D for new models, features, and testing. There's a huge amount of information to sink your teeth into here, and despite what many might believe, the company's revenue per compute dollar spent is actually in Anthropic's favor, shattering the myth that tokens cost more in compute than they do for a user.

• Anthropic’s IPO filing shows most of its compute goes to R&D, not serving customers — company details massive $800 billion in TPU and GPU contracts

As neoclouds and hyperscalers (who rent compute out to companies like Anthropic) scale up and scale out, domains become exceedingly important. With copper materials coming up to their natural limits, eyes have turned to the photonic domain to alleviate bandwidth bottlenecks. Now, PsiQuantum has entered the domain, showcasing a prototype photonic switch that loses less light than other devices on the market, and the company expects to develop and mature its solution and is likely to enter the photonic switch market.

• PsiQuantum turns its photonics know-how into an optical switch for supercharging AI data center GPU networks — 8x8 lab prototype loses less light than rivals, with plans to develop larger devices

Cadence has revealed Maestro, a suite of tuned LLM's, designed specifically to work in EDA flows using the company's in-house suite of agentic tools. We've covered the rise of agentic EDA tools closely, and Cadence's Maestro suite appears to be the company's own shot across the bow in helping its customers automate elements of their own chip designs. In initial results compared with general-purpose AI models, Cadence claims up to a 30% improvement in the quality of results and that Maestro effectively uses fewer tokens for the same task, with up to 5x the token efficiency compared to off-the-shelf AI models.

• Cadence reveals Maestro, fine-tuned AI models built to run specialized chip design agents, with early access coming for 'select customers' in Q4 2026 — claims up to 30% higher quality results and up to 5x token efficiency over a general-purpose AI model

But all of those new AI chips have to come from somewhere. With the ongoing, ceaseless AI buildout, firms are looking to secure their supply chains, with Nvidia one of the chief companies among them. As demand continues to soar, the very real limitation of fabs is particularly highlighted in the realm of the substrates used to create PCBs. Ajinomoto Build-up Film, or ABF, is one of the hottest commodities in the chipmaking industry as a result, and Nvidia has announced plans to secure the material from Ibiden, in addition to other critical materials used in chip packaging, testing, and more. Of course, Nvidia isn't doing this out of the goodness of its own heart; it's doing it in exchange for reserved production capacity.

• Nvidia is bankrolling AI accelerator supply chain buildout to reserve production capacity — announces billion-dollar deals with six major suppliers across substrates, advanced packaging, semiconductor testing, and optical engines

The ramifications of AI are still being felt across the globe, but if you've been on social media over the past several weeks, you may have found that there's one particular trend: Decompilation and recompilation of video games, recompiled to work natively on PCs. We detail how additional software tools like Reverse Engineer Anything and Ghidra are powering the boom.

• Why native PC conversions of console games are suddenly everywhere — Reverse Engineer Anything gives AI agents a unified toolkit, and byte-for-byte checks keep hallucinations in line

To read any of the articles we mention above, as well as unlock Bench, you'll need to purchase a Tom's Hardware Premium subscription. Your support goes a long way in helping us to publish deeper, more insightful articles that might not otherwise be covered.

Although the consumer PC market is shrinking and will continue to shrink in the coming quarters, Intel is confident that the overwhelming demand from the AI industry for everything that can compute or store data will offset declines in the client space. To that end, the company is reallocating some of its capacity from client to data center CPUs, expanding its Fab 24 in Ireland to make more Xeons, has begun equipment installation into Fab 62 in Arizona ahead of schedule and vows to make 18A chips there, and is expanding its advanced packaging capacity in New Mexico and Malaysia.

"The industry continues to navigate one of the most significant supply shortages in history, driven by the unprecedented demand for Super Intelligence," said Lip-Bu Tan, chief executive of Intel, during the company's earnings call. "Against this backdrop, I am pleased by the progress we made growing our server supply in Q3. I am mindful about the ongoing challenges we face in the future quarters to keep pace with server demand."

More Intel 3-based Xeons

Intel's manufacturing expansion comes as demand for server processors continues to exceed supply despite substantial production increases over the next several quarters. The company's Data Center and AI (DCAI) business generated $7.3 billion in revenue in the third quarter of 2026, up 17% sequentially and nearly 80% year-over-year. Intel expects demand for data center CPUs to remain strong in 2027 – 2028 as AI infrastructure operators deploy more CPU-intensive agentic and inference workloads.

To meet demand for Xeon CPUs in the coming years, one of Intel's immediate priorities is to increase output of Xeon 6 'Granite Rapids' processors, which are made using its Intel 3 process technology. The company recently began expanding production capacity at its Fab 24 manufacturing complex near Leixlip, Ireland, but this capacity will only come online by the end of 2027, unless the move can be accelerated.

To further increase its Intel 3 capacity, Intel plans to convert some manufacturing resources at its Oregon facilities from Intel 18A to Intel 3. These resources were originally intended to manufacture Panther Lake client processors, but weaker PC demand has allowed Intel to prioritize higher-demand data center products. The company expects the additional Intel 3 capacity in Oregon to enter production in the second half of 2027.

"What we are taking offline to put Intel 3 into Oregon is 18A wafers, which we are generally going to build Panther Lake products," said Dave Zinsner, chief financial officer of Intel. "We see the opportunity with some of that capacity freed up to go produce more data center wafers."

Granite Rapids has already become Intel's fastest ramping server processor family, but demand continues to exceed available supply, according to the company's management. Intel is also improving manufacturing yields and reducing production cycle times to extract more usable processors from its existing capacity. According to the company, Intel 3 manufacturing costs have declined by approximately 15% since the first quarter of 2026.

Prepping for Diamond Rapids, Nova Lake

Intel is also accelerating the expansion of its leading-edge manufacturing operations in Arizona. The company is ramping Intel 18A production at Fab 52 and is accelerating equipment installation into the adjacent Fab 62, which is set to become the company's second high-volume manufacturing facility for 18A, 18A-P, and derivative process technologies.

These investments will support future Intel 18A/18A-P-based products, including server CPUs, although the company has not disclosed the precise allocation of manufacturing capacity between client and data center products at the facility. While the latter is natural, Intel said during the conference call that it expects to win some market share in the client space with its Core Ultra 400-series 'Nova Lake' CPUs, which will require the company to produce these parts in high volumes.

To support its capacity expansion, Intel aggressively procured new tools in the third quarter.

Not only wafers

While expanding production of Intel 3 and Intel 18A/18A-P wafers is a must to increase output of data center CPUs, producing more wafers will not necessarily solve Intel's supply problems. The company also faces shortages of advanced organic substrates required to package its multi-chiplet processors (or rather system-in-packages, or SiPs?) that rely on EMIB and/or Foveros 3D packaging technologies. Intel has been working with substrate suppliers to expand their production capacity and expects approximately 40% additional substrate capacity to become available in the second half of 2027.

"We are not only constrained on the wafer side, we are also constrained on the substrate side," Zinsner said. "So, it is important we get the substrate piece of this ramped up to be able to meet the demand."

In addition, Intel is expanding its advanced packaging operations in New Mexico and Malaysia to support growing demand from internal product groups and external customers. The company already uses its EMIB technology to connect multiple compute dies in Granite Rapids processors, while its newer Clearwater Forest Xeons combine EMIB with Foveros Direct 3D and hybrid bonding to stack Intel 18A compute dies on Intel 3 base dies. Additional packaging capacity will be essential as Intel increases production of these processors and introduces more complex multi-die products as well as assembles advanced system-in-packages using EMIB, EMIB-T, Foveros 3D, and other technologies for external clients.

"On Advanced Packaging, we are pleased by the progress we are making with EMIB and EMIB-T for our internal products," Tan said. "Both continue to see significant external customer traction as well. We continue to improve on execution, working with our supply chain to secure the capacity to meet the needs of our Advanced Packaging customers. We are also seeing robust progress in the readiness of Hybrid Bonding Interconnect (HBI), which is an enabler of 3D chip stacking for our upcoming server products."

Capital needed

Intel's expansions will require substantial capital. Intel expects its capital expenditure (CapEx) budget to reach approximately $20 billion in 2026 and increase significantly in 2027 (though it did not reveal any firm numbers). The company said firm purchase orders to semiconductor equipment suppliers increased by nearly 40% sequentially as it secured tools for its manufacturing expansion. Intel is also evaluating opportunities to accelerate construction of its semiconductor manufacturing complex in Ohio, where the first major structural truss was recently installed.

"We have made good progress securing the equipment required to support our growth plans with firm purchase orders to equipment suppliers increasing nearly 40% sequentially," Zinsner said. "We are also accelerating clean room build-outs, including Fab 62 in Arizona and exploring opportunities to accelerate in Ohio.

Yet Intel's capacity expansion will take time to produce results. New equipment must be installed and qualified, additional wafers must pass through lengthy manufacturing cycles, and substrate suppliers must increase their output (which will likely require them to procure new tools and install them) before Intel can ship more finished processors. Consequently, the company expects supply constraints to remain particularly severe during the first half of 2027, even as the client PC market is set to continue contracting.

Nevertheless, Intel expects its data center business to continue growing and believes that additional manufacturing capacity, higher yields, and improved substrate availability will enable it to capitalize on demand for AI infrastructure in the second half of 2027 and beyond.

Developer Jan de Jager has created a full Intel 486 PC system in a browser. On the e486-web GitHub page, de Jager boasts that the emulator is coded in 100% pure JavaScript. The project’s fuller title is the Retro486 Modular Browser PC. Claude gets a development credit, so we guess you could say e486-web was (partially) vibe-coded.

The live demo you can boot with a single click is impressive, with tabs to interact with and configure your browser-based, dependency-free Intel 80486DX2-66 PC in a browser. The first tab is dubbed the CRT monitor, and this is where you will spend the most time using the command line, apps, and games. However, it is also very appealing to be able to adjust your PC configuration in the tabs for Motherboard & Slots, Storage Bay, Custom Card Studio, and System & Diagnostics.

I couldn’t resist taking the e486-web for a spin using the live demo. The CRT monitor screen is totally charming, with various display options including color and scanlines, and tributes to classic CRTs with green phosphor, amber, or paperwhite displays. This is the screen where you will also type DOS commands and interact with whatever you get to run.

The next tab in this comprehensive 486 PC system emulator lets you pick a motherboard design and, probably more importantly, add and remove expansion cards. The default system comes with a VLB graphics card, Sound Blaster 16 ISA card, a multi-I/O controller, network card, and more. All the cards seem to be configurable. A Custom Card Studio tab lets you create and test more cards, and it looks quite advanced.

I spent quite a lot of time in the Storage Bay tab, switching through the OS and software disks inside the “1-Click bundled & Cached Disks” collection. FreeDOS started fine, but I got some errors when I wanted to test some of the games in the library, which includes Doom, Wolfenstein 3D, and more. Though tempted to find and install Syndicate on the hard disk that the emulator can create and configure, I didn't get around to it yet. However, the Bullfrog classic is one of the first games I played on an actual 486DX2 system in the 1990s, and I also played it on Amiga. This browser-based emulator features browser persistence, so e486-web settings can be maintained between sessions, and it automatically caches ROMs, floppy images, and custom uploaded .img/.iso files for your convenience.

Another tab in this extensive system emulator is called System & Diagnostics. Here you can switch the CPU to a different model, view the CPU registers, and nose around in RAM, VRAM, storage, BIOS, boot sectors, and so on.

Currently e486-web is in version 0.1, and the GitHub shows that most files were uploaded/updated about a week ago. Some files are just two days old, though, so hopefully wrinkles are busily being ironed out by de Jager and Claude. The presentation of this no-downloads live emulator, and the conveniently tabbed interface and configuration options, show lots of promise.

Reverse-engineering games for converting them between systems is currently a hot topic, and it's getting messier by the day. According to multiple user claims, Anthropic's Claude Code and/or Opus 5.5 model are blocking attempts at innocuous reverse-engineering, including but not limited to those using the popular Reverse Engineer Anything (REA) tool. That issue may be linked to a text rule that's now in force, which allegedly instructs the model to avoid "[recreating] a specific existing game." The updated rule appears to be a move to limit a flood of AI-cracked and cloned titles, with some game devs even recently being hit by extortion scams that threaten to clone their game demos into complete games before they have a chance to finish and release the original.

According to Twitter user Chasm, the game-related textual rule is "Do build original games, games in a genre, and games from the user’s own designs and assets. Don’t build a recreation of a specific existing game, even if it is renamed or called original." There is no official or additional confirmation if this is the actual rule text, but it's plausible enough given the very clear refusals that devs have been encountering over the past week.

This situation is tricky to analyze due to the varying types of reports. First, the existence of a game-related rule is real, as Claude Code's GitHub is seeing an influx of issues complaining about refusals to do standard game work halfway through development, when replicating a game mechanic, and patching a tabletop-like game. It's possible that some of those reports have been made by users with nefarious purposes, but at least one is a bona fide developer with a real track record.

As for reverse engineering, there had already been a smattering of user reports across Reddit and other platforms, wherein Claude refused to perform tasks like (allegedly) analyzing a simple Windows crash dump, clearing up junkware on a Samsung TV, or just using REA altogether.

The simultaneous timing of both game- and cyber-security restrictions may be a coincidence, and yet there are complaints from purported game developers saying that Claude blocked their own games' anti-cheat development and security reviews, under the justification that its safeguards were triggered.

Anthropic introduced its Cyber Verification Program on October 6, essentially saying that access to its frontier models' advanced cybersecurity capabilities would be limited to entities covered by Project Glasswing, taking effect immediately.

It's possible that whichever ruleset was applied is the same one that's causing the cybersecurity-related refusals. Still, it's odd to see that 24 days have passed since then, and yet reports of bot refusal are only coming in since the last week or so.

As for possible motivations behind these restrictions, they are twofold. The first and most obvious one is that Anthropic — and, by extension, other AI players — are preemptively protecting themselves from potential lawsuits from software companies, including game studios, for offering the means to reverse-engineer their software.

There is no official statement on these matters from Anthropic or other AI companies so far. Anthropic isn't the only one playing this game, too, as recently OpenAI deactivated the account of another person trying to clean up their TV's software.

The recent rash of AI-powered clones of application suites like Adobe's suite and Microsoft Office 365, as well as the countless recomps and decomps of console games, likely has most every software company on earth rethinking their life choices. And probably also in frequent contact with their legal teams.

The online reaction was swift, suggesting that users looking to perform reverse-engineering work simply use Chinese, open-weight, or local LLMs instead of Claude or GPT. This, at the same time, highlights the irony of frontier AI companies that copied the world's information and art for their training sets and are now concerned with copyright infringement.

And yet there are situations that merit some discussion, like the recent controversy about the Steam Next Fest being chock-full of AI slop copied from existing titles. That particular situation is getting particularly nasty, as game developers are reportedly being extorted under threat that their game demos will be cloned into complete games before they have a chance to finish and release the original.

Adding insult to injury, this is seemingly being done to small studios whose main priority is usually "having food," rather than big publishers looking to microtransaction everything into a new yacht for shareholders.

Exin Therapeutics, a biotech startup focusing on next-generation neurotherapeutics, claims it has successfully conducted brain surgery on a mouse using an AI agent. Its co-founder and CEO, Gabriel Ocana-Santero, shared the entire procedure on X, showing how the AI agent chose the gene therapy vector and dosage and the brain region where it would be applied. The AI agent also drove the robotic arms that injected the medication into the mouse’s brain. Thankfully, the operation seems to have been a success, with the mouse recovering well and recorded moving around in its enclosure.

“Autonomous surgical robots exist and they are better at the mechanics than our bench rig, [to be honest]. But in every one of them, a human decided what operation to perform, and the robot executed it,” Ocana-Santero said. “Nobody told this agent what to give or where to put it. It got a phenotype and a shelf of candidates and had to work out the therapy and the coordinates itself. I am not aware of an agent previously choosing a treatment and a brain target and then delivering it into a living mammal.”

The team used Anthropic Sonnet, which received a mouse model of epilepsy, control of a motorized surgery rig, and 19 gene therapy vectors in a spreadsheet. Aside from that, it also had access to three research papers, allowing it to choose the brain region where it should apply the treatment. Nevertheless, a licensed veterinarian was purportedly overseeing the entire operation, with every irreversible step requiring approval before the AI agent could proceed. Aside from that, the entire system also had an emergency stop button that would cut the power to every motor in the surgery rig.

The only thing that the vet surgeon on duty did was to do the craniotomy procedure to expose the brain, as the AI agent wasn’t allowed to do that yet for safety reasons. There were also several safeguards that the AI agents were banned from overriding, which even the human operators couldn’t bypass, necessitating a manual fix to the agent’s code during the operation.

What’s interesting, though, is that the startup only used Anthropic’s mid-range Sonnet model for the operation. It initially tried using Opus 5.5, but it immediately refused to do any work after it detected that it was working on a biological experiment. The older Opus 5 model was able to run the entire procedure on paper but refused to do live surgery. “The block isn't the rig and isn't the inputs. It's me. My own output was stopped when I started writing the plan for this surgery... if I try, it gets cut off the same way, and the worst version of that is me cut off partway through a live procedure with a needle somewhere in the brain,” the LLM reportedly told the Exin Therapeutics team. “Starting something I can't finish on an anesthetized animal is worse than not starting.”

Despite the seeming success of the operation, some people are concerned about the ethics of such a procedure. While AI LLMs have been useful in medical research, adding them to sinus surgery has seen malfunctions skyrocket by more than 12 times. Aside from that, relying on an AI to make every decision in such a risky procedure puts accountability in question, especially if something goes wrong. We also haven’t even touched the long-standing ethical question of animal experimentation in the medical setting. Still, some users applauded the experiment, with one comment saying that they’re excited about the possibilities of these integrated technologies, as their son has been suffering from drug-resistant epilepsy for 19 years.

Dave Plummer, former Microsoft engineer and wizard of Task Manager, Windows Pinball, and now TMOG, has an interesting project in his hands once again. After seeing exactly how many zeros were in the price tag for a megabyte of memory for his vintage PDP-11, he decided to take matters into his own hands and conjure up a new RAM board.

The PDP-11 is from 1970, and later models could fit in as much as four megabytes of memory. Plummer enlisted the help of ChatGPT and the EasyEDA online circuit simulator, though he notes that he hadn't done complicated projects like this before. As several PDP-11 models used Q-Bus boards for main system components, he designed this RAM as a standalone card, then tested it on a backplane alongside another board containing the system's M8190 CPU.

Atop it, he placed a Lattice MachXO2 FPGA serving as a memory controller (model 2000HC, if you're wondering), connected to four ISSI 1 MB memory chips. After adding a U2S I/O chip, a bank of DIP switches, comm ports, and sprinkling a handful of capacitors, he had a complete circuit. The M8190 board design has a 22-bit bus and reserves the top 256 KB for I/O and ROM.

However, it wasn't a first-time-lucky project. Plummer encountered a few abortive starts, as he'd never flash-programmed an FPGA chip before. He fixed that after a few attempts, but then he fell into what appears to be a classic PDP-11 trap.

The machine's memory bus design means it can only access entire words (two bytes) at once, and treats the lowest bit in a memory address as a "lane" selector that picks between the low or high byte in the word.

When reading, the CPU will pick out the right byte itself, but Plummer's "memory controller" still needs to return two. Similarly, on writes, the bit selector must be read and then the proper byte selected so that only that will be updated, as the bus lines will still carry an entire word that could otherwise write invalid data.

Plummer seemingly wasn't aware of this before, and one commenter joked that he'd "built 4MB of memory that turned out to be 2MB", to which he replied that the memory "only returns every second word," matching the aforementioned design pattern. Although there are no further updates, since more commenters pointed this out, we assume that he implemented an easy fix in his FPGA code.

One person suggested that he sidestep the entire problem and implement his memory in software via a Raspberry Pi instead, wired directly to the PDP bus. Plummer replied that he'd basically done that already last week with a modern QBone DEC interface board, which in turn contains a Beaglebone Black atop it (similar to a Raspberry Pi).

Just like we suspected, our fellow Australian hardware examiner TrashBench is up to shenanigans again. Not too long ago, after putting a graphics card in the "wrong" place for much better cooling results, he's ... thinking bigger, let's put it that way. He took an old 4-kW outdoor radiator-fan unit from a split air-conditioning setup (sans gas) and hooked it up to a water-cooling loop to assess its performance.

He kicked off the video with his best impression of Crocodile Dundee, and set out to rearrange the unit for the task at hand, as he only needed the actual radiator, fan, and pump. Most of the fiddly guts were taken out, and casters were added for convenience. TrashBench then added a bog-standard reservoir to the main enclosure.

Moving on to the PC side, he converted a T-joint into a Y-joint with judicious application of heat and force, and, being the ever-clever craftsman, designed 3D-printed mounting brackets for the various cold plates. It was around this time that it was made certain he'd be successful, as at 02:49 you can see that his knuckles paid the requisite blood toll to the machine god.

After firing up the entire system, he set to benchmarking, enlisting both a modern RTX 5060 and a hotter, earlier RTX 3080 for the process. The 5060 is a very power-efficient GPU, and its temperature with the standard cooler was a tame 52ºC. However, the mighty power of four kilowatts' worth of cooling brought that down to just 33ºC, around the temperature of lukewarm water, and a 16ºC drop altogether.

With such a wide thermal range to work with, he set out to overclock the living daylights out of the GPU, moving the boost clock needle from 2902 MHz up to 3300 MHz. Despite the 14% clock-speed improvement, average FPS saw a mere 3% uplift, clearly highlighting how perfectly Nvidia has the power/performance curve dialed in since the RTX 2000 series.

He then moved on to the RTX 3080, a beastly, much more power-hungry card that could provide a worthy challenger for the Fujitsu radiator. It was promptly defeated, and its temperature went from 65ºC down to just 28ºC, or in other words, not much above ambient. Overclocking this card, however, also yielded a mere 3% fps, barely worth the man's time.

The final challenge was seeing how well the teeny weeny Fujitsu could handle a combined GPU and CPU load. The processor on hand was probably the worst possible consumer chip to try and keep cool, the lightning-fast and flame-hot Intel Core i9 14900K.

TrashBench hooked it up and saw CPU temperatures drop 18ºC down to 76ºC, a strong achievement for this silicon outside of rooms with liquid nitrogen buckets. As for the graphics card, its heat sensor simply didn't move at all, suggesting that the radiator had far greater headroom than TrashBench had hardware.

A study published on October 22 in the journal Forensic Science International reports that researchers from Curtin University in Perth, Australia, have developed a forensic method to analyze 3D-printed PLA parts and match them to their respective filament, correctly matching 26 out of 28 samples of PLA. This potentially provides a way to link seized 3D-printed “ghost-guns” with other seized filament, allowing police to form a clearer network for the manufacturing and distribution of the 3D-printed firearms.

3D-printed firearms have become a big problem worldwide, as they are untraceable and even compostable in the case of PLA, with tools like this AI 3D-printed gun generator making it scarily accessible. Serial numbers, registration, and a network of licensed distributors are usually the first line of investigation when it comes to crime involving firearms, but easily manufacturable, untraceable, disposable plastic firearms are a new threat with no clear way to crack down on them yet.

However, this research, by Dr. Georgina Sauzier et al. from Curtin University's School of Molecular and Life Sciences, along with forensics experts from ChemCentre, has devised a way to trace a printed object to a specific filament product, down to the brand and type.

The process of 3D printing changes the filament’s physical properties, and this change can be measured. In this research, differential scanning calorimetry (DSC) is used, a thermoanalytical technique that lets the sample go through a range of temperatures, and the instrument measures how heat flows through the sample. It measures how much energy or power is used to keep the sample at a certain temperature or to change its temperature, giving us a graph of its thermal properties.

The sample’s thermal properties can then be cross-referenced with its unprinted filament, creating an association between them. As of now, the tested PLA products are the only successfully identified material, with PETG and ABS not providing enough information to link them to their filament product. Sauzier blames the amorphous nature of those filaments, giving very similar results with no clear identifiers linking them to their respective filament products. PLA has a semi-crystalline molecular structure, which provides clearer temperature transition points under the temperature tests as the crystalline structures break down and rearrange.

However, ABS and PETG did show some promise when tested using ATR-FTIR (Attenuated Total Reflectance-Fourier Transform Infrared), which uses infrared to identify chemical bonds and material composition. The team did find differences in additives using this method, but still did not give enough information to link them back to their respective filament products.

The research is still very early, with more rigorous testing to be done, as the team printed using only one machine, printed small sample tiles cut with scissors, and left them for a year in monitored office conditions before testing. Properties could further change as cooling rates vary greatly depending on what the printer is printing: Faster cooling and slower print speeds on overhangs, or slower cooling and faster print speeds on infill sections. Different printers may also affect the physical properties, adding one more factor in the chain.

So far, correctly identifying 26 out of 28 samples is a great start to cracking down on dangerous, untraceable illegal firearms. When the research matures and can reliably identify a broad range of materials, it could potentially trace 3D-printed firearms to the printer and filament, potentially creating a list of where they were purchased and who purchased them.

A technology enthusiast claims that the oldest GUI application that still runs on Windows 11 is CDPLAYER.EXE from November 1992. The app had to run on Microsoft’s newest consumer OS without modifications or emulation involved to be chosen as the ‘winner’ of their experiment. Blogger Enlyth expected something from the early NT era, as Windows 11 doesn’t support 16-bit apps, despite its strong backward compatibility. Finding an app that is almost 34 years old that still works with a simple double-click is a remarkable result.

At the beginning of their journey to find the oldest Windows UI app that just works on Microsoft’s newest OS, Enlyth confirmed FREECELL.EXE from February 1993 would still run without issues. This game was distributed alongside the Win32s environment, designed to allow some 32-bit applications to run on the 16-bit operating systems like Windows 3.1 and 3.11. Win32s was made available in beta in October 1992, but went mainstream in July 1993.

A tip Enlyth received via a tech forum suggested that FREECELL.EXE was a candidate for the oldest GUI app remaining compatible with Windows 11. However, the blogger wasn’t satisfied with this easy win and dug through “some early NT 3.1 pre-release versions that seemed promising.” Build 347 caught the detective’s eye, specifically CDPLAYER.EXE, linked on Nov 16, 1992. That indeed seems to be the earliest GUI app that works without modification or emulation on Windows 11. But this knowledge almost defied Enlyth’s eye as their system is, like many modern systems, optical-drive-less. Luckily, adding a small DLL (cdemu.dll) allowed Enlyth to emulate a CD drive and demonstrate that the 1992-era application still functions as intended.

I wanted to verify that CDPLAYER.EXE was still usable on the latest Windows 11 release. Unfortunately, the multi-part.ARJ archive of Windows NT 3.1 that I found on Archive.org threw up errors when I tried to pick through its contents. I have a USB-attached optical drive that plays CD audio in Windows 11, so I thought I could get CDPLAYER.EXE to run without any extra DLL.

Lastly, Enlyth wanted to push the envelope a little and see what even more ancient Windows apps might work if they got some help from the Winevdm project on 64-bit Windows. “Using otvdm does feel like cheating,” admitted the digital sleuth, and we’d agree. However, being able to run 16-bit apps in Windows 11 ratcheted back the wheels of time. The result was that Enlyth managed to enjoy “a fully playable version of REVERSI.EXE dated 15-Nov-1985.” That’s a GUI-driven game distributed as part of Windows 1.01 and is roughly 41 years old.

First, it was the GeForce RTX 5090, and now the GeForce RTX 5080 – two of the best graphics cards have gone dark. After the Blackwell flagship left Nvidia's marketplace, the GeForce RTX 5080 followed suit. Prices for the sub-flagship have exploded to more than 2.2X its original $999 MSRP, and the Blackwell-powered graphics card is slowly going out of stock at various U.S. retailers.

As with the recent disappearance of the GeForce RTX 5090 on Nvidia’s marketplace, the listings and filters for the GeForce RTX 5080 have also disappeared. While its absence does not necessarily mean Nvidia has discontinued the graphics card, it does raise questions about its status and future availability. This move is especially fishy because other Blackwell SKUs, such as the GeForce RTX 5070 Ti and GeForce RTX 5070, remain listed on the marketplace, albeit marked as “Out of Stock.”

The continued presence of the GeForce RTX 5070 Ti and GeForce RTX 5070 suggests that Nvidia wants to keep the products visible even when they are not immediately available so that consumers can anticipate restocks. The complete removal of the GeForce RTX 5090 and GeForce RTX 5080 breaks that pattern. It fuels rumors that Nvidia has allegedly stopped production of the GeForce RTX 5090 and possibly the GeForce RTX 5080 too, in favor of a rumored GeForce RTX 5080 24GB.

Rumors claim Nvidia allegedly stopped producing the GeForce RTX 5090 to repurpose the valuable GB202 silicon into the chipmaker’s RTX Pro Blackwell lineup, which fetches higher margins. This has a lot of credence, since many agree that Nvidia stopped being a gaming hardware vendor long ago and has transformed into an AI provider.

Rather than simply discontinuing the GeForce RTX 5090, Nvidia purportedly intends to introduce the GeForce RTX 5080 24GB as the new flagship gaming graphics card. The thing is, a 24GB variant of the GeForce RTX 5080 would solve one of the biggest consumer gripes with the graphics card: the lack of memory. While a GeForce RTX 5080 24GB would offer the same memory as the GeForce RTX 5090, it simply isn't a direct drop-in replacement. The former has 2X fewer CUDA cores, a substantial performance gap that even a 50% memory increase can't make up for.

How much money are you willing to spend during the Black Friday sales?

The GeForce RTX 5080 launched in January of last year with an MSRP of $999. Demand quickly outpaced supply, and with the rapid price hikes due to the memory shortage, the market price has more than doubled its original MSRP. As of this moment, the most affordable GeForce RTX 5080 in stock, the PNY GeForce RTX 5080 ARGB Epic-X RGB OC, retails for $2,199.99, which is 2.2X over the original launch price.

While some stock remains, it’s selling out fast, and the remaining units will only get more expensive as availability dwindles. At this rate, the GeForce RTX 5080 is unlikely to be available by Black Friday. If you are set on a GeForce RTX 5080, you may have little choice but to act quickly and buy at today's elevated prices.

If the GeForce RTX 5080 24GB is indeed legitimate, the graphics card will almost certainly debut at a substantial premium over the original GeForce RTX 5080. Although Nvidia maintains that the RTX 5090 is still alive, the reality suggests otherwise. We have contacted the company for comment regarding the GeForce RTX 5080’s status.

Intel's shares plunged 30% on Friday, wiping out nearly $150 billion in market capitalization in a single day, the largest single-day loss in the company's history. The decline followed a disappointing financial outlook, largely driven by an anticipated drop of more than 20% in sales at Intel's client computing group (CCPG), the company's main cash cow. The declines quickly spread across the semiconductor industry. Although losses elsewhere were nowhere near as severe as Intel's, approximately $1 trillion in semiconductor market capitalization was wiped out on Friday, according to intraday estimates by Benzinga.

Fire sale

The PHLX Semiconductor Sector Index (SOX), which tracks the stock performance of 30 major semiconductor companies, is one of the key indicators of the industry's financial health. On Friday, the index plunged 9.14%, or 1,052.82 points, to 10,465.38 points as of 2:31 PM ET, as Intel's disappointing outlook triggered a massive selloff across the semiconductor industry.

Intel was by far the biggest loser, as other chip-related stocks, while Arm (-12.68%), AMD (-12.48%), Qualcomm (-12.14%), Applied Materials (-8.73%), ASML (-8.13%), Lam Research (-8%), Micron (-7.28%), and Broadcom (-7.1%) suffered considerably less. Then again, while Nvidia lost 5.66% of its market cap, this translates to around $270 billion, which is a massive amount of money. Paradoxically, Nvidia lost more market capitalization in absolute dollar terms than Intel.

In an unexpected turn of events, the downturn extended to server manufacturers and cloud giants too, as Dell (-12.67%), HPE (-8.10%), Supermicro (-6.96%), and Oracle (-6.88%) lost nearly 7% to 13% of their value. Reuters noted that investors increasingly question the scale of capital investments required to expand AI infrastructure as well as the sustainability of that spending.

Gloomy outlook

Intel’s client computing and physical AI group (CCPG) generated $8.1 billion in revenue during Q3 2026, down 5% year-over-year and 9% sequentially. Weaker demand and increased competition reduced client processor shipments by 14% year-over-year, which was partially offset by a 9% increase in average selling prices. CCPG accounted for nearly half of Intel's revenue of around $16.5 billion and the company's main source of cash. As strong demand for server processors only partially offsets declining client sales, Intel guided its Q4 revenue to be between $15 billion and $16 billion.

Intel expects the client market to deteriorate further in Q4 2026 and into 2027 as elevated memory prices and shortages of other components impact PC prices and demand. In general, Intel expects the client PC market to decline 10% in 2027 compared to 2026, which will inevitably have an effect on CCPG performance. While Intel expects its data center business to continue growing in 2027, industry-wide supply constraints will limit its ability to fully capitalize on demand.

2026 has been a mixed bag for Intel so far. On the one hand, the company achieved its highest market capitalization ever — $673.43 billion in mid-June — on growing sales of data center CPUs, strong sales of client products, and a return to profitability (if the $12.5 billion non-cash accounting charge associated with shares issued to the U.S. government is excluded). On the other hand, the company's capitalization has dropped by nearly 50% to $338.91 billion since mid-June (with $148.68 billion in just one day), as investors began to realize that the performance of Intel's CCPG heavily depends on memory prices, as customers and businesses tend not to like overpriced systems. Yet on the other hand, now that Intel sells fewer client processors, it can sell more expensive data center CPUs.

What about Apple?

Surprisingly, Apple (-1.66%), which only sells client products, was nearly unaffected by the performance of Intel's client computing group, as investors probably believe that the world's largest supplier of consumer electronics is more immune to memory prices than Intel. Ironically, if we count the A-series SoCs used in iPhones (which tend to outperform mainstream laptop CPUs), Apple sells substantially more processors than Intel, making Apple by far the world's largest supplier of microprocessors. Despite this, Apple's shares were largely unaffected by the semiconductor selloff.

There are several reasons for this distinction. Firstly, Apple's business model is fundamentally different. Intel sells processors to PC manufacturers, whereas Apple designs its own processors and sells them as part of finished products. Consequently, Apple captures revenue from the entire device rather than just the processor. Secondly, the two companies face different demand dynamics. Intel depends heavily on the PC market, while Apple's iPhone business follows the smartphone replacement cycle. Rising memory prices could affect both, but not necessarily to the same extent and at the same time.

There is an additional irony: Apple is also a major customer of TSMC, so its processor supply chain overlaps with those of AMD, Intel, and other semiconductor companies whose shares declined sharply.

While we should not directly compare Apple and Intel, the comparison illustrates that investors were not necessarily abandoning companies that design or use semiconductors, but were rather concerned about the profitability and growth prospects of these high-tech companies. And if we take a look at the situation from this point of view, the whole thing will start looking completely different.

OpenAI CEO Sam Altman has data centers full of memory, but seemingly it's the 32GB of RAM in your desktop that's the problem. In an interview over beers with the YouTube channel Cheeky Pint, Altman said that local inference, and, by extension, people having DRAM in computers at home, is "quite terrible."

When discussing local inference on home computers with open-source models, Altman discussed the assumption that people will be able to run local AI on their own computers, the ones they may already own or want to build. Then he explained why that's a bad idea (at the 29-minute mark):

"From a global allocation perspective, actually, it's quite terrible to have everybody have a bunch of the world's limited supply of DRAM sitting in their own computer and being used 5% of the time," Altman said. "You want maximum utilization in the cloud if you're trying to deal with this very supply-constrained environment. And somehow many people's intuition is the opposite of that."

While OpenAI does have some open-source models, its primary business is in closed-source models that run in data centers, so he's really defending the company's business model.

OpenAI isn't exactly threatened by some laptops and gaming rigs just yet, but local AI could mean people one day paying less for subscriptions to models like GPT-6 Luna if they have the power to run them at home. Altman's talk suggests that the company would prefer that more RAM go to OpenAI's data centers rather than home computers, which also sparks enthusiast fears of not being able to own your own hardware or software, but rather rent an agent and a computer in the cloud.

Some of what Altman is saying is based on an idea that data center buildouts won't slow down. It also assumes that RAM being used in AI data centers 24/7 is more worthwhile than RAM that is used for spreadsheets, web browsing, gaming, and maybe even local AI occasionally.

The rest of the wide-ranging interview includes talk of this week's PHP vulnerabilities, sandboxing models in training, and OpenAI's research in mathematics.

The UK’s Investigatory Powers Commissioner’s Office (IPCO) has flagged “a very significant number” of General Communications Headquarters (GCHQ) storage drives that have been missing for years and have yet to be found. According to The Register, the lost storage media have been known to GCHQ officials for years now, but they’re still unsure about the scope of the problem.

"I remain concerned that GCHQ has not yet established a sufficiently clear understanding of the scale, location, and recoverability of lost removable media that may contain IPA-related data, notwithstanding that the underlying issues were first drawn to my attention several years ago. While I recognize the technical and practical challenges involved, the passage of time without a comprehensive account heightens the importance of sustained senior focus and assured remediation. Given the potential sensitivity of the material involved, I will continue to monitor progress closely,” investigatory powers commissioner Lord Brian Leveson wrote in the report.

GCHQ acknowledged to The Register that the tracking system for its removable media had been insufficient, but it has since taken steps to prevent it happening in the future. "We recognize the importance of security of removable media in our systems and have a range of measures to mitigate the risks posed by these items. The findings in the report have been addressed comprehensively, and all necessary steps to correct this issue have been taken," a GCHQ spokesperson told the publication. It has also launched a program to help locate as many of the lost storage media as possible and will have an automated asset-tracking system up and running before 2027.

The GCHQ is the UK’s equivalent to the National Security Agency (NSA), making it the country’s primary signals intelligence and cybersecurity agency. Its primary mission is governed by the Investigatory Powers Act (IPA), which was passed in 2016 and lays out the legal framework for government surveillance, data collection, and intercepting communications. While neither agency has exactly confirmed what data the missing drives contained, it could potentially have information about GCHQ operations, administration, and other crucial data.

While the UK’s premier signals intelligence and cybersecurity agency losing thousands of removable drives may sound alarming, it’s not alone in running into blunders like this. The publication reported that Britain’s Ministry of Defence has lost 87 classified USB drives from 2003 to 2008, and that the UK police have also dealt with officers losing their assigned tech gadgets. Other governments have also made considerable tech missteps — for example, the South Korean government lost 858TB of data after a catastrophic fire destroyed a data center, and it discovered that it had no backups. Indonesia discovered the same issue when it was hit with a ransomware attack, with one government official calling the lack of backups “stupid.”

Some 700 Micron employees in Taiwan will receive five years’ worth of salary in bonuses as the company announced three bonuses in a single payout. According to Digitimes, this accounts for about 17% of the over 4,300 production-line employees working at Micron Taiwan. The remaining staff will not come out empty-handed, though, as they will receive three bonuses in the month of October. The news comes as Micron continues to battle worker unrest and unions fighting for increased pay.

The lump-sum payout consists of three incentives — an annual performance bonus that’s estimated to be between 7.5 and 12.5 months of salary for the majority of workers, a mass-production bonus amounting to 20% of their annual salary, plus a one-time $31,000 (NT$1,000,000) appreciation bonus which will be prorated for employees who haven’t reached a full year of service. Micron workers across the globe also expect a new quarterly profit bonus which will be paid 50% in cash and 50% in stock. This bonus will start in January 2027, with the first one to be paid 100% in cash.

Micron is following in the footsteps of Samsung and SK hynix, which have recently paid out massive bonuses to their workers of up to $400,000. This is a direct effect of the AI-driven memory chip boom, which saw the big three memory chip manufacturers — Micron, Samsung, and SK hynix — raking in massive orders from AI hyperscalers. The record profits that these companies are drawing from the appetite for AI data centers have led workers on production lines to demand a slice of the pie. Samsung workers voted to accept a $340,000 average bonus in May 2026, while SK hynix workers are set to receive $477,000 in bonuses this year, which could potentially grow to almost $900,000 in 2027.

Despite this seemingly massive payout, the workers’ union at Micron Taoyuan is still in the midst of a dispute with management regarding their pay. Some officials of the group have already launched a one-day strike as they call for clearer profit-sharing arrangements and terms for bonuses, with nearly 70% of members at the Taoyuan site turning down management’s latest offer. The two sides are tentatively set to meet on November 6 again for further negotiations. On the other hand, the union at Micron Taichung has already agreed to a 10% employer’s contribution towards worker pensions and a tripling of their day-shift allowance.

These bonuses allow workers to get a share of the massive profits that the chipmakers are raking in at the moment, allowing them to stay competitive for chip talent. However, their reliance on performance and profit bonuses also means that these incentives could fall precipitously if AI demand falls and the demand cycle returns to normal.

After Intel let loose its Panther Lake chips earlier in this year, followed by Wildcat Lake in April, the firm has been pretty quiet about upcoming consumer CPUs. Known leaker Jaykihn just let a tiny cat out of the bag, though: the base specs for Moon Lake. The diminutive chip is expected to have four low-power-efficiency (LP-E) cores, and zero performance cores. These are rumors, so have your salt shaker at hand.

Given the all-efficiency layout, it's a fair guess that the cores will be Darkmont LP-E silicon. The leaker additionally notes that the chip layout is likely to be the same as Wildcat Lake, with the efficiency cores in their own island. According to Jaykihn's table, the expected TDPs range from 6 W to 15 W, within the ballpark of phone and tablet SoCs on the low side.

The specs seem to imply that Moon Lake is the successor for the Intel N-series chips present in a great deal of ultra-budget offerings. The set of specs suggests the chip will find a home in laptops and mini-PCs in the $300 to $500 range, though those amounts are naturally variable depending on how much RAM and storage the AI world will continue to slurp. Furthermore, the new silicon is only slated to arrive in 2028, and supplies of NAND and DDR may well shift further until then.

There's no word on clock speeds, though. As a reference point, the N150 had a 3.6 GHz turbo. This year's 18A Panther and Wildcat chips broadly have clock speeds matching whichever silicon they've replaced. They do seem to be both faster and more power-efficient.

Some of the newer models pack extra cores, but Moon Lake keeps the same 4-LPE count and 6 W TDP as matching N-series designs. In turn, we speculate that while some performance gains are expected thanks to the new architecture's higher IPC, machines with Moon Lake inside will likely offer markedly improved battery life compared to their predecessors.

In a world where youth unemployment is growing as AI replaces more and more entry-level jobs, Cisco president and chief product officer Jeetu Patel is going for a different approach. According to a report by Fortune, he's given a 27-year-old ex-startup founder — brought into the company by an earlier acquisition, and previously languishing in a role five levels below — a new job to refactor 98% of the company's legacy code within 24 months.

The employee, Padmanabhan Krishnamurthy, joined Cisco after it acquired EzDubs, a translation startup that Krishnamurthy helped build in 2023. While having executive experience, Patel found that "Paddy" was left "buried away in the organization doing something unstrategic."

According to the report, Patel spoke to Krishnamurthy in Cisco's office in San Francisco, where Krishnamurthy told him that he could rewrite Cisco's legacy code in a team of just two people, and that "even the most AI-forward people on [Patel's] own team weren't thinking that way."

As a result, he's now been tasked with rewriting 98% of Cisco's legacy code within 24 months, with four weeks as a proof of concept. "I've given him unlimited tokens because he's going to refactor code," according to Patel, telling him "you can go nuts."

AI coding has skyrocketed in recent months, with GitHub forced to deploy 4 million extra CPU cores and 160 petabytes of storage to cope. However, the practice is not without its pitfalls, with rogue AI agents notorious for taking unsolicited actions including deleting entire company databases, TV show edits, and production setups.

Patel hopes that there are more young hires in the Krishnamurthy mould that he can find, both in the company and externally to hire. He's looking for hires to "challenge the system and the status quo," to be "an energizer bunny that just goes and kind of revs everyone up."

Patel believes that current corporate structures work against this kind of thinking. "If you don't hire the early-in-career [workers], and you just let your workforce continue the way that they were, where do fresh ideas get injected? How do you challenge the status quo?"

He acknowledges that this will create friction, putting Gen Z employees among decades-old employees. "Tension is good.... I actually try to create tension in the teams because you want to have enough friction on the ideas. You don't want to have friction with the people and personalities."

Patel believes that a combination of experienced, scale operators at Cisco, alongside external hires and "entrepreneurial founders, CEOs and engineers who are AI-native" will bring results. "Either of these groups by themselves aren't nearly as potent as when all three work together. It's what creates magic."

Bastian Recha, “Unskilledcast,” is a German former CS:GO commentator, tech reviewer, and 3D printing enthusiast. On October 28, he posted a demo of his “Mausstudio” project on X that generates a customizable mouse shell around an imported 3D model of a mouse PCB intended for 3D printing — all running locally and not generated using AI.

Bastian posted on X after six days of inactivity, stating that he’s been busy working on Mausstudio, his custom 3D-printed mouse shell generator. His post shows a video of him demonstrating the software, playing around with the ways users can customize their mouse.

Mausstudio works by importing an STL 3D file of a mouse PCB, like the Razer Viper V2 Pro and Zaunkoenig M2K PCBs in Bastian’s demo, from which the software detects the location of several key components: Mouse button switch locations, sensor location, scroll wheel location, and screw hole locations. It then generates a shell around those key components, placing stems to reach the switches, cutouts for the scroll wheel, buttons for the side switches, and base cutouts for the sensor.

Bastian’s demo also showed how users can adjust the width, length, and height of the mouse, including the location of the hump. Further customizations include weight-reducing cutouts for the upper and lower shell, with options to have a fully enclosed shell without the cutouts. To demonstrate the level of customizability, Bastian also showed a battery shelf option, allowing you to customize the location of the battery and the shelf’s size by inputting the battery’s dimensions — this feature, Bastian emphasizes, is lacking in many custom 3D-printed mice, and battery placement is treated as an afterthought, often just using hot glue or double-sided tape to adhere it.

Bastian also boasts that Mausstudio runs completely locally and without generative AI, just math and algorithms.

Bastian has not yet announced a release date for this project, nor has he announced whether this is paid or free, but he has posted an update on what has been changed since his demo, including being able to edit detected component assignments, more side button control, saving shell designs to be used on other PCBs, and cleaner geometry. We’re not sure if it can handle split PCBs like on the G Pro X Superlight, but support for that may come in the future as well.

This project looks to be a great way for users to create a mouse that’s closer to their ideal shape; good gaming mice are not cheap, and it’s a costly endeavor to buy many mice just to try out various shapes. This project may be able to let users experiment with more shapes more affordably with their existing mouse without needing CAD skills or hoping that someone has designed a mouse shell for their PCB that fits their needs. We will have to see how good the software is when it releases, and how it will improve and mature with future updates.

Nvidia’s Vera CPU is an important next step in the company’s years-long spearheading of AI infrastructure buildout. It’s arriving just as the shift to agentic AI workflows has created unprecedented demand for data center CPUs, and it’s designed specifically for this moment, at least according to Nvidia’s marketing bullet points. Although the idea that anyone developed a CPU for a shift in demand that arose just six months ago is reaching, as our benchmarks below show, Nvidia’s claims about Vera aren’t full of hot air.

Based on the workloads we have now — there aren’t standardized end-to-end agentic benchmarks focused on the CPU right now — Vera has strong indicators about its usefulness in agentic workflows. It offers chart-topping performance in Python, for instance, which makes up much of the backbone of the AI software ecosystem.

It also shows commanding memory bandwidth, both at the socket level and system level, with the high-speed C2C interconnect between Vera chips. In contentious agentic environments where agents may be querying large databases, that bandwidth is critical.

Specialization is one thing, but Vera is arriving in a hotly contested server market with custom Arm chips penetrating the data center and the old guard of x86 chips from AMD and Intel, which not only have a foundation in the data center, but also an extensive list of clients that work with their systems. Regardless of how Vera performs, that’s no small undertaking.

What’s surprising about Vera isn’t that it performs well in these agentic-adjacent workloads, but that it offers competent performance in general-purpose server workloads as well, often managing to match or beat the comparable AMD Turin and Intel Granite Rapids-AP chips. The scales are more balanced when looking at frontend workloads, with the core density available on x86 CPUs often the winner, but Vera trades that density for strong performance in backend, data-rich operations.

It’s an impressive showing for Nvidia's first custom core, called Olympus, and a stark improvement in nearly all aspects over Grace, which used an off-the-shelf Arm core design. The 88-core CPU is built on the Armv9.2-A architecture, including two threads per core through Nvidia’s “spatial multithreading.”

The chip is characterized by a 10-wide decode, similar to Arm’s AGI, as well as Nvidia’s second-gen Scalable Coherency Fabric (SCF). You can read more about the SCF and the Olympus core in our Vera deep dive, but SCF is critical to Vera’s performance. It’s a mesh that distributes L3 across the die, offering up to 3.4 TB/s of bandwidth across the die.

Vera has high memory bandwidth as well, leveraging replaceable SOCAMM2 LPDDR5X at 9600 MT/s, offering up to 1.2 TB/s of total memory bandwidth or around 14 GB/s per core, more than double that of AMD’s dense Turin designs.

We’ve heard a lot about the speeds and feeds of Vera, however. It’s time to understand its role in the modern AI data center, as well as see how it performs in our benchmarks against the x86 competition before AMD’s Venice and Intel’s Diamond Rapids start rolling out.

Understanding the CPU's role in agentic AI

Depending on where you’re at in the fast-moving world of agentic AI, you probably have a different definition of what exactly an agentic CPU is. Given the rise in prominence of agentic CPUs, the understanding of what makes a CPU tick for this type of workload has been largely defined by two companies: AMD and Nvidia. Although the points they’ve driven about concurrency and the varied work of agents are correct, it’s useful to derive a definition from how we’re seeing agentic platforms deployed right now.

Let’s back up from there and look at real agentic workflows in action to better understand where CPUs fit into this chain. As we’ve seen with Meta Muse and OpenAI Dots, there’s a class of persistent agent that gives you a slice of compute in the cloud. These CPU-only racks communicate with inference servers to actually run the models, but their workload is defined by concurrency. It’s not about the performance of an individual sandbox but instead how each sandbox performs when the chip is fully loaded.

Concurrency provides important context to some of the workloads companies like Nvidia and AMD have described. Some tool calls are more demanding, like code compilation and large database queries, but reading/writing files, running code tests, and going online in a headless browser is not demanding CPU work in isolation. When the chip is handling potentially thousands of these tool calls, however, slight advantages start to pile up. And, the CPU topology plays a bigger role in how performance is sustained as agent sessions increase.

That’s one major tenet of an agentic CPU, one that can maintain performance with high concurrency. The other is variability, which touches on the CPU, but also the larger infrastructure available. Mark Lohmeyer, VP/GM of AI and computing infrastructure at Google Cloud, described an example to Tom’s Hardware about planning a vacation with an agent. Using AI to plan a vacation is just about as trite as examples get, but Lohmeyer sheds some light on the unique challenge of agentic workloads from the hyperscaler perspective.

In the example, Lohmeyer described planning a vacation over the course of a month, where he would suddenly prompt the agent to do a bunch of work, but may not return to prompt again for another week. “The actual use of the underlying compute, it's happening in basically bursts, right? There's like a burst of use at the start, maybe for an hour, and then it goes away, and then a couple days later, [it] bursts again for some other purpose and goes away,” Lohmeyer said. “They're very long-running workloads, effectively long-running, but often idle, basically.”

Agentic calls for a lot of power on demand, as the right alignment of this bursty work on a server with hardware contention can bring everyone’s sessions to a crawl. This factors into the type of work the CPU is doing, as well, which can include heavy code compilation, graph traversal, and large database queries. Scale that up across several agents, all with their own independent loops that don’t neatly align with each other, and the CPU problem for agentic AI becomes clear.

In the August 2026 study, Architectural Implications of Agentic AI Workflows, from researchers at The University of Texas at Austin and Microsoft Azure, the paper describes three tasks of the CPU in an agentic flow. In addition to a scheduler for GPU inference, which is how CPUs function in one-shot-style LLM queries, CPUs also serve as orchestrators for maintaining agent states and communication, and as runners that call tools and execute agent logic (often in Python).

The paper also shows this contention that can happen when multiple agents are running at once, passing requests to the GPU, receiving responses, acting, and then passing requests back. We’re looking at a cross-section of four agents over less than half a minute, and you can already see how small CPU-based tasks can align, as well as create hangs between GPU time.

Nvidia has been clear that Vera is an agentic CPU, though defining the performance of an agentic loop is still a work in progress. “The industry built a pretty good way to benchmark traditional workloads,” Lohmeyer said. “I think this is happening now for GPUs and TPUs for inference, for example. I think we're going to need the same type of industry-level benchmarks for agentic workloads that are probably still emerging.”

Standardized benchmarks that look at the full agentic loop aren’t available yet. We have our own internal tests that we’re running and hope to have data for soon, and we’ve seen some vendor-level benchmarking from companies like Modal, but the broader industry hasn’t come forth with a set of benchmarks for the end-to-end agentic loop along the lines of a SPEC or TPC yet.

Although those tests aren’t available yet, we can still look at CPU performance through the lens of an agentic workflow, prioritizing aspects of performance that are important for agentic loops. We can also look at common workflows for agents, including code compilation for coding agents and database performance for retrieval agents. We can look at things like JSON parsing through the simdjson library, evaluating how these CPUs parse the large JSON files agents use to communicate. And we can look at the foundation of much of the AI ecosystem with Python performance.

That’s the focus of our benchmarking here, to establish where Vera stands among the x86 competition from AMD and Intel in well-known benchmarks. Hopefully we’ll have the opportunity for further testing in the near future to evaluate full agentic loops once that testing procedure is finalized.

Although we’re evaluating performance through an agentic lens here, Nvidia did not limit what tests we could run nor place any stipulations on our coverage. We’re thankful to Nvidia for providing us with remote access to a dual-socket Vera system for the purposes of testing, but beyond configuration checks to ensure the platform was operating as expected, we were left to run the tests we wanted to run. That includes several tests that are applicable to agentic workflows, but also tests from our general server CPU benchmarking suite, including Java, cryptography, compression, and rendering performance.

A gentle reminder for readers

Servers are not client PCs. You have much smarter operators with deeper access to the hardware, and often a software stack that’s unique to the specific workload at minimum, and highly tweaked and optimized for that workload at a maximum. With client CPUs, we can say that an AMD X3D chip is the best for gaming, or that Intel’s latest flagship offers the highest single-threaded performance. Servers depend much more on use cases.

In a typical review, we’d say that X is better than Y for Z purpose, and back that up with data. Here, that commentary is more muted. The reality is that hyperscalers maintain large fleets with a variety of hardware that can fit any purpose, and enterprises need to choose the hardware best suited not only for their operations, but also for TCO and a list of at least a dozen other variables that are specific to that business.

Nvidia Vera SPEC CPU 2026 estimates

So far, the primary benchmark we’ve had for Vera is SPEC CPU 2026 intrate, so we ran the full SPEC CPU 2026 suite to check Nvidia’s performance claims, as well as look at floating point performance. For anyone who hasn’t read SPEC CPU benchmarks before, there are a few disclaimers before heading into the results.

All of the runs here are estimates, and that’s due to how SPEC reporting works. There are strict reporting rules, and we’re not reporting these results to SPEC directly. The charts say “estimated,” but the numbers aren’t a guess. We ran SPEC CPU 2026 following the standard testing procedure, using the same compiler and flags for each CPU.

For the compiler, we used GCC 15.2 with a few common flags, including link-time optimization and march/mcpu to expose the correct instruction support to the compiler. We also used the general-purpose jemalloc memory allocator across all of the tests we ran.

Starting with the overall intrate, this is a series of applications primarily dealing with integer operations instead of floats, and the SOP is to run a copy of the application on each thread available. The test looks at what a CPU is capable of with many applications running at once, measuring throughput.

Nvidia’s numbers aren’t dissimilar from our own. In the company’s white paper, it showed Vera ahead of the EPYC 9755 by 3%, and with our tests, Vera is ahead by 1.5%. Keep in mind the considerably higher 512-thread count of the 9755 system here against the 352-thread Vera system. Vera absolutely runs away with performance when breaking down the score per-core.

Despite the core density of Intel’s 512-thread Xeon 6980P in a two-socket configuration, it’s not in the conversation here. Retesting with oneAPI represented an impressive 10% increase in performance with Intel-optimized binaries, but even against optimized code, Vera is 8.6% ahead with generic GCC.

There are some tests where Vera steals the show in the above album, as well as some where it falls behind, reflective of the testing we have later in this article. You can see that GCC compiler performance and Python performance are particularly strong points, with Nvidia even beating out AMD’s top-of-stack EPYC 9965.

Elsewhere, there are some weaknesses. SEAL crypto provides a look at the weaker cryptography performance of Vera, which is reflected in our other testing. Zstd compression also shows Vera trailing AMD and Intel, which makes sense. In our separate Zstd testing, Vera offered impressive compression performance with Zstd but fell behind on decompression.

We were curious about float performance on Vera given that Nvidia led with the standard intrate suite in its initial benchmarks. Vera tops the charts here on the back of some particularly strong showings in some of the tests. Our estimated PALM numbers, for instance, show off Vera’s second-generation Scalable Coherency Fabric (SCF), while ROMS pushes on memory bandwidth. This strong memory performance shows up in Marian and LBM, as well. There are a couple of misses, notably FemFlow and CACTUS, but the overall performance picture is clear.

The throughput benchmarks are what matter here for chips in this class, but we also ran SPEC CPU 2026’s intspeed suite for some estimated results, scaling up to use every thread available. No one is realistically running a single application on servers utilizing every thread available like this, but the numbers are there to browse if you’re interested.

Nvidia Vera Python and AI benchmarks

If there’s any direct benchmark for agentic AI right now, it’s Python performance. There isn’t one universal language for AI agents, but Python (and, to a lesser extent, TypeScript) make up the foundational agentic frameworks like LangGraph, CrewAI, and AutoGen. A strong agentic CPU right now is defined in part by strong Python performance, and Vera has exactly that.

Starting with a broad overview, you can see the total average test times for PyBench above, with Vera leading the pack. It’s 4% ahead of the EPYC 9755, which itself leads Intel’s Xeons and the larger 9965. Python is largely single-threaded, especially for these basic, built-in functions, so we see the 9755 pair leading the 9965 pair throughout all of our Python benchmarks.

PyPerformance provides a much more granular look at Python performance, evaluating start-up times for the Python interpreter, regular expressions, and even full Python applications. The story doesn’t change, however. Outside of a couple of misses in web server-focused tests, Vera tops the charts across the board.

The AI ecosystem right now is largely built on Python, so strong performance here is a good early indicator of strong AI performance in the data center. Of course, this space is rapidly evolving, and other languages are working their way into the ecosystem. For this moment, however, Python is critical, and it’s an aspect of performance Vera nails.

We also ran Llama.cpp with a CPU BLAS backend, checking text generation and prompt processing (at different token lengths) on the CPU using gpt-oss-20b-q8. For prompt processing, much of the process is handled on the GPU, so this pure-CPU benchmark isn’t totally representative of a real deployment. However, the CPU still plays a role here, particularly in tokenization.

Unsurprisingly, we can see the single 6980P topping the charts in prompt processing, with a 2P Xeon 6980P coming up in second place, likely on the back of Intel’s AI-specific ISA extensions. We expect AMD to further extend support for these instructions in Zen 6, which should level the playing field.

Nvidia Vera memory and compression benchmarks

Nvidia has touted the memory bandwidth of Vera as one of the chip’s strongest points, and the industry-standard STREAM memory bandwidth benchmark backs up those claims. Vera comes with a memory bus width more than twice as large as Grace, plus a large jump up to LPDDR5X-9600. It’s capable of up to 1.2 TB/s of memory bandwidth through the LPDDR5X interface, and up to 1.8 TB/s of bandwidth between Vera sockets.

AMD’s support for DDR5-6000 is relatively slow, and although Intel offers more bandwidth with MRDIMMs reaching up to 8,800 MT/s, Vera is in a league of its own here. That’s true without optimization, as well. After my initial STREAM pass, Nvidia suggested passing the OpenMP “spread” policy, which distributes processes as far away from each other as possible to reduce cache/memory contention.

With AMD and Intel, we see a slight regression with this setting, but it offers Vera a massive jump in memory bandwidth. Undoubtedly, the scales will start to level off as AMD and Intel roll out their next-gen Venice and Diamond Rapids parts, both of which tout much higher memory bandwidth than their predecessors.

Flipping over to the simple MBW bandwidth benchmark, we can see similarly strong performance from Vera, regardless of copy size.

The classic CacheBench benchmark flips the script, with Vera trailing the pack in the combined read / modify / write test. This weaker cache performance here is largely overshadowed by the memory bandwidth. The performance level is slightly below Nvidia’s contemporaries, but not by enough to make a significant difference.

Vera loses some points in compression workloads, which we see echoed in our SPEC runs in dedicated benchmarks. Using Zstd, Vera offers chart-topping compression performance at the default level 3 compression, as well as the maximum level 19 compression, across both standard and Long Mode runs. However, in decompression, the Turin chips take over, with Vera sitting alongside or below a dual 6980P configuration.

7-Zip traditionally favors Zen, so it’s no surprise to see both Turin configurations at the top of the charts. Vera loses out to the dual 6980P system in both compression and decompression performance via 7-Zip.

Nvidia Vera Java, database, and crypto benchmarks

Python is the language of choice for the AI ecosystem right now, but JavaScript/TypeScript are a close second. We ran three standard Java suites to evaluate performance: Java SciMark, DaCapo, and Renaissance.

Java SciMark is the best place to start. AMD wins out with the Zen 5-based 9755 in the composite score, but Vera maintains surprisingly strong positions in straight scientific operations, including FFTs, sparse matrix multiplies, and a Monte Carlo simulation. It falls behind in dense LU matrix factorization and Jacobi SOR.

DaCapo and Renaissance are both Java suites with a wide variety of real Java workloads, focused on native performance in DaCapo and VMs in Renaissance.

Database performance for Vera is strong across our ClickHouse and SQLite benchmarks. In max-thread SQLite, Vera is only beaten by a single-socket 6980P server, and it jumps into the lead in our like-for-like 64-thread and 128-thread tests. ClickHouse provides some interesting data that goes beyond a steady-state SQL database.

On the second and third runs of ClickHouse, Vera is competitive with a dual EPYC 9755 server, but from a cold cache, Vera is much faster, racking up an advantage of nearly 40% in the first run.

For bringing that data in, we measured JSON parsing speed with simdjson, which, as the name suggests, relies heavily on modern SIMD instructions. Turin has a natural lead here with its native AVX-512 execution.

In cryptography workloads, Vera falls behind the pack significantly in the John The Ripper password cracker, while trailing the massive core arrays of its competition in OpenSSL.

Nvidia Vera HPC and compilation benchmarks

HPC is usually the domain of dense CPUs, but Vera performs well in a variety of HPC tasks that we tested. In Discrete Fourier Transforms (DFT) using FFTE, Vera tops the charts, as it does in Octave and CloverLeaf. Although not strictly HPC, Vera falls in the middle of the pack in intensive mathematical operations like m-queens, and it trails chips with massive core arrays in the heavily threaded Primesieve.

Code compilation is a more contentious workload, with Vera trading blows against AMD and Intel depending on the workload. Starting with the compilers themselves, Vera takes a strong lead in a timed GCC compilation, beating out a dual 9755 system by 12%. It falls into last place in our LLVM compilation tests, however, both using standard Makefiles and Ninja. These tests measure the time it takes to compile the compiler itself, not the time it takes to compile an application with it.

Elsewhere, Vera shows up strongly in ImageMagick and PHP compiles while losing out slightly to the dual 9755s in Godot. Compiler performance is important to agentic AI given the prominence of coding agents, which often need to build software from source as part of an agentic coding workflow.

Nvidia Vera encoding and rendering benchmarks

Encoding and rendering workloads aren’t the focus of Vera, but we ran our standard list of benchmarks, which includes some rendering and encoding tests. Vera has strong performance here in some applications, though it certainly takes a backseat to the headline Python and memory performance shared earlier in this article.

The fight is between AMD and Nvidia here, with the 9755 and its full Zen 5 cores topping the charts in our AV1 encoding tests. With x265, Vera comes out on top, though by a margin lower than the 25%+ we can see the 9755s hold with AV1.

We normally run Blender, though there isn’t an official Arm Linux version available, so we skipped the test here. Instead, we have the cross-platform C-Ray and POV-Ray. Vera manages to outclass even the 768 threads available on a 2P EPYC 9965 server, both at 1080p and 4K. Vera also leads in POV-Ray, though on a technicality. Every server we tested was tightly coupled short of the 2P 6980P server.

For a more granular look, we have OSPRay, where Vera tops the charts in real-time path tracing, though falls short of the AMD and Intel flagships in real-time scientific visualization. The “dim_512” subset is worth drawing attention to. Strong AVX-512 performance allows AMD to offer three or even four times the performance of Vera in this workload.

Nvidia makes a delayed debut

Although Vera isn’t Nvidia’s first data center CPU, it’s the first one that the company is aggressively pursuing with the same fervor that it carries with its accelerators. It’s an impressive chip, often punching far above its weight class, especially in those workloads that can show off its unique capabilities and topology.

Still, it’s a single 88-core chip arriving in a market characterized by granular SKU lists to meet a wide range of specific workloads. Vera won’t be the right choice for all of them, which is something that Nvidia itself has already stated. “The world is not going to be served by one SKU of CPU. And that's not our intention,” said Nvidia’s Ian Buck when Vera was revealed in March.

That’s not the intention with Vera, but the performance against contemporary x86 CPUs shows that Nvidia’s new push into standalone data center CPUs has legs, more so than the various building-block-style Arm chips we’ve seen from hyperscalers, certainly. If nothing else, Vera demands some attention from AMD and Intel.

The x86 crew is responding shortly, with Zen 6 queued up to roll out through the end of the year and Intel’s Diamond Rapids targeting a 2027 release. As you can imagine, we'll be putting those chips through their paces as well.

MORE: CPU Benchmark Hierarchy

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A criminal investigation into the environmental damage caused by Google’s Finnish subsidiary has been launched. Tuike Finland Oy is being investigated after five complaints were received regarding the Muhos data center project, according to an official police statement published today [machine translation].

The police say that the investigation is backed by Finland’s Licensing and Supervision Authority (LVV). Google/Tuike Finland Oy is accused of suspected breaches of environmental law and environmental damage. Other lesser criminal offenses related to ambiguities and shortcomings in the drainage and water management are also being investigated.

Since Google representatives have previously publicly apologized for the unauthorized forest clearings near Muhos, which have turned 816 acres of forest into an expanse of mud, there seems little doubt it is guilty of some wrongdoing. Rather, it is the scale of the punishment and any proposed remedies that will probably be of most concern to execs from the tech giant now.

As well as the awful mess Google has made near Muhos, it seems to have also jumped the gun and plowed ahead with data center development by clearing 500 acres of forest near Otanmäki.

Though the horse has already bolted, Reuters reports that a Google rep said the company would cooperate with the ⁠police investigation and continue to focus on maintaining compliance at its sites. Maintaining?

Last week we reported that wolves had descended on Finnish villages after their habitats had been destroyed to make way for the Google AI data centers. Data center developments were already a thorny issue with locals. Potential construction and operating noise, pollution, utility competition, and environmental damage concerns were overshadowed – for some – by the presence of wild animals that had moved out of the forest.

Google sees Finland as an attractive location for data center development with its cool climate, affordable power, and government endorsement of AI projects. It has also benefitted from being able to develop in low population density areas and from a populace with a low propensity for protest. However, Google is testing the Finnish public with its trampling of the environment. The wolves, pollution, potentially 40% higher electricity bills, and other impacts weren’t helping its case, even before this criminal investigation.

Finnish government representatives have yet to comment on the case.

From hereon, this month shall be called SpookITober. In yet another high-profile cybersecurity incident this month, Binance.com, the world's largest cryptocurrency exchange, has been hacked, to the tune of $5.6 billion taken in various cryptocurrencies. As of this writing, withdrawals at the exchange are suspended until the company sorts out the situation, though it has stated repeatedly that "user funds remain safe," adding that it "will cover all losses." The U.S.-specific Binance.us website is so far reportedly unaffected. At least two security agencies, including TRM, have pinned the attack on an elite North Korean hacking unit.

"Our initial investigation suggested that the security incident may have involved a compromised laptop, however our subsequent investigation has uncovered additional points of breach," the company said in an update on Friday. "This was a complex attack, and we are doing all we can in coordination with law enforcement to resolve it. We are still gathering the facts, and will share more directly as we have them."

"TRM attributes this attack to TraderTraitor, North Korea’s premier hacking unit," the security firm said. "We base this assessment on robust on and off-chain intelligence sources," continuing to note that the group has been named responsible for a $1.5 billion Bybit hack last year. Per TRM, this latest instance bears many of the same hallmarks as earlier North Korean heists. According to CNN, an unnamed analyst at Pangolin Intelligence corroborated this story by "Analyzing the on-chain activity related to this hack and correlating with information gathered from previous known incidents [involving the North Korean cyber unit]."

Preliminary info breaks down the stolen amounts like so: $1.57b in Tether dollars (USDT), $1.45b in Bitcoin (BTC), $875m in Ethereum (ETH), $823m in Circle dollars (USDC), $234m in Tron (TRX), $88.5m in Ripple dollars (RLUSD), $75M in Wrapped-Beacon Ethereum (WBETH), $50m in Tether Gold (XAUT), and $447.5 million in other cryptocurrencies. The "dollar" currencies are all stablecoins, meaning they're pegged to $1 to represent dollars in the virtual space. Meanwhile, Tether Gold is pegged to the value of one troy ounce.

The stablecoin issuers all moved to blacklist the thief's main wallet (you can see it here on the Ethereum blockchain) in their smart contracts, a move that means the amounts in USDT, USDC, and RLUSD are frozen in place, totaling around $2.4 billion. It's possible, but not guaranteed, that the gold-pegged XAUT will also be frozen.

Note that this does not mean those funds have been recovered — the thief simply cannot do anything with those tokens, be it moving or exchanging. Normally in these situations, restitution from the frozen funds happens only after complainants provide proof of ownership, something that usually means copious time spent in courts.

The situation is a lot more grim for the amounts in pure cryptocurrencies like Bitcoin and Ethereum, among others. Since there is no governing entity for their blockchains, the most likely prospect is that the remaining $3.2 billion or so is forever lost in the ether — though some networks incorporate clawback mechanisms. Exchanges may block the thief when he eventually tries to cash out his virtual dough, but historically this hasn't been a reliable means of recovery.

Note that this is a developing story, so the tallies are subject to change. Should that amount be final, this heist would exceed the $1.5b stolen from Bybit in February 2025, of which only a paltry portion has so far been recovered. There's little info so far on the miscreant's means of larceny. Apparently, the Binance 14 , Binance 16, and Hot Wallet 34 hot wallets, which hold pooled customer funds, were drained. It's currently not known if this happened via stolen private keys, a compromised signing process, or another type of intrusion.

Binance has assured users that their funds are safe, and most importantly, that it will cover all losses. For what it's worth, the company has a means of insurance and a reasonable track record in these situations, though the amount of bullion pilfered today is exorbitant.

The company created its SAFU reserve fund in 2018, where it deposits 10% of its trading fees to cover any losses incurred from security issues. That fund currently sits at 15,000 BTC in it (verifiable here), or about $1.17b at the current BTC-USD price of $77,858. SAFU is a cold wallet, meaning its private keys are stored offline.

Back in May 2019, thieves made out with 7,000 BTC from Binance, amounting to around $40 million at the time. The company covered the entirety of the losses with SAFU funds.

However, while $1.17b is certainly a tidy sum, 'tis but a morsel of what currently looks like a multi-billion loss. Binance co-founder, shareholder, and former CEO Changpeng Zhao (CZ) stated in a tweet that he too expects that users will be made whole.

As a final note, Binance is alerting everyone to be on the lookout for contact attempts impersonating the company and/or referencing the current situation. Expect a flood of emails, SMS, and phone scams in the coming months. Be especially wary of any "compensation" or "withdrawal restoration" offers, and never click any e-mail links or otherwise offer any account information — always use the official website or applications yourself directly.

South Korean authorities have identified 80 Tesla drivers who allegedly forced Full Self-Driving (FSD) onto cars that were never cleared for it, Electrek reports. The Seoul Metropolitan Police Agency says it confiscated 21 of the devices used to switch the feature on. The unlock modules reportedly connect to the car’s CAN bus (the internal network that links its electronic control units) and get around the regional software lock that keeps FSD disabled. That works because Tesla builds the same computer into every car, whether or not the feature is approved locally.

Some suspects bought ready-made units from Europe and China, and one reportedly plugged a European-made module into his own Tesla. Others skipped the shopping. One suspect, an IT worker, built his device using information he found on GitHub, which suggests the barrier to entry for this kind of modification is lower than it sounds. The group also traded tips online, including how to stop the car from keeping any record that FSD had been activated. Concealing the activation history is likely an attempt to avoid detection by Tesla, which has been looking for these devices itself.

Then there is the legal risk, where prosecutors are using the Motor Vehicle Management Act, which bans unauthorized software changes that could affect vehicle safety. In Korea, the maximum penalty is two years in prison or a fine of up to 20 million won, or roughly $15,000, a steep price for a driver-assistance feature that many of these owners had already paid for.

South Korea’s transport ministry first flagged suspicious activations to police in April. “Safety risks were confirmed, including the possibility that FSD may fail to properly recognize driving conditions or operate differently from the driver’s intentions,” a police official said then.

Notably, the root of the problem is a certification gap. Tesla began offering FSD (Supervised) in South Korea in late 2025, but only on US-built Model S, Model X, and Cybertruck units, which skip local safety certification under the Korea-US free trade agreement. The Model 3 and Model Y arrive from Giga Shanghai and have yet to pass. Nobody agrees on how many Korean Teslas are eligible, although Carisyou Data Lab figures cited by MK last month indicate 47,458 of 229,311 cars (20.7%).

The South Korean investigation follows Tesla’s own efforts to stop unauthorized activation. In April, the company began remotely detecting unlock devices and disabling FSD access on affected vehicles. It has also warned owners that unauthorized modifications could void warranty coverage and leave them liable for crashes. Moreover, the restrictions are not the first time Tesla’s software protections have come under scrutiny. In August 2023, researchers exploited a hardware security flaw in the AMD processor powering Tesla’s media control unit to unlock certain paid features. The exploit targeted the car’s AMD-based system-on-chip, although researchers did not confirm that every premium feature could be unlocked.

Tesla has also faced regulatory scrutiny over software restrictions. In December 2021, the US National Highway Traffic Safety Administration investigated the company after an over-the-air update allowed drivers to play certain games while their vehicles were moving. The case highlighted how software changes can raise safety concerns when features are accessible under conditions regulators consider unsafe. Hardware reliability is another challenge for Tesla. Last year, the company detailed how it identifies defective processor cores in its Dojo AI supercomputers, where a single faulty component can compromise lengthy training runs.

The explosion at Intel's Fab 28 near Kiryat Gat, Israel, that happened earlier this month forced Intel to shut down the whole facility for two weeks, and now the company expects that it will cost it approximately $600 million in lost revenue over two quarters. The disruption is expected to affect server processors manufactured using Intel's 10nm-class process technologies and will reduce the company's shipments in both the fourth quarter of 2026 and the first quarter of 2027.

"In Israel, we have been shut down for about two weeks," said Dave Zinsner, chief financial officer of Intel, during the company's earnings call. "We feel like we are going to be starting up production here by the end of this week."

The explosion occurred in mid-October, killed two, seriously injured one, and caused an extended shutdown of Fab 28, one of Intel's key manufacturing facilities for chips produced using its 10nm-class and Intel 7 process technologies (Intel 7 was formerly called 10nm Enhanced SuperFin). Intel expects the incident to reduce its Q4 2026 revenue by approximately $300 million, and a similar impact is anticipated in Q1 2027. The company also expects the accident to reduce its fourth-quarter non-GAAP gross margin by approximately 2%.

Intel has not identified the affected Xeon processor families, disclosed the number of wafers damaged, or specified when Fab 28 will return to full production capacity after it restarts this Friday.

Although Intel's latest Xeon 6 processors use compute dies produced on Intel 3 fabrication technology, they still use I/O dies fabricated on Intel 7 (aka 10nm ESF) fabrication process. Furthermore, Intel continues to sell previous-generation Xeon 5 Emerald Rapids (and even Xeon 4 'Sapphire Rapids', though it stopped accepting orders on these CPUs in late September) that are made on Intel 7 manufacturing technology.

While Intel has plenty of 10nm-capable fabs — including Fab 12, Fab 22, Fab 32, Fab 42 in Arizona as well as Fab 28 in Israel — the disruption comes as Intel struggles to satisfy strong demand for server processors, so Intel cannot completely offset the lack of output at one facility by adjusting manufacturing at the others (i.e., it has done everything it could by now). For sure, Intel will not repurpose product lines it uses to make I/O dies for Xeon 6 to make previous-generation CPUs. In addition, other fabs may lack product-specific manufacturing qualifications, or the time and costs required to transfer production between facilities are too high, keeping in mind that Fab 28 is about to restart.

In addition to lost production time, Intel faces potential losses from wafers that were undergoing fabrication when the accident occurred. The company is assessing work-in-progress inventory to determine which wafers can be recovered and which must be discarded and written off. Zinsner expects most wafer write-offs to occur in Q4 2026, which explains why the gross-margin impact will be concentrated in that quarter despite the disruption to shipments extending into early 2027.

"Please note almost all of the $300 impact from Fab 28 is related to 10nm server products," Zinsner said.

Although Intel stresses that the shutdown primarily affects its 10nm-class server processors, the company has not disclosed whether the incident will also disrupt shipments of client processors manufactured using its Intel 7 (10nm ESF) process technology. Intel uses this node to manufacture a wide range of client CPUs, including its 12^th, 13^th, and 14^th Generation Core processors, such as Raptor Lake. However, it is unclear whether Fab 28 manufactures any of these products and, if so, whether Intel can compensate for the loss of production by increasing output at its other Intel 7-capable facilities. Consequently, the shutdown could potentially affect the availability of some consumer processors as well.

Intel's next-gen Nova Lake desktop processors will be the first client product to use Intel's enhanced 18A-P node, CEO Lip-Bu Tan confirmed in the company's Q3 2026 earnings call. The chief executive also said that the company intends to share more about Nova Lake at CES in January. The company's chief financial officer shared that Intel intends to take Panther Lake capacity offline to build more Granite Rapids data center CPUs.

"Nova Lake, our first Intel 18A-P client product, is tracking ahead of schedule, with design wins expecting to be twice those of Panther Lake at launch," Tan said in his prepared remarks. "We look forward to sharing more at CES in January."

There has been a lot of speculation about what node Intel's Nova Lake CPUs will use, with the rumor mill ping-ponging between Intel 18A and TSMC's N2P. Current speculation says the chip will use 18A-P for the SoC tile while using TSMC's N2P for the compute tile. It seems unlikely that Intel would use its most advanced node for an SoC tile, though it's possible.

Regardless, Nova Lake chips will use 18A-P in some regard. Intel announced in June that 18A-P had entered risk production, and at Hot Chips in August, Intel revealed its next-gen Xeon 7 Diamond Rapids will be the company's first server products to use the enhanced node.

Compared to 18A, which is available in Panther Lake and Wildcat Lake products currently, 18A-P comes with a 9% improvement in speed at iso-power, or an 18% reduction in power at iso-speed. Intel has described 18A-P as a drop-in replacement for 18A, allowing designs built for 18A to instead leverage 18A-P without any extra engineering work.

For 18A products, Intel says its new Wildcat Lake products that have shown up in devices like the Framework Laptop 12 are increasingly popular. "Demand for our Wildcat Lake is outstripping supply," said Tan. Intel says that 18A products now make up a third of client CPU revenue, up from less than 5% in Q1.

It makes sense that those products would be popular given the current environment. Tan said that Intel's OEM partners are "aggressively managing inventory to match softer sell-through," which means buying less to match the demand in the market. Intel's David Zinsner said the company expects the company's client business to decline by "more than 20%" in the next quarter."

Zinsner warned of a further contraction in the PC market, describing a sustained decline in the size of the market over the next year, at least. "We expect the 2026 PC unit [Total Addressable Market] to decline approximately 12%, with second half units down more than 20%. We do not expect a sustained recovery until memory availability improves and pricing begins to normalize," Zinsner said. "For 2027, we're planning a PC unit TAM to decline roughly 10%."

Taking the comments together, Intel is saying that it expects the market itself to shrink significantly over the next year due to memory prices. It's not a situation of demand outstripping supply, at least not directly like the case of a component shortage. Rather, it's a pullback from direct customers, OEMs, and IT departments from spending, which is more significant.

Although Intel says the client PC market is shrinking, the company says that its long-term outlook remains positive. "We think next year, and in future years, we're a market-share-gain type of environment... we do think the PC is going to through a renaissance despite [memory shortage]," said Zinsner.

Despite the optimism, Zinsner said that Intel is taking some of its client capacity offline to build more data center products. In response to a question about reallocating wafers from client products, here's what the executive said: "Answer: Yes... What we're taking offline to put Intel 3 into Oregon is 18A wafers, which were generally going to build Panther Lake products, so we see the opportunity with some of that capacity freed up to go produce more data center wafers."

SpaceXSI, Elon Musk’s data center business powering Grok, is reportedly buying 418 properties in Colonial Hills, Southaven, which sits about half a mile from the powerplant that the company will use to power its data centers in the area. According to The Guardian, real estate agents have been going door to door, offering owners deals on behalf of the company provided that they do not join a class-action lawsuit over the noise pollution coming from the gas turbines that power its GPUs.

MZX, the SpaceXSI subsidiary that operates the Colossus II site, circulated an open letter to nearby residents, saying that it “requires property for its business purposes,” with Southaven mayor Darren Musselwhite saying that they will be used to house SpaceXSI staff that the company will need as it expands operations. Musk himself is reportedly staying in the area in an Airstream trailer four nights per week to personally oversee the data center build-out needed to power Grok AI.

The bigger issue here, though, is the requirement that sellers must sign an agreement that prevents them from joining the ongoing case against the company for noise pollution. The Colossus 1 and 2 sites have been in hot water since they started operating, primarily for their use of unpermitted turbines and the air pollution that they bring. SpaceXSI has since promised that it will remove all its unpermitted turbines in a year, especially as its 1.2-gigawatt powerplant comes online. However, this apparently hasn’t resolved the noise pollution issue that the community has been complaining about.

“It sounds like an airplane engine but it’s continuous, it’s incredibly loud. I’ve been woken up at 2am by it, I’ve been woken up at 3am by it. I sometimes have to just get away from it because it makes me so angry,” piano teacher Elizabeth Rose, who lives a couple of miles from the site, told the publication. One of the plaintiffs in the class-action, Jason Haley, also said to The Guardian, “There’s a high-pitched hum and then another sound like an airplane over your house. It’s been so torturous I can’t sleep; I just leave the house or turn on some music until I fall asleep completely exhausted. It drives you insane. You also lose hope because you’re being ignored or dismissed. There’s no escape.”

The company has denied all the allegations against it, saying that it has adjusted its power output and installed sound-dampening walls to reduce the impact on the neighborhood. Musselwhite also said that city monitors have not detected the site breaching the local ordinance limit of 70 decibels, although some experts say that persistent noise as low as 50 decibels could have adverse health impacts.

Opposition against the noise pollution and Musk’s purchases aren’t unanimous, though. Some residents aren’t as affected by the noise pollution, while local businesses are experiencing increased activity. Other people have also agreed to sell just so they could get away from the noise. The mayor says that the buyout isn’t intended to turn Colonial Hills into a company town. “The buyout has multiple purposes which are to provide homes for some employees and a goodwill gesture from a good corporate citizen for those who perceive the industrial development near them as negative,” Musselwhite said.

The AI industry continues to gobble up manufacturing resources that were once dedicated to serving PC, smart device, and gadget makers. We’ve seen huge demand/supply driven price rises in RAM, GPUs, SSDs, and more through 2026. Now that most basic and unglamorous electronics necessity, the PCB, looks set to join them. A news report published by Nikkei Asia quotes several leading PC and PCB industry managers, making it clear we are going to see PCB (and PCB subcomponent) shortages and price rises in the coming months.

An unnamed exec from an American PC maker spoke to Nikkei about the dire shortage of PCBs. “Even though PC shipments could decline again next year, we are still struggling to secure enough PCB supplies to meet our needs,” said the manager, who asked to remain anonymous. They were talking about securing enough supplies to make plans for products in the coming year. Several commenters pointed to AI computing demand eating up production capacity.

The outlet also talked to business people further up the supply chain – the makers of PCBs and the makers of the components/materials needed to actually make PCBs. Traditional PCB demand is becoming underserved as AI products demand and pay more. One PCB maker talking to Nikkei said that a significant number of products have lead times that have doubled. Specifically, some PCB orders will take as long as 30 months to fulfill. Another PCB supply firm explained to Nikkei that a new order today would not be fulfilled until late in 2028.

Though PCB makers are planning to invest and expand, they also need to secure multiple upstream materials. These can include copper clad laminates (CCLs) and the specialized fiberglass used for PCB construction.

One Taiwanese PC maker noted that costs for CCLs have surged at least 12 times since July. Moreover, one of the world’s leading suppliers of CCLs has told the Nikkei that it has an order book that is currently filling up to 2030. This situation appears to have occurred despite its plans to triple capacity by 2028.

In closely related developments, Nikkei observed that Nvidia recently announced partnerships with several major chip substrate makers. Chip substrates are essentially the tiny and intricate PCBs that CPUs and GPUs are mounted on. Japan's Ibiden, South Korea's Samsung Electro-Mechanics, and Unimicron of Taiwan all have new agreements with the Green team. Unimicron is looking at expanding capacity by 2.5x over the next three years.

PCBs have been viewed as an unglamorous lower-tech part of the supply chain, partly due to the small numbers on the BoM. That may change. Companies are now racing to ramp up production of advanced PCBs and chip substrates to meet demand, and boost income.

Finally, the source report also notes that it is increasingly difficult to get the engineering staff required at new production facilities. Salaries have already risen 20% to 30% across the PCB industry to keep skilled employees.

Concerns have been mounting everywhere about the environmental impact of data centers, with alarms ringing at various environmental think tanks. The Sierra Club, one of the oldest environmental preservation organizations in the U.S., has now put forth an estimate that by 2030, U.S. data center buildouts will be outputting as much carbon dioxide as the entirety of Mexico, pushing back progress on greenhouse gas reduction by 15 years, Bloomberg reports.

The Club's principal advisor for climate and energy, Jeremy Fusher, puts it plainly: "the numbers continue to sort of boggle our minds." He's referring to an estimate that with the output from pending data center builds, the output from the U.S. power sector is set to put 2.1 billion metric tons of CO2 in the atmosphere by the end of the decade, an uptick of 42% compared to 2025 figures. The researchers' previous estimates were between a quarter and half of the current projections.

An increasing number of countries and states are enacting laws demanding that new data center builds have a large portion of their power generated by green sources. However, those laws rarely apply retroactively, and oftentimes have a phase-in period. Even the Irish law on the matter demanding that new data centers be fed by 80% of renewable power only requires that that happens six years after they're powered up.

In the current AI-driven market, speed of construction is king. Roughly 40% of U.S. data centers are fed by natural gas turbines, and the situation doesn't look like it'll be changing much. It'll likely get worse, as the addition of green sources almost assuredly won't keep up with demand. Much like RAM, GPUs, and storage, turbines were all slurped up by data centers. Their prices have risen by 50%, and production is backlogged for years, with one maker stating it's opening slots for production in 2032.

Having said all that, Brendan Pierpont from Energy Innovation, another climate-concerned organization, points out that although the current path looks dangerous for the environment, it's hard to make a hard prediction of how the situation will play out. He justifies his position with common data center roadblocks: the availability of turbines, delays in grid connections, and other delay factors.

Truth be told, at this point it's hard to predict how many data centers will actually reach completion, given that requests for permits far exceed available grid capacity across the U.S., and a sizable number of buildouts are encountering strong opposition. Plus, although the AI bubble is still alive and well, concerns started to arise about the economics of hyperscalers, indicating some may go the way of the dodo — and their projects along with them.

The Nvidia GeForce RTX 5080, Nvidia's second-fastest GPU from its current generation, now costs as much as the RTX 5090 did, on paper, when it launched. Prices have reached $2,000 and higher, with most of the available stock online limited to third-party sellers. If you can find it in stock, it may end up costing you two to three times as much as it did when it launched.

The cheapest, in-stock RTX 5080 available to buy from a reputable seller online for delivery is this PNY RTX 5080 for $1,999 from Best Buy, the same price as the RTX 5090's on-paper MSRP. You might otherwise end up paying $2,299.99, at least, for this Asus ROG Astral mode at Newegg, or you could pay nearly $3000 for this Gigabyte RTX 5080 from MemoryC, now priced at $2,983.23.

Stock for the RTX 5080 is otherwise limited everywhere, with just one or two available for local pickup (in Best Buy's case), and limited options for delivery. You can also find similar in-store-only pricing at Micro Center, depending on location, which has a Gigabyte card for $1,979.99.

You'll also see a lot of the listings online from sketchy, third-party sellers.. For instance, you could pay at least $2,211.17 for this PNY RTX 5080 from a third-party Amazon seller. Empowered PC, the seller for this card, has had 81% positive ratings over the last 12 months, with 202 ratings in total.

Reputable seller or not, this price scale means you're paying significantly more than the $999 launch price for this GPU. As our GPU price tracking shows, though, this isn't an isolated case. RTX 5080 prices have, like the rest of the RTX 50-series, risen steadily since it launched. However, this is the first time that its price floor has been around the RTX 5090's launch price. That PNY RTX 5080, for instance, was being sold for around $1,500 on average back in August.

It's a bitter pill for gamers, though, that the RTX 5080 has now reached and breached the RTX 5090's launch pricing. That GPU has an MSRP of $1,999, although it'll now cost you thousands more. Prices for the RTX 5090 hit as high as $9,500 last month, but the situation is even worse today. What little stock that remains on sale online can be found for as high as $12,000.

The RTX 5080 uses Nvidia's GB203 GPU and comes with 10,752 CUDA cores, along with 16GB of GDDR7 VRAM. Unfortunately, it, like the rest of the consumer gaming GPU market, is being affected by AI. Demand for high-end GPUs hasn't stopped, but Nvidia's attention has shifted to the enterprise market. So, too, has the focus of memory manufacturers, meaning DRAM allocations and production lines that would otherwise produce memory chips for graphics cards have instead gone to feeding the demand for high-margin HBM for AI data centers.

Multiple sources claim that a top executive at a Singapore AI firm suspected of steering Nvidia AI GPUs to China and who has close business ties with Jensen Huang was apparently passing information to Beijing as a Chinese intelligence asset. According to The New York Times, U.S. intelligence has been aware of her activities for over a year already and has even briefed President Donald Trump on the situation. However, Trump reportedly chose not to do anything about the information, as he apparently did not want to affect Nvidia sales or antagonize China.

The suspect, Alice Huang or Huang Le, was listed as the managing director of Singaporean AI firm Megaspeed, an Nvidia cloud partner that builds data centers and rents out the compute to Chinese firms like Alibaba. The investigation into the firm began when the U.S. Commerce Department visited the firm’s Malaysia data center and found the Nvidia equipment that it ordered still packed in boxes. This raised suspicion that the machines were either just shipped in or about to be shipped out, instead of being installed on the site. However, it remains unclear what made U.S. intelligence agencies investigate Ms. Huang. Megaspeed was accused of helping smuggle $2 billion worth of Nvidia chips last year.

There have been multiple instances where Ms. Huang was spotted in public with the Nvidia chief. She was photographed sitting beside Jensen Huang alongside other executives in an informal event around Computex 2024 in a LinkedIn post by NetThunder, identified as Megaspeed Chairwoman Alice Wong. About a year later, around the same time as Computex 2025, she was once again seen with Jensen Huang leaving a restaurant in Taipei alongside other business associates.

“Megaspeed has been no different than many other regional cloud customers. Jensen’s interactions with Ms. Huang are not unusual or unexpected. Meetings with customers are part of a CEO's job; Jensen talks with thousands of customer executives every year,” an Nvidia spokesperson told Tom's Hardware. “Our regional cloud customers receive ordinary business information — not sensitive NVIDIA trade secrets — and zero information relevant to national security." Still, the possibility that Chinese intelligence is targeting a senior figure close to President Trump is apparently troubling some U.S. officials.

The Chinese embassy in Washington denied the allegations, saying that it was merely “groundless smears and slanders” against China. Nevertheless, it would also make sense for Beijing to keep tabs on senior executives of the top American firms that arguably single-handedly fueled the AI boom. And given Jensen Huang’s status as a celebrity CEO, it’s not surprising that he is surrounded by all kinds of people from both the U.S. and China. Sources cited in the report claimed that Mr. Huang hasn’t yet been given a defensive briefing — a warning that U.S. counterintelligence agencies would sometimes give to American citizens who have been targeted by foreign intelligence services — even though the surveillance against Alice Huang continues. The report speculates it is possible this was done to avoid alerting Alice Huang that she was being investigated.

As for Jensen, U.S. officials do not believe that he is aware of Ms. Huang’s intelligence activities. "We're confident that if the U.S. government believes that any person is a threat to American industry, it would let us know," Nvidia told Tom’s Hardware. "We often receive updates from the U.S. government regarding parties and entities of concern and always act accordingly."

Nvidia is reportedly facing questions from the United States Department of Justice over whether its purchase of Hugging Face will have an impact on competition in the growing AI space. Nvidia agreed to buy Hugging Face in September, paying almost $13 billion. Hugging Face offers an open-source platform for the hosting and testing of new AI models, and that appears to be behind the DOJ's concerns. Reuters reports that the department has made inquiries about how the Hugging Face deal could change the landscape for Nvidia's competitors. The move could be seen as a way for Nvidia to lock in a key distribution channel for its own products. Hugging Face also sells AI infrastructure. Nvidia has declined to comment on the story.

This isn't the first time that Nvidia has found itself in the DOJ's crosshairs. As Nvidia becomes more vital to the tech industry as a whole and AI technologies specifically, the company has come under increasing scrutiny within the U.S. government, stretching back to the Biden administration. Things didn't improve when the Trump administration took over, with the U.S. president reportedly tempted to break the company up — despite being unfamiliar with what it was. More recently, Nvidia and other AI hardware makers found themselves under export controls. Nvidia's powerful AI-focused hardware can only be sold to approved entities, while it cannot ship to countries including China, Iran, North Korea, and Russia.

At home, the DOJ has also looked into Nvidia's purchase of SchedMD, a company whose software is used to run supercomputers that have ties to national security. Nvidia promised to continue to support the software as part of a larger three-year investigation into its conduct. Officials have also considered whether Nvidia dangled a larger allocation of its AI chips in front of customers who also purchased its networking hardware as well as Groq chips — Nvidia bought Groq for $20 billion in December of 2025. The deal included the acquihires of key members of the AI chip startup's team, including its CEO.

As for Hugging Face, Nvidia has similarly confirmed that it will continue to support a range of chip and AI providers once the buyout completes. It's expected that Hugging Face will officially become part of Nvidia in the first half of 2027.

Systems administrators are among the first to point out lax security policies in other companies' or people's setups. Sometimes, they are the ones who get bitten by not taking the same advice they will so readily dispense. Such is the tale of Reddit user NakamuraHwang, who had files on their Synology DS918+ NAS encrypted by a ransomware attack after making a series of unforced errors.

Hwang appears to be quite technically proficient and provided a detailed write-up of the situation. The key points, however, are that the unit in question (a) had its web interface exposed to the internet and (b) was roughly 18 months out-of-date with security patches. Just like the recent PHP snafu has adeptly shown, complacency is a far worse enemy than an army of AI bots.

The tech has a 3-2-1 backup setup, so they seemingly didn't lose any data. They attached the disks to a Proxmox machine for further inspection — Synology's DSM operating system uses mdraid underneath, so you can take the disks to another box and mount them there. Investigation of logs and the operating system files revealed that the attack was likely performed by abusing the CVE-2024-10441 remote code execution vulnerability, revealed a year and a half ago.

As most any other sysadmin will tell you, it's never advisable to expose devices directly to the internet without some layer of protection in between, chiefly but not only a VPN or an SSH tunnel. Tailscale, for instance, makes having a VPN fairly painless these days, and its free plan covers most home installations.

Synology had published its own advisory regarding the exploit, but that's where the NAS' behavior regarding updates complicates matters. According to multiple comments on the thread and in other forums, the UI will happily tell you that your device is up to date, all while there are critical security updates that need to be manually installed. The manual for DSM indicates that units from 2025 onwards have automatic updates, apparently implying that those of previous makes need to be configured so.

Furthermore, Synology apparently doesn't have a direct channel to warn its customers of critical security vulnerabilities —a simple e-mail notification would suffice. Although the firm is seemingly shifting away from consumer gear, it still sells small units that regular users might buy, and it's not reasonable to expect them to even be aware of security bulletins. Plus, package updates like the patch for the PHP2Shell vulnerability have to be installed separately, too.

Fujifilm plans to boost production of magnetic tape by as much as 5x as it works to keep up with demand for the data storage medium that has seen a jump in popularity amid ongoing global AI data center buildouts. The Japanese firm will spend 10 billion yen ($63.3 million) to increase capacity at its Odawara plant throughout its fiscal year 2028. The move will enable it to produce more advanced tapes with capacities of up to 100TB each, Nikkei Asia reports. The expansion of production capacity will mark the company's largest investment in tape-based storage in around 20 years. Fujifilm first began working on magnetic tape technology back in 1954 when it was used for television broadcasting equipment.

Nowadays, magnetic tape is the go-to storage option for data centers everywhere as a form of long-term data backup. While tapes lag behind solid-state drives and traditional spinning hard disks in terms of performance, tapes are hard to beat when it comes to their durability and relatively low cost for storing huge amounts of data. Magnetic tape storage can cost as little as one-quarter that of hard disks, and even less when compared to faster SSDs. That makes them the perfect solution for so-called cold data, information that is rarely accessed but could be considered mission-critical and must be stored safely. Tape storage isn't just cheaper, either, with research showing that it's also less polluting than hard disks and SSDs.

While Fujifilm will produce 100TB tapes, the Linear Tape-Open (LTO) roadmap allows for considerably larger capacities, all the way to 365TB per cartridge. By comparison, SSDs top out at around 256TB and cost considerably more.

Fujifilm is far from the only storage company looking to take advantage of the AI data center boom. Toshiba announced earlier this month its plans to double hard disk production capacity amid shortages, and the company, alongside Seagate and WD, is working towards ever-increasing capacities. Kioxia and SanDisk are also getting in on the act after detailing 332-layer 3D NAND flash in August. The pair hope to bring higher storage capacities to SSDs for data center use.

The demand for AI data center storage is so high that it's at the heart of a global shortage that shows little sign of improving anytime soon. Scarcity has seen prices skyrocket, while manufacturers have chosen to divert resources away from consumer-focused products, instead focusing on the more lucrative enterprise market.

Malicious GitHub repositories masquerading as two well-known and legitimate PS5 game emulation tools have been identified as spreading malware that steals gamers' passwords. The malware uses self-signed certificates, created using fake company names and on the same day the malware was built — suggesting that the malware campaign may be automated. Just two minutes after infection, a cleanup script scrubs the affected computer of the malware's files, leaving no trace that it was ever present, according to a report shared with Tom's Hardware by NordVPN's Threat Intelligence research team.

The campaign trades on the back of the popularity of two projects that have become popular within the PS5 emulation community. AnyPS5 is a portability layer that allows PS5 games to run as native applications on both Windows and Linux. Kyttyps5 is an emulator for running PS5 games on PCs, and both projects are freely available for download via their respective GitHub repositories. However, users who visit one of the malicious repositories posing as these tools are directed to download a zip file containing a Windows executable. Notably, the GitHub repositories used as part of the malware campaign rapidly rotate, and it's possible that GitHub is not the only source.

The malicious executable unpacks a hidden application before launching its own multi-stage loader. It first checks whether the PC is a legitimate machine or a security researcher's sandbox by checking if less than 2GB of RAM is available, if it's a virtual machine, if it's running on a cloud-based server, or if it doesn't have a physical GPU installed. If any of those criteria are met, the loader exits silently. If the machine is deemed to be a legitimate target, the malware adds exclusion rules to Microsoft Defender to ensure that it will never be scanned and identified during an antivirus scan. Finally, the payload is decrypted and executed.

That payload, we're told, is an executable protected by a commercial packer that is believed to be an infostealer — a type of malware whose job it is to steal passwords as well as any other sensitive data that it comes across.

While this appears to be a relatively new malware attack, there are some steps that you can take to avoid falling foul of it. If you're trying to download tools like AnyPS5 and Kyttyps5, it's vital to ensure that the repository belongs to the official project team. Double-check the owner of the project, look for download links that use third-party destinations, and check metadata like the project and files' creation dates.

If you do find yourself in a position where you have accessed a suspected malicious download, disconnect your computer from the internet and run a full antivirus scan. It's also recommended that you change your passwords from a separate device where possible.

PS5 emulation is likely to become more popular than ever once GTA 6 is released on November 19, 2026. The game will be a console exclusive at launch, with a PC version expected to arrive in early 2027.

David Durman, a research administrator at the University of Kentucky (UK), has successfully argued that using AI violates his beliefs as a humanist and has received Title VII religious accommodation from university officials not to use the technology. According to the Religion News Service (RNS), Durman believes in "the human part of life," which includes making his own decisions. However, he says that when the UK asked its employees to use AI, he felt that doing so would mean outsourcing his thinking to tools like ChatGPT and Microsoft Copilot, thus violating his deeply held beliefs.

“The process and work involved in thinking through situations, through my work, through my ethics, through the way I handle other people, is a key component in being able to develop myself as a moral person,” Durman told RNS in an interview. “And offloading that labor, regardless of whether it’s taxing or not, is undercutting my ability to be a moral actor.” Because of this, the research administrator was granted a Title VII exemption under the Civil Rights Act of 1964, which prohibited employment discrimination based on race, color, religion, sex, and national origin. While humanism is often a non-religious philosophy or a worldview, federal courts have ruled that it qualifies as a religion.

The American Humanist Association (AHA) assisted Durman with his request for the accommodation. “This is the first of its kind at the AHA and for humanism generally to exempt those who see AI use as flying in the face of their freedom of conscience,” AHA staff attorney Alexandria Huiskens said to RNS. She also added, “For us, humanism is a comprehensive way of life. It’s built on inherent worth and dignity, and everyone should have room to exercise this comprehensive way of life at work the same way Christians do, or Muslims do.”

It should be noted that while anyone can request a Title VII exemption, it will only be granted if the applicant has a deep moral conviction that could count as an article of faith. Aside from that, the law also states that the request should not place an “undue hardship on the employer’s business.” So, even though Durman is no longer personally required to use AI tools for his tasks, he is still expected to attend meetings and cooperate with colleagues who use AI or to submit materials to AI tools, as needed.

AI use in corporate settings has been one of the biggest questions ever since LLMs exploded in popularity with ChatGPT. While AI proponents have said that the technology would increase productivity multiple times, reports say that corporate America, except for Big Tech, is not seeing any financial returns. Even so, about half of all U.S. employees now use AI one way or another at work, even as many executives remain uncertain about AI’s return on investment. But as for Durman, he says that the fact that Americans are already thinking about the impact of AI is a good thing, and that he hopes that his story would help others who’d rather not be forced to use the technology.

Is most programming code now made by AI? That's not hard to believe given the number of testimonies, but we now have exact figures behind that statement. GitHub, the world's largest code repository, says that over the course of this year, it added 4 million CPU cores, 160PB of storage, and 50 Tb/s of networking capacity to keep up with the influx of AI-generated code.

Raw repository activity saw a massive year-over-year increase, as per GitHub's technical report. Code commits (adding/removing code) grew sixfold, while merged pull requests (finalizing a feature or bugfix) jumped 4x.

The multipliers sound impressive enough, but the exponential growth pattern is even more so: half a year ago in April, there were already 1.4 billion commits per month — a figure that rose to 2.9 billion in August, and now sits at 7.4 billion for September. GitHub saw 17.9 billion code pushes in the past 12 months, which is more than all of 2024 and 2025 put together.

Predictably, the company says that agentic AI is behind the dizzying rise in the amount of code, and the need for it to both upgrade and rethink its technological infrastructure. By last September, over 40% of pull requests were started by an agent, up from 17% in March 2026. Moreover, repositories that kick off using AI generally see eight times the activity compared to handcrafted operations.

All this code activity puts disproportionate strain on CI/CD pipelines. In modern development, whenever someone commits code or merges a pending pull request, many actions are triggered: for example, checking for leftover "to-do" items, visually arranging code for consistency (linting), and running code and integration tests to check that the new code doesn't break existing functionality within the application and works correctly with associated programs, services, and APIs.

Triggering all of the above for each commit and/or pull request means that GitHub's hardware requirements have ballooned, now requiring massive amounts of computing power and I/O. The rise was so intense that the firm even had to reengineer its backend to try to decouple storage from compute as much as possible. Given how quickly demand is rising, GitHub is likely to have to upgrade sooner rather than later.

Flock's AI surveillance cameras are continuing to cause controversy, this time in Dayton, Minnesota, where it is alleged that the company reinstalled two cameras despite express orders from the city not to do so. The city's local government posted on Facebook to explain, claiming that the Police Department was made aware of a maintenance work order on September 17, to which Dayton responded the next day, stating the program had been put on hold and no work orders were to be completed. However, Flock went ahead and reinstalled them anyway without authorization from the city.

Dayton’s city council had already voted to suspend the automatic license plate reader program and Flock cameras on August 25, following community concerns over privacy and data misuse, The Drive reports. Flock is infamous nationwide, with instances of its cameras being used for unjust purposes, such as a car reviewer mistakenly being pulled over because one of its cameras misread the tags. In another case, Flock’s chief information security officer also falsely assured the Oshkosh City Council that Flock did not create a heat map of individual license plates, when it in fact does have a heat map feature in the Flock system.

Flock cameras in the city had already been broken and vandalized, leading police to disable and remove the cameras to prevent further damage on August 20, before the program’s suspension became official, local media reports. Despite the suspension, almost a full month later, on September 17, Flock submitted the offending work order, which led to this latest controversy.

We don’t know when Flock reinstalled the cameras, but Dayton citizens had already complained and reported spotting the two Flock cameras, leading to the September 29 Facebook post. The local government further stated that the Dayton Police Department had been informed and ordered to remove the cameras.

It is unclear what happened in the chain of communication from when the license plate reader program was suspended and canceled its contract to notify Flock to cease work orders; either Flock defiantly ignored the orders or instructions had not reached the people responsible for installing the cameras; either way, this could be another blow to Flock's reputation.

Apple finally has touchscreen laptops. At an event in Los Angeles, the company detailed its new MacBook Pros featuring OLED XDR displays that you can control with your fingers.

The 14-inch and 16-inch MacBook Pros are available for pre-order now and will ship on Nov. 6. The 14-incher will start at $2,899, while the 16-incher will start at $3,399. The systems use Apple's existing M5 Pro and M5 Max chips, and go up to 16TB of storage and 128GB of memory.

These systems also feature the first redesigns for the MacBook Pro since 2021, when the M1 Pro and M1 Max launched in what has become a familiar design with a notch. These new models include the Dynamic Island around the webcam, similar to the iPhone, which can be used for real-time notifications. They also have light-up Apple logos, which we haven't seen on a MacBook Pro since 2016.

The PROLED XDR display comes in 14.2-and 16.3-inch sizes, with ProMotion allowing for refresh rates between 10 Hz and 120 Hz. Apple claims 2,000 nits of peak brightness and a 10,000,000:1 contrast ratio for intensely deep blacks.

The 14-inch MacBook Pro is 3 pounds, about half a pound less than the existing version, while the 16-inch MacBook Pro is 4 pounds, or 0.7 pounds lighter than it was before. Apple's bezels are also less noticeable than before. The hinge seems to be a point of pride (that's the second time this month, after the iPhone Duo). In this case, it's that the screen is less prone to wobble when you touch it.

The new laptops are only slightly thinner than their predecessor. Apple claims that the 16-inch model is "nearly a millimeter thinner" and the 14-incher is "half a millimeter thinner." Apple attributes those shavings to OLED's display stack.

Apple's own apps are already set to work with touch out of the box, and the company says many other apps will already work right away, including Notion, Canva, Zoom, Microsoft Word, Excel, PowerPoint, Adobe Lightroom, DaVinci Resolve, Blender, and more.

Apple already had the best speakers in a laptop, and now it's promising improvements: 50% deeper bass and new tweeters for clearer sound. There's even dedicated audio tuning for people who want to use the speakers with their laptop lid closed.

The significant change to the Mac is another in a flurry of launches for new CEO John Ternus, who also recently introduced the iPhone 18 Pro; the foldable iPhone Duo; Apple Watch Series 12 and Ultra 4; HomeView; and a new iPad Mini, also with an OLED panel.

The devices follow a long resistance to touchscreen laptops from Apple. Former CEO Steve Jobs, at an event introducing Mac OS X Lion in 2010, called laptops with touchscreens "ergonomically terrible", claiming that "after an extended period of time, your arm wants to fall off." (HomeView, Apple's smart home manager, has a vertical touchscreen and no one has made a peep about that. The iPad, too, has been vertical on Apple's keyboards, though some of those include touchpads as well).

The Mac resisted a touchscreen throughout former CEO Tim Cook's tenure, as well, though these new laptops definitely were developed under his leadership. In the last decade, touchscreens have become a common feature on laptops running Windows, and Microsoft's latest operating system, Windows 11, was designed to be slightly more touch-friendly than its predecessors.

Malaysian customs have seized over $12 million (49.5 million Malaysian ringgit) worth of advanced AI chips in a single bust, as the country clamps down on a suspected semiconductor smuggling syndicate. According to a Reuters report on October 28, officials found 30 servers containing the chips at Kuala Lumpur International Airport’s free trade zone on October 15th. Initial investigations revealed that the shipment originated in an Asian country and was bound for another undisclosed Asian country.

The airport’s customs director, Aspalila Ag Tuah, confirmed that the servers were falsely declared as ordinary electronic goods, saying the suspected scheme used Malaysia as a transit point to avoid restrictions on the goods’ onward movement. The Malaysian company involved in the shipment has been summoned to assist in the investigation.

Authorities suspect that the foiled smuggling attempt was carried out by the same group responsible for a similar attempt last June. On June 5, customs officers discovered 72 server units containing advanced AI chips at the Kuala Lumpur International Airport, valued at approximately $13 million. This shipment had also been falsely declared as computer components and was meant to be reshipped without the required permit. Aspalila said the network has been linked to seizures worth over $25 million so far across at least five cases. She declined to provide further information on the network or the current case, citing ongoing investigations.

Several key facts remain unknown, including the chip type, country of manufacture, final destination, and intended recipient — whether a company or a military organization. The U.S. had placed export controls on the movement of advanced Nvidia and other U.S.-origin AI chips to China, the most likely destination for the seized chips. Smuggling networks began using Malaysia, a U.S. ally not affected by the controls, as a hub, exporting the banned chips to the country before re-exporting them to China.

While the country’s geographic location and mature logistics infrastructure make it important for legitimate international trade, these characteristics also make it ideal for smuggling sensitive technology. To restrict this avenue, Malaysia subsequently imposed export controls on the ​movement of advanced AI chips of U.S. origin, at Washington’s request. Other Asian U.S. allies, such as Singapore and Taiwan, have implemented similar controls. Demand for powerful Nvidia GPUs remains high in China, fueling a booming market for smuggled hardware.

HP, Asus, and Acer have reportedly moved some of their manufacturing capacity back into China as increasing component costs — driven by AI — bite into their margins. According to Nikkei Asia, increasing bill-of-materials costs have made China’s experienced workforce, relatively lower prices, and established supply chain ecosystem a more attractive option. However, they are not going back to traditional suppliers like Foxconn, who have focused their production capacity on AI data centers. Instead, they’re signing contracts with smaller Chinese contract manufacturers, like Luxshare, Huaqin Technology, and Longcheer Technology, that are trying to establish themselves in PC manufacturing.

Many PC brands slowly moved out of China in 2024 and 2025 as the U.S.’s sanctions and tariffs on China have increased prices and forced them to find friendlier nations. While these nations do not have established supply chains and expertise like China, it at least spreads the risk as manufacturers avoid getting caught up in geopolitical tensions.

However, it seems that rising component prices have forced them to move back some of their capacity into China. The biggest culprit is the AI-induced memory and storage chip shortage, which has caused memory prices to climb an unprecedented 500% in 12 months. More than that, other parts like PCBs and fiberglass cloth used in motherboard manufacturing are seeing increasing prices, which Acer chairperson and CEO Jason Chen bemoaned as suppliers hyping shortage fears to protect margins.

While tech companies aren’t abandoning their “China+1” diversification strategy, the mature industry in China is helping offset increased component costs. Aside from that, the Chinese contract manufacturers often have better access to memory chips from domestic manufacturers like CXMT. "These Chinese contract manufacturers have been telling potential clients and computer makers that working with them could be a good option for securing better access to Chinese-made memory chips," a supply chain manager in the computer ecosystem told Nikkei Asia. "They are marketing themselves as a good alternative to even help sort out the supply chain."

Moving back some manufacturing capacity into China is easing some of PC manufacturers’ cost concerns, but it’s also negatively impacting the newly established supply chains in Southeast Asia. "We built two new production lines in Thailand for the PC clients, and now they are moving substantial orders back to China,” one manager at a PC component supplier told the publication. “Those two lines have not yet turned profitable, and I don't know when they will add the orders back."

Unfortunately, building an entire supply chain for PC manufacturing is slow and expensive. The U.S. has been trying to accomplish this for years, and, so far, only Anduril has attempted to build laptops with 90% of its component costs coming from the U.S. and other allied countries. Even then, it expects its devices to be around 20% more expensive than the competition, which it could afford by offering them mostly to critical industries that demand supply chain security, like the government and the military. But if you’re a PC manufacturer vying for customers in the consumer market, you’re likely incentivized to find the lowest possible costs to be able to compete in pricing.

Amazon wants its engineers to quit chasing token counts, warning that heavy AI usage does not automatically mean better work. In updated engineering guidelines published on October 27, the company laid out how developers and managers should gauge AI’s real impact — and the takeaway is blunt: consuming more tokens is not the same as delivering more value. Amazon is not backing away from the technology, though. “We are certain that whatever future best practices are, AI will be at the center of software engineering practice,” the company wrote.

The guidance targets ‘tokenmaxxing,’ a practice where employees deliberately inflate their AI usage to look productive on internal dashboards. According to the memo, metrics such as AI adoption rates, token consumption, code changes, and code reviews become ‘deeply flawed’ when they are disconnected from actual results. Turning them into targets, Amazon says, encourages wasteful behavior that clashes with its emphasis on ‘frugality and simplicity’ and can raise both compute bills and software complexity.

The company’s preferred yardstick is long-term results. “We believe that it is best to measure, and be measured by, long-term outcomes,” Amazon wrote. That does not mean managers should ignore usage data altogether, as input metrics remain useful when examined ‘with care and judgment’ alongside other indicators. Amazon suggests looking at both the teams using AI the least and those using it the most to determine whether adoption is translating into better delivery.

The push comes as Amazon, under CEO Andy Jassy, has been weaving AI deeply into its sprawling operations. Jassy said earlier this year that the technology will ‘change the scope and speed’ of everyday work, and he has suggested that automation could shrink the company’s corporate workforce over the next few years — though he also expects it to create ‘other jobs.’

The memo also pushes back on the idea that every task needs an AI tool. If current tooling does not benefit someone’s work, Amazon says, that is ‘a finding, not a failing.’ Engineers who can explain where today’s tools fall short are demonstrating judgment rather than reluctance to adopt AI.

In May, it emerged that Amazon employees were using AI unnecessarily to boost internal usage scores, amid pressure to use the company's tools. Workers reportedly used MeshClaw, an internal AI agent capable of handling tasks such as deploying code and interacting with Slack, to inflate their token counts.

Notably, Amazon is not the only company rethinking how it spends on AI. Uber president and COO Andrew Macdonald said in May that higher token usage has yet to translate into useful consumer features. “That link is not there yet,” he said. The remark stands out because Uber’s CTO previously revealed that the company had burned through its entire 2026 Claude Code budget by April.

Accenture appears to also be wrestling with the same problem on behalf of its clients. Leaked audio obtained by 404 Media in June captures executives acknowledging that token spending is climbing fast as companies move from simple chatbots to agentic workflows, and that nobody can reliably predict what a given task will cost or whether it was worth the money. Accenture’s agentic AI strategy lead described it as ‘not a niche problem,’ and the firm is reportedly building an advisory tool called ‘Token IQ’ to help customers manage the bill. OpenAI CEO Sam Altman has previously acknowledged that AI token costs had become 'a huge issue', as companies faced mounting bills from heavy AI use.

Amazon, however, says it has not imposed token caps on its employees. According to CNBC, however, it shut down an internal leaderboard in May after workers were found gaming it to climb the rankings — an early example of the behavior the new guidelines are meant to discourage.

A team of around four dozen U.S. Marines is joining a NATO-led operation in the Baltic Sea to help protect undersea internet cables and gas pipelines in the area. In a press release, the U.S. Marine Corps shared its plans to deploy a "specialized team" to support NATO's Enhanced Vigilance Activity Baltic Sentry operation. The move comes amid a backdrop of growing undersea cable interest, movements of Russia's shadow fleet, and fears of sabotage.

The U.S. Marines are being sent from Marine Corps Base Camp Lejeune in North Carolina to provide "advanced expeditionary surveillance and tactical reconnaissance capabilities" to the region in support of Finnish and other NATO allies. Marines from 2nd Reconnaissance Battalion, 2nd Marine Division, and the II Marine Expeditionary Force Information Group are joining the mission.

Capt. Johan Tillander, Chief of Operations for the Finnish Navy, explained that the "experience, skills, and systems of the U.S. Marine Corps are perfectly suited" for the operation, including "monitoring of the Baltic Sea."

While the press release stresses that the Marines are serving in a "supportive, enabling capacity," with European nations taking the lead, the Marines are there to provide "highly specialized, agile force multipliers."

Maj. Gen. Daniel Shipley, Commander of U.S. Marine Corps Forces, Europe and Africa, shared that its participation shows its ability to "rapidly project specialized capabilities where they are most effective," and as a result, "ensuring we can operate seamlessly as a cohesive, lethal team to deter hybrid threats" in the area.

The U.S. deployment means that the operation will be able to "leverage... unique capabilities of Reconnaissance Marines, including small Unmanned Aerial Systems and littoral sensors, to feed vital data directly" to NATO.

The Baltic Sea is the frontline in potential assaults against European undersea infrastructure. Several attempts have been made to sabotage cables in the area, and two cables were cut between Finland, Sweden and Europe back in 2024. Meanwhile, Finland's own concerns about shadow fleet operations led it to set up a dedicated maritime surveillance center earlier this year to monitor activity in the Baltic Sea.

Russia isn't mentioned in the U.S. press release, but the authoritarian nation is one of Europe's biggest threats in the region, and has had worsening relations with European nations for several years, particularly since it went to war with Ukraine in 2022. Over 500 incidents of Russian fishing boats loitering near undersea cables in Europe have been seen in the last four and a half years, showing a persistence in mapping and identifying, if not directly interfering, with global data links in the area.

Solidigm, an American subsidiary of SK hynix, one of the biggest flash chip makers on the planet, is reportedly in talks with the Commerce Department over plans to invest $45 billion in a new plant to be built in the United States. But, Bloomberg reports, there's a catch — Solidigm wants to be given the go-ahead to export equipment to upgrade its existing plant in Dalian, China. The company has purportedly told officials that the upgrades would allow the Chinese plant to then help finance the cost of its U.S.-based counterpart, according to unnamed sources. It's as-yet unclear how the proposal was received, or whether the Commerce Department is inclined to agree to the deal.

The U.S. government has been keen to drive at-home manufacturing wherever possible, including for chips used in its computers and data centers. National security is regularly cited as a key reason behind the move. It's also the reason for the government's stance on exporting technologies to China, including the equipment that Solidigm says will help upgrade its Dalian plant. Similar export restrictions are in place for high-end AI data center components, with a black market having sprung up to take the place of sanctioned sales. Earlier this month, it was reported that some of Nvidia's powerful Blackwell Ultra B300 GPUs are being used in China Telecom data centers, with no explanation of how they got there. A 2025 report claimed that a cool $1 billion worth of Nvidia AI chips had been smuggled into China, a figure that is sure to have increased significantly as 2026 draws to a close.

In this case, Solidigm's planned Chinese factory upgrades could be mutually beneficial, allowing funds to be transferred to the new U.S. plant. But that requires the U.S. government to give it permission to ship the machinery in the first place, something that is far from guaranteed. The Dalian factory produces NAND flash memory, the likes of which are used in everything from relatively simple devices to full-blown data centers. It also comes at a time when global NAND production is a hot-button topic. Samsung and SK hynix are upgrading their Chinese factories to produce more of the chips used in AI data centers in an attempt to meet growing demand. Manufacturers have already slashed production of consumer-focused memory in favor of the more lucrative data center-grade components, causing prices to spike globally. This volatility in the market as a whole was given as one of the reasons SK hynix chose to cancel a planned Solidigm IPO earlier this week.

Beyond dangling a $45 billion plant, Solidigm has also allegedly told the Commerce Department that its total investment in U.S. manufacturing could reach $100 billion eventually, with construction in New York penciled in for 2027. The company is fresh from its latest earnings call, where it announced revenues of $75 billion with $60 billion in operating profit, and $28 billion in net. The company made no mention of its Dalian plant or a planned US investment, but did warn that memory shortages are set to last through 2030.

Palit released an official statement denying recent speculation that it has closed its Galax factory — where it makes Nvidia RTX 50-series GPUs — because it cannot guarantee a sufficient supply of Nvidia chips. According to the company’s press release, its operations continue as usual, it has not shut down any factories, and customers are able to purchase Galax GPUs through existing distributors and retailers for both domestic and international markets.

“We are aware of recent reports and speculation regarding Galax’s business operations. We would like to clarify that these claims are inaccurate,” the company said. “Galax continues to operate as usual. No manufacturing facilities have been shut down, and Galax products remain available through our sales channels in both domestic and international markets.” It also mentioned that Galax and Palit share the same factories, and that production and business operations continue as normal. Nvidia was not immediately available for comment regarding speculation about the availability of chips.

The initial news of a possible Galax closure came as a shock to many, especially as the brand had just launched its latest flagship model, the Galax GeForce RTX 5080 HOF Extreme. The rumors stemmed from an exclusive news report by a Spanish outlet (expand the post above) claiming that Palit could no longer supply Nvidia GPU chips to Galax, meaning it could no longer build graphics cards at that facility, and keeping it operational was no longer viable for the company. Thankfully, the sources were wrong, so we are not going to see another major consumer GPU brand like EVGA leave the market. Still, the speculation that Galax would stop GPU production was not implausible, especially as we’ve been seeing reports that Nvidia was ending RTX 5090 production in favor of more expensive AI data center and professional GPUs.

This wasn’t the first time this year that Galax was rumored to have ended production. Palit Group restructured its operations earlier this year, which brought similar reports of closure for the GPU brand. This was further reinforced after Palit announced that it would consolidate Galax operations at its headquarters in Taiwan. Thankfully, the speculation both then and today is false, and we will continue seeing Galax GPUs on retail shelves. However, it still doesn’t solve the skyrocketing GPU prices brought by the AI-driven memory chip shortage.

Taiwan’s Ocean Affairs Council is working on an autonomous system that would allow drone fleets to coordinate and execute missions without human intervention in response to "grey-zone" activities carried out by China. According to the South China Morning Post, the council is working with the National Cheng Kung University, which has already completed simultaneous flight simulations of two drone fleets, with each fleet comprising 10 units. This is already the second step of the process, which has shifted from individual aircraft control to the coordination of two drone detachments.

This project is designed to help Taiwan’s Coast Guard Administration (CGA) to counter Beijing’s grey-zone tactics in the waters surrounding the island nation. There have already been instances of civilian Chinese ships damaging undersea cables that Taipei relies on, with the CGA increasing its defensive patrols around 24 undersea cables and closely monitoring nearly a hundred China-linked boats. Despite that, Chinese “research vessels” continue to loiter over Taiwan-U.S. undersea cables, mimicking the activities of Russian trawlers in the Atlantic.

“The next stage will involve hardware-in-the-loop testing, prototype integration and flight trials over actual maritime areas to assess reliability under different sea and weather conditions,” the council said. The researchers programmed the drones to have a minimum separation of 100 feet in 3D space, and they’re also incorporating leader-follower formation flying and dynamic obstacle avoidance. This should allow them to respond to quickly changing conditions out in the open sea and even deal with unexpected hazards.

More than that, the autonomous system Taipei is working on would let different types of drones coordinate their movements, divide tasks, and plan routes, primarily for maritime surveillance. This is especially important as it would allow them to use different types of drones to accomplish various missions, much like how different air forces use different aircraft types.

The project is currently focusing on three Western-made drones — the Jump 20, V-BAT, and Flexrotor. In fact, the Ocean Affairs Council just received the V-BAT, a VTOL drone built by U.S. defense firm Shield AI. This unit can take off vertically from the relatively constrained deck space of non-aircraft carrier ships and offers an endurance of over 12 hours for extended missions, making it ideal for the CGA, as it would not need to make major renovations to its ships to accommodate these drones while extending their reach.

If you ever wanted to own your own supercomputer or data center while earning eBay cashback, Core 4 Solutions has the perfect opportunity. The company is selling a 300-rack AI supercomputer on eBay for $59.7 million, complete with $50 overnight shipping, or free shipping if you're willing to go with FedEx Ground. The cluster packs 9,600 Nvidia A100 accelerators and 4,200 AMD EPYC Rome 64-core processors, enough to jump-start your own sovereign AI nation.

Nvidia and AMD have moved to newer architectures like Blackwell, Vera Rubin, and EPYC Turin. Nonetheless, A100 (codenamed Ampere) accelerators and EPYC Rome processors remain highly relevant for many computing tasks, including AI, machine learning, and scientific research. With the modern Blackwell and Vera Rubin racks from Nvidia, like the GB300 NVL72 and VR200 NVL72, costing up to $6.5 million and $7.8 million per unit, respectively, the A100 has turned into a legitimate budget alternative that can also circumvent the long waitlists and supply chain delays on Nvidia's newer AI hardware.

The cluster's sheer specifications are truly jaw-dropping. Each rack has four compute nodes and three management nodes. Each compute node has two AMD EPYC Rome 64-core processors, eight Nvidia A100 40GB SXM4 accelerators, and 1TB of DDR4-2933 memory. The management nodes are no less impressive. Each features two EPYC 64-core chips and 192GB of DDR4 memory.

When you sum it all up, the cluster comprises up to 9,600 A100 accelerators and 4,200 AMD EPYC Rome processors. With 64 Zen 2 execution cores per chip, the entire cluster boasts 268,800 cores. Memory capacity is equally impressive, with up to 1,401.6 TB or 1.4 petabytes for the entire cluster.

At first glance, $59.7 million sounds like a fortune, and it certainly is, but it seems like a reasonable valuation for the cluster. A rough estimate would be about $24 million for the A100S, $3 million for the EPYC Rome chips, and about $1.1 million for the DDR4 memory. That is about $28.1 million in crucial components, before factoring in other hardware like racks, cooling, power supplies, Nvidia Mellanox ConnectX-6 SmartNICs, etc.

Listing a 300-rack supercomputing cluster on eBay sounds absurd, but given today's AI-driven world, it is not as surprising as it may seem. After all, a broker once sold a 405-foot luxury Gigayacht for $168 million on eBay, so there is no reason why Core 4 Solutions cannot move a $59.7 million cluster. As AI farms and data centers expand, some enterprise clients out there will recognize the value and opportunity in the listing.

Shipping that many racks will be a logistics nightmare, and as funny as it sounds, Core 4 Solutions offers free local pickup or FedEx Ground delivery directly to your home. There is even an option to pay for a $25 two-day or $50 overnight delivery. Imagine FedEx casually dropping off 300 server racks at your front porch.

The cluster seems to have already drawn attention, with 11 people reportedly watching the eBay listing and one prospective buyer already letting it sit in their cart. I guess the real question is not whether it will sell, but whether the buyer will choose $50 overnight shipping.

There are a lot of salesman sayings, and, chances are, most were inspired by audiophiles. The eternal sufferers of audiophilia nervosa keep coming up with novel ways to attribute sonic and emotional characteristics to any piece of equipment — even when discussing things purely in the digital realm. One of the current fads is audiophile-grade network switches, like the Aurender NH10 Network Hub, coming in at a mere $4,700.

This piece of gear —which we presume is actually a network switch, rather than a hub — comes fitted with eight gigabit ports. Six of those are standard copper inputs, while the other two are SFP ports where audiophiles can plug in a transceiver for either fiber or copper. The product page remarks that, naturally, a fiber connection is electrically isolated. I was teeing up a joke about the existence of audiophile-grade SFP transceivers when, while proofreading, I found that life does truly imitate art. Oh, and that $4,700 doesn't even get you 10 GbE capabilities.

Four of the six copper ports on the Aurender switch have a total of seven electrical filtering stages behind them, while the remaining pair "only" has two. According to an article at Headphonesty, Aurender's designer said the team experimented with using over 10 stages, but eventually settled on seven "from listening comparisons." That's an interesting feature considering that the Ethernet standard already mandates electrical isolation for standard twisted-pair connections.

Aurender also says that it's using an OXCO (Oven-Controlled Crystal Oscillator) controlled by an FPGA for clock synchronization, and there's a BNC input on the back for an external source. From what we can tell from the board picture, the main chips include: a Realtek RT8370N switch controller ($5.5 at LCSC, an MV85 oscillator ($39 on eBay), and an AMD/Xilinx Spartan 6 FGPA ($8-15 depending on model). There are, no doubt, more manufacturing and materials costs to go from a collection of chips to an assembled switch, but presumably not enough to get anywhere close to the $4,700 asking price of the NH10.

This is all adorable, but the fact of the matter is that the hardware here cannot, by any reasonable definition, make any difference to the sound. TCP connections have built-in error correction. The Ethernet protocol includes Cyclic Redundancy Check (CRC), and there are several similar layers, up to the point where the data turns into an actual analog signal.

It's also up to whichever equipment is handling the playback to electrically isolate the actual DAC output stage, not at the network or switch level. And even affordable audio interfaces have signal-to-noise ratios that 1970s recording engineers would happily give up their LSD for.

Likewise, the whole conversation about jitter is pointless, though much like any audiophile myth, it's grounded in a piece of truth — just one that's completely irrelevant in this scenario. In a recording studio, you occasionally need to sync the clock across multiple pieces of gear, just like matching tape machines back in the day. For just the one piece of playback gear, however, it's pointless, and it's downright nonsensical at the network level.

Yet logic has never stopped some self-described audiophiles. One can but wonder what folks seriously looking at these pieces of gear would feel knowing that the studios where their precious music is being made use, for example, one bog-standard Ethernet cable to carry 4 balanced analog lines. Or how their favorite guitarist's pedals and amplifier in a famous recording session were held together with lamp cord, duct tape, hopes, and dreams.

A scary-looking humanoid robot with a chainsaw arm was shown in action on Japan's ANN News recently. The rail truck-mounted mech was being used by Japanese railway firm JR East to clear tree branches that had begun to impede rail tracks. The unnamed heavy machinery robot’s chainsaw and grabber arms are remotely controlled by a human in this instance. However, JR East is broadly integrating AI and robotic technologies across its operations to enhance safety, efficiency, and round-the-clock maintenance.

In the future, this robot could become autonomous with AI-based image analysis for recognizing rail obstructions that need to be addressed. While they’re at it, why not dispose of the wheeled vehicle and add some legs and a jet pack?

JR East is the largest passenger railway operator in Japan. It is famous for operating the northern Shinkansen (bullet train) routes, but also has business interests/revenue from shopping centers, hotels, smart cards, and more. The firm has a reputation for quite aggressive AI adoption, including company-wide AI tools, designing AI programs for disaster recovery, and it is even using drones equipped with AI image analysis for rail infrastructure inspections (PDF).

The source video and report (machine translation) indicate that the chainsaw mech you see has been available to JR East engineers since March, but the TV crew took the opportunity to catch a rare glimpse of this heavy machinery in action. JR East is turning to robots like this due to labor shortages. Apparently, operating a robot like this requires some skill, but so does chainsaw work at height.

There’s also been a recent shift in rail building and maintenance work to the daytime – despite some inconvenience to customers – to the benefit of workers and safety. Perhaps that’s why we now have a video of the chainsaw-wielding truck-mounted robot for the first time.

The combination of labor shortages and advances in robotics and AI makes development of this kind of rail maintenance robot seem very timely. If the metallic humanoid can be made autonomous, it could save a lot of labor and promote safety. What could go wrong?