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.

HP’s HyperX Omen 35L is ambitious, and it’s hard not to be impressed by it in our maxed-out test guise with a Ryzen 9 9950X3D and Nvidia RTX 5080. From its monochrome design and extensive RGB lighting to its top-tier components, it checks the boxes enthusiasts are looking for.

But when a PC is $6,211 like our test configuration, it raises the question of whether the Omen 35L’s blend of style, performance, and premium features is enough to justify a purchase, even if that's what high-end PCs cost right now.

Design of the HyperX Omen 35L

The HyperX Omen 35L’s black and white case stands out, looking refreshingly modern, even though parent company HP has used similar designs before. At 8.27 by 16.06 by 16.14 inches, it’s firmly in mid-tower territory. The metal case feels reassuringly solid and makes extensive use of tempered glass. Bright RGB fans shine through the front while many RGB-lit components are visible through the side panel. It’s a classy system.

The contrast continues inside, where the black motherboard, graphics card, and power supply are easily visible against the white case. The white liquid cooling loops are especially attractive, as is the CPU cooler featuring its own LCD panel. The latter shows CPU and GPU temperatures.

HyperX Omen 35L Specifications

Ports and Upgradeability on the HyperX Omen 35L

HyperX’s top-mounted front ports include USB-C (10 Gbps), two USB-A (5 Gbps), and an audio combo jack. This location works well if the desktop is on the floor.

At the back, the tower offers a pair of USB-C ports (10 Gbps), two USB-A (5 Gbps), four USB 2.0, DisplayPort 1.4 output, Ethernet, and audio jacks. It’s disappointing to see a desktop this expensive offers neither USB4 nor Thunderbolt 4, though it does include Wi-iFi 7 and Bluetooth 6.0 from a Realtek 8922AE networking card. The wireless antennas are built into the chassis, eliminating the need for external protrusions. It’s also notable that HP offers a Clear CMOS button, a surprise on a mainstream tower.

The HyperX Omen 35L offers about as much upgradeability as expected from a mid-tower, without proprietary parts. Its side doors are secured with thumbscrews; the slide rearward and away. The mATX-style motherboard offers four DDR5 DIMM slots, populated in our review unit with two 32GB RGB HyperX modules running at 6,000 MHz.

The system also supports two M.2 2280 drives. The primary slot is covered in a substantial heatsink just above the RTX 5080. Accessing the other slot requires removing the RTX 5080. The card is secured with five screws, two of which hold it to the front support bracket. Deft fingers are required to reinstall the screws at the back of the card.

HP’s wiring work is tidy for a mainstream tower. Sleeved cables would have improved the refinement. On the plus side, HP includes a fully modular power supply, an uncommon feature in big box systems.

Gaming and Graphics on the HyperX Omen 35L

We tested the HyperX Omen 35L in a maxed-out configuration featuring a Ryzen 9 9950X3D processor, 64GB of RAM, and a GeForce RTX 5080.

I fired up 007 First Light to test out this system’s prowess. At a 3840 x 2160 resolution with high settings and post effects enabled, I saw up to 120 frames per second (fps) indoors and in the mid-90 fps range in more complex scenes without DLSS. The game never stuttered and remained supremely playable.

The systems we’re using for comparison include a pair of RTX 5090 towers, the Alienware Area-51 (Ryzen 9950X3D, $5,749.99 when tested, $8,899.99 as of this writing) and the Maingear MG-1 (Ryzen 9 9950X3D2, $7,067 when tested, $9,076 as of this writing), plus Corsair’s Vengeance a7500 powered by a Ryzen 7 9800X3D and an RTX 5070 Ti ($2,799.99 when tested, $3,299.99 as of this writing). With its RTX 5080, the HyperX desktop ought to slot in the middle of this group.

The HyperX desktop performed as expected given its RTX 5080. That said, the 1080p numbers show significant variance from system to system. In Shadow of the Tomb Raider (Highest detail preset), the HyperX produced 300 fps to outperform even the Alienware (248 fps), though it couldn’t catch the Maingear (355 fps). In other instances, even the RTX 5070-powered Corsair was able to pull ahead as seen in Far Cry 6 (Ultra settings), where it produced 197 fps against the HyperX’s 141 fps.

But these desktops are designed for 4K play. At that resolution, the HyperX’s RTX 5080 produced admirable numbers. Naturally, it wasn’t in the same league as the RTX 5090 systems; in Red Dead Redemption 2 (Medium settings), the HyperX produced 83 fps against the Alienware’s 122 fps and the Maingear’s 121 fps, and in Borderlands 3 (Badass settings), the HyperX landed at 115 fps against Alienware’s 166 fps and Maingear’s 186 fps. The differences narrowed in Far Cry 6, with the HyperX producing 118 fps to Alienware’s 128 fps. The largest deltas were seen in Cyberpunk 2077 (Ray Tracing Ultra preset), where the HyperX produced 36 fps, well ahead of the Corsair’s 29 fps, but significantly behind the Alienware’s 55 fps. Overall, though, this HyperX desktop is well-suited to 4K play.

We stress-test gaming desktops running 15 loops of the Metro Exodus benchmark at RTX settings, simulating about half an hour of gameplay. During the test, the HyperX desktop averaged 172.21 fps across all runs with a sub-one fps variance, indicating stable performance. During the test, the Ryzen 9 9950X3D averaged 4.79 GHz and 48 degrees Celsius while the RTX 5080 ran at 2.74 GHz and 50 C.

Productivity Performance on the HyperX Omen 35L

Our HyperX test unit features a Ryzen 9 9950X3D processor, 64GB of RAM, and two 1TB PCIe 5.0 drives running separately, one hosting the OS andthe other for storage.

Featuring the same Ryzen 9 9950X3D processor, the HyperX and Alienware systems performed similarly across our CPU tests. In Geekbench 6, the HyperX system produced 19,923 points against Alienware’s 20,411 points, though it was handily outperformed by the Ryzen 9 9950X3D2-powered Maingear, at 23,741 points. With fewer cores, the Ryzen 7 9800X3D-powered Corsair finished last, at 18,124 points.

The HyperX desktop finished our Handbrake 4K-to-1080p video transcoding test in 1 minute and 47 seconds, tying the Alienware, though the Maingear edged ahead, at 1:32. The Corsair took nearly twice as long, at 3:02.

In our 25GB file transfer test, the HyperX’s average transfer speed of 3,197.42 MBps was excellent, just behind Alienware’s 3,414.29 MBps and ahead of the Corsair’s 2,659.94 MBps and Maingear’s 2,782.43 MBps.

Software and Warranty on the HyperX Omen 35L

HP includes several useful apps. Omen Gaming Hub app provides performance control, with per-fan RPM settings and, in Extreme mode, PPT and EDC settings for the Ryzen 9 CPU. The Advanced Performance Tuning section also provides PBO, CPU Boost, and voltage curve controls plus a benchmark to test your settings. For quick access, the app provides an overlay that shows your power profiles and system monitoring, including component temperatures and fan speeds.

Gaming Hub also provides customization of the LCD display on the liquid cooler. Many faces are available, from system monitoring to static images, GIFs, videos, and a clock. It seems simple, but it’s one of the most fun features of the system, allowing you to easily change up the look.

Lighting is managed through Omen Light Studio, which provides per-device RGB control. Fine control is possible – each of the five fans has 10 LEDs, which are individually selectable. Clicking and dragging selects multiple fans and LEDs at a time, to which you can apply preconfigured light recipes – Galaxy, Ocean, Jungle, Volcano, and Omen – and apply an effect, such as wave, static, ambient, breathing, color cycle, an audio visualizer, and radial. The speed and direction of the effect is also configurable. You can also create layered effects and save everything in profiles that you can export or import. HP covers the HyperX Omen 35L with a standard one-year warranty.

HyperX Omen 35L Configurations

We tested the HyperX Omen 35L with a Ryzen 9 9950X3D processor, 64GB of dual-channel DDR5-6000 RAM, GeForce RTX 5080 graphics, Windows 11 Pro, and two 1TB PCIe 5.0 solid-state drives. The system uses a 1,000W power supply and a 240 mm liquid CPU cooling with a built-in LCD display. This configuration represents the top of the stack, retailing for $6,210.98.

The entry-level is probably where the Omen 35L offers the least value – with standard liquid cooling and a modular power supply, this system is for premium builds. AMD-based starter configurations begin at $2,399.99 with a Ryzen 5 9600X or $2,589.99 with an Intel Core Ultra 5 250KF Plus. Standard components include a GeForce RTX 5060 Ti (8GB), 16GB of memory, and 1TB of PCIe 4.0 storage. Memory tops out at 128GB and two SSDs are supported up to 2TB each.

Bottom Line

Primarily, the HyperX Omen 35L gets one thing right: it feels genuinely premium. Its sharp black-and-white design, quality lighting, quiet operation, and enthusiast-grade components like a modular power supply give it the polish it needs to compete against boutique builders and aftermarket systems. Its performance in our test system with a Ryzen 9 9950X3D and an RTX 5080 match the expectations for this lofty price point.

That said, it’s clear that this desktop is aimed at the more premium side of the market, as prices of the entry-level configurations are difficult to justify given their level of hardware. But if you have deep enough pockets for a well-equipped configuration, the HyperX Omen 35L is a highly compelling gaming desktop well worth considering alongside anything the market has to offer.

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.

Agentic AI has set off a firestorm in the world of CPUs over the past six months, yet it’s an area of performance that still doesn’t have solid, repeatable benchmarks to evaluate where the latest hardware falls. We’ve been working on an internal set of tests to evaluate at least one aspect of contentious agentic loops end-to-end, isolating CPU and memory performance from the accelerator to understand how these components contribute to the agentic loop.

This is still a work in progress, but we wanted to bring Tom’s Hardware Premium readers behind the scenes to understand how we’re going about tackling this problem, our methodology for testing, and detail what we’ve learned about agentic loops along the way. We've prototyped the test and settled the central design, but there's still work to do before the numbers it produces hold up to scrutiny.

Given that this area of computing is bleeding-edge and rapidly evolving, we undoubtedly have blind spots here. That’s OK. The testing we’re doing is built to look at how agentic performance scales across the CPU as sessions increase, providing an ideal throughput for each chip that provides as many agent sessions as possible while remaining interactive.

Toward the end of this article, we've included a section on some of the shortfalls of this approach, as well as some ideas we’re toying with to tackle things like multi-turn agentic flows. And keep in mind that these tests aren't meant to replace our other testing of local AI, but to supplement it. As this space continues to evolve and the hardware characteristics are better defined, we’ll be able to make more informed decisions about where this testing goes in the future.

Defining an agentic AI loops

"AI agents" imply some little workers that, strapped with an LLM, can go and handle whatever task you throw at them. It's a fine description, but we'll need a bit more a technical definition if we're going to benchmark the performance of agentic AI.

There are two parts here that are important. First are functions. With agentic AI, we give the LLM access to tools, which, in the case of popular agentic frameworks like LangGraph, are Python functions. The tool may be reading or writing a file, querying a database, running tests on code, or just about anything else you can define in a function. Here's a basic example.

@tool    def run_command(command: str) -> str:        """Run a build or test command in the repository root. Allowed: make [targets], python -m pytest [args]."""        return traced("run_command", {"command": command}, lambda: ws.run_command(command))

This is how you write a tool call with LangGraph inside of Python, and it's an example of a tool call that the we've given the LLM access to through our LangGraph persona. We identify the function as a tool the LLM can call with "@tool," and then we define the function like normal Python with "def run_command," passing through the command as an argument. The extra "-> str" on the end notes that the argument will be a string, and the function should return a string.

That's followed up by a description of the tool that the LLM can read (triple quotes just allow you to space this across multiple lines in Python). We're prompting the LLM that it can run build or test commands using make or pytest. For this example, we wrap the function up with the anonymous lambda function and pass it back to an overarching trace function. That's not important right now, however. The goal is to illustrate what a tool call looks like.

This is only one of a handful of tools available to our coding agent. It can also read and write files, edit files, and search code using regular expressions, all defined in the same way with Python functions. Agentic AI is often described as a "loop," however, and that's because the task often requires calling a tool multiple times and/or calling different tools.

If we're working a Python project, for instance, we may prompt the LLM to "debug this Python script." It may call the "list files" tool to evaluate what Python script we're talking about first. Then, it will call the "read file" tool to understand it, or maybe "search code" to find a spot to start troubleshooting. Then it might run "edit file" and "run command," and if the file isn't fixed, it may loop back over these tools a few times.

This is the agentic loop. We prompt the LLM, it evaluates and starts calling tools it needs to form an answer. Then, it gets back the information from the tool calls, it re-evaluates, and then it may call more.

Starting with concurrency

We’ll dig into the specific tests and what they’re doing later in this article, but it’s good to set the stage with why our benchmark is designed the way it is. We developed the test on an Nvidia DGX Spark (GB10) featuring 128 GB of unified memory, a Blackwell GPU, and the relatively weak Arm-based Grace CPU. Above, you can see the time spent running these agent loops on the GB10 with gemma4-26b-a4b. The model isn’t super important here as long as we're able to call tools, and we’re able to re-record the tests using different models as they continue to release.

The point here is the share of time spent, not the model or the specific work the agents are doing (though that does provide useful context). Each result here is a list of sequential tasks for the agent to execute against the model, and nearly all of the time is spent on the GPU. Even relatively demanding CPU tasks, like with our coding agent, represent just a small fraction of the computational time. Running one or even a few agents isn’t a problem for the CPU when we have access to a large, powerful GPU with a sufficiently large model running on it.

Looking closer at one specific set of tasks, you can see that there are plenty of tasks for the CPU to execute during the window, but those tasks happen so quickly that it barely registers in the overall time spent.

These tasks aren’t evenly spaced, either. Depending on when the agent needs to call tools, the shape of the workload can look very different. Above, for example, you can see our document agent, which is tasked with scanning and extracting information from a set of PDF files. Nearly all of the agent time is spent at the very beginning of the task, after which it’s almost pure GPU inference.

Obviously, a more powerful GPU will churn through the inference step faster, but we’d have to create a severe imbalance between the CPU and GPU for the CPU portion of a single agentic loop to create a bottleneck — at least with these common tools. Agents don’t just handle a sequential list of tasks, however. They spawn sub-agents, and if multiple sessions are running on the same system in sandboxed environments, contention for limited resources becomes a real issue.

Instead of designing the test platform in such a way that creates a severe CPU/GPU imbalance, we looked toward concurrency. The idea is to sweep across the chip with concurrent sessions running a list of tasks in a loop in their own processes. As the sweep scales up, contention across the CPU starts to appear, and minor differences in tool calls, code execution and compilation, web browsing, and the variety of other tasks our agents do pile on.

How the benchmark works

With concurrency as our north star, we can start breaking down the design of the benchmark and how it works internally. The overall design is simple. Custom agents built with LangGraph have a list of 30 tasks to execute in sequence against a llama.cpp server running gemma4-26b-a4b. Instead of going directly to the server, however, the agents go through a proxy server that records a trace.

During live recording, the agent communicates with the proxy server, which passes a live call to the llama.cpp server. The proxy server meanwhile writes every logged request to the LLM and the response from it to an overarching JSON. Tool calls bypass the server chain and are logged to their own JSON files. Finally, for each task, a correct response is generated, hashed, and stored as its own JSON.

Recording everything allows us to remove the live model entirely from the equation. The agents still run as normal, but instead of running against a live LLM, they run against that proxy, replaying the traces captured. The requests and tool calls are all happening live, but the agents are immediately handed back a response from the trace based on the live inference capture.

Starting with the request from the agent, it’s matched by session label and hash to ensure it’s accurate before the response is looked up in the master JSON. If there’s a mismatch, the run stops and is marked as invalid. If everything checks out, the response is passed back, and the agent session(s) call the tools they need. The tool calls are compared to the JSONs generated during recording, and the recorded result is passed back. This is critical.

The test compares the live tool call to the recorded result, but it always passes back the recorded result to keep the test from drifting, even if the tool call is correct. True tool mismatches are logged. The test proceeds, but it’s marked as invalid for each level with tool mismatches.

This setup makes redesigning and expanding the benchmark simple. We can write a new persona in Python using LangGraph, or expand to other frameworks like CrewAI. Additionally, we can rerecord traces against different models in the future, perhaps some with expanded capabilities. As long as we’re communicating over an OpenAI-compatible endpoint, we can mix and match pieces of our testing to represent a broader range of workloads.

The test starts at a single worker process and scales up to the maximum thread count of the CPU, running three repeats per level. Each worker process operates off a list of 30 tasks, with new worker processes starting at the next point in the list. So, worker session one would start at task one, worker session two would start at task two, and so on. Tasks do not take an equal amount of time, so this offset creates imbalanced contention as tasks overlap.

What this actually measures

The metric we get out of the test is sessions per second. A session is a single task completed from end-to-end, and we measure the number of sessions completed per second at each level. Naturally, at lower levels, we complete fewer sessions per second, often falling below 1, meaning the task takes longer than one second to complete. The tasks are completed sequentially. What we’re looking for is how chips diverge as sessions increase, measuring the throughput of agentic work on the CPU.

We derive two headline scores from the sweep. First is peak throughput, which is the highest sessions per second we recorded, as well as the concurrency level at which it was recorded. Next is ideal throughput, which is the point at which the slowest 5% of sessions take no longer than 1.5x the time it takes for a single session to complete at the base concurrency level. We expect that tasks will take longer as the number of sessions increases, but past that 1.5x mark, the length of the task gets significantly longer, and depending on the processor’s topology, throughput can start to degrade.

It’s worth highlighting again that there is no GPU delay in this concurrency sweep. We’ve removed the GPU entirely, throwing as much concurrent work at the CPU as possible to determine where that peak throughput point lies. However, the GPU still plays a significant role, so the results should be viewed in a realistic context.

As more agents work on the CPU, they’ll compete for resources on the chip, but this balance can easily shift. If there isn’t enough GPU horsepower, agents may queue for a slot, and that’s not effectively utilizing the compute on either processor. Our testing is more than theoretical throughput from napkin math, but it’s less than a true end-to-end system. There are too many variables to account for in that setup for us to produce any useful numbers that are broadly applicable.

However, we can run the sweep with live inference to evaluate system-level performance. For live runs, the responses handed back to agents are still served from the recording to keep runs equal. However, the request is recorded by the llama.cpp server, which generates the same number of tokens before being discarded. This accounts for the normal inference pipeline, including prefill, decode, and cache performance, as well as agents queuing for a slot.

Both numbers are important. Live sweeps are a realistic look at local AI across the entire system, while frozen replays measure the peak throughput a CPU is capable of with access to all the GPU compute (theoretically) that it could want.

Our starting agent personas

We’ve defined six agent personas. A “persona” is a Python script built with LangGraph that defines what the agent can do. The agents tool calls are Python functions, defined in that main script that connects our LangGraph nodes. Each persona is working off its own unique list of tasks from local documents. For example, the research persona is able to answer questions by searching in a Wikipedia corpus of about 50,000 articles.

Above, you can see an overview of each persona. For five out of the six, we start with some sort of planning stage, followed by the main agent loop, and finish with a report. For the analyst persona, for instance, the planner takes the questions and splits them into structured output with two to four steps. Analysts then run in parallel, searching the database, and the writer turns those findings into a response.

The one exception is the coder persona, which works a bit differently. It’s working off four projects, attempting to address a bug from an issue report (similar to a GitHub issue report). The coder finds the bug, runs tests to confirm the fix, and then passes it off to a reviewer step. The reviewer step (naturally) reviews the fix before applying it.

For each of the personas, the current test setup looks at single-turn tasks. Agents make multiple loops to the LLM depending on the task, and often use tools several times, but it’s all to resolve a single task with no previous context. For instance, when a customer support query comes up, the agent may take multiple passes at the knowledge base to answer that query. However, once the agent has answered the query and moved on to the next, it’s treated as a separate action without context from the first query.

This is not completely representative of agentic workflows, which are often multi-turn processes. We're able to script multi-turn tasks, but that creates greater divergence risk, as well as risk of a crash. Running this locally, it's not hard to spill over the context window after a few turns of complex interactions. It's possible to do, but it's not on the top of our list of priorities right now.

We won’t cover all of the tasks the current personas do because, frankly, it’s a lot and most of it isn’t that interesting. Still, we want to provide at least one example for each persona so you can get an idea of what’s going on under the hood.

All of the other tasks are very similar to what you can see in the examples above. With the documents persona, for example, the agent is always approving the correct invoices, but the mix of documents and types of errors change between sets. Similarly, the support persona is always answering support questions, but they focus in different areas. Some ask to troubleshoot error codes, others (like the example above) ask about policies, and more still include multiple questions within one query, tasking the agent with piecing together multiple areas of the knowledge base.

Shortcomings of the current approach

Although we’ve tried to create a broad range of agentic work here, there are some shortcomings of the current version of this test. Most notably, our testing and recordings only come from two types of systems: a 64-core Threadripper test bed with 128GB of DDR5-6000 RDIMMs, and a DGX Spark.

As we continue to test other platforms, we may need to adjust the tests, rerun recordings, or swap the model. We’d have to re-gather all of our results if we make those changes. By other platforms, we’re not just talking about an Intel system, either. We’ve seen a variety of different local AI setups, from Mac Studio clusters to GPU-ridden workstations. The load on the CPU completely changes depending on the GPU performance, model, quantization level, and server settings.

The other shortcoming is API performance. The test is designed in such a way that we can easily connect to any model from a frontier lab through their API. Although that model runs in the cloud, the tools themselves run locally unless you’re using an entirely cloud-based sandbox. If you’re working through an AI-assisted development workflow on a budget laptop, for instance, all of the compilation, testing, and agent code still runs on your machine.

Although we have plans to look into this hybrid model with a weaker host PC and a cloud API, we didn’t explore it too much during initial development. API usage is expensive, and we don’t want to be throwing away tokens trying to get the initial test suite set up correctly.

Finally, sandboxing. The current test is isolated through a Docker image, which introduces negligible overhead (<2%) compared to a bare-metal run. We’ll need to explore the overhead of sandboxing local agents via different means in the future, which is an area we really haven’t explored much in this test.

Continuing to learn

Agentic AI is rapidly evolving, so much so that you’ll get different definitions of what characterizes agentic workloads and hardware built for agentic workflows depending on who you ask. Add on top of that rapid hardware and model changes, and it’s obvious that we could never define a bulletproof benchmark that can stand the test of time. That’s a fool’s errand. What we have here is our starting point, which we will continue to build on top of.

From what we have now, the next steps are to get the first real numbers. We've done some basic tests to ensure the system is set up correctly (for instance, running a free-threaded version of Python to test GIL impact within a single session), but the focus here was getting the basic system in place to record traces and replay them.

We're also relying exclusively on LangGraph here. We need to explore other frameworks like CrewAI, as well as the "one-click" agents like Hermes and OpenClaw.

You don't have to settle for huge price rises in the PC hardware market. Thanks to Newegg, there's a $1,250 discount right now on a 4K-capable Alienware Aurora DACT1250 gaming PC with an Nvidia GeForce RTX 5070 Ti and 32GB of DDR5 RAM for $2,599.99.

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This is Dell's premium brand for gamers and, as you can expect from a premium brand, the prices are usually towards the top end. The $1,250 discount here means you'll be saving a huge sum on a gaming PC with serious firepower for 4K gameplay, putting it at a much better price than you would get elsewhere. This DACT1250 model also comes with an Intel Core Ultra 9 285 CPU and a 1TB SSD for storage.

Front and center here is the Nvidia GeForce RTX 5070 Ti. This is one of Nvidia's newest Blackwell GPUs but, more importantly, it's the third most powerful consumer GPU of Nvidia's current-gen lineup. It has 8,960 CUDA cores and 16GB of GDDR7 VRAM, operating on a bigger 256-bit memory bus. This is a card that'll smash through 1080p with the highest frame rates, taking you up to 1440p and 4K with ease.

The VRAM boost to 16GB is important because it means your PC has the opportunity to switch up to 1440p or 4K and run games at high and even graphic presets at those resolutions. 4K, especially with ray tracing switched on, might require a few tweaks to your settings, or involve you falling back on Nvidia DLSS with multi-frame generation for an AI-helped uplift, adding extra frames into the mix for smoother gameplay. The Bench charts and our GPU benchmarks place the RTX 5070 Ti pretty highly compared to rival GPUs.

For the CPU, you're getting the Intel Core Ultra 9 285 here. It ships with 24 cores, eight of which are performance cores, which'll be good for high-performance gaming. You also get 16 efficient cores for multi-threaded tasks. Unlike the 285K, the 285 is locked, meaning you can't overclock it. You get clock speeds as high as 5.7 GHz with the performance cores in action here, though. This Arrow Lake CPU hasn't been a big hitter in the gaming world, admittedly, but the raw power of the RTX 5070 Ti is still formidable, and should help to compensate.

Elsewhere, though, you're getting 32GB of DDR5 memory, so you won't see a bottleneck as far as system RAM is concerned. There's also a 1TB SSD included here, giving you space for a few big games alongside a Windows installation on the same drive.

The $1,250 saving on this pre-built Alienware Aurora DACT1250 gaming PC is a serious bargain, and it means you'll only pay $2,599.99 compared to the original $3,849.99 list price. The RTX 5070 Ti in this build means you've got a GPU ready for 4K gaming, with specs that should keep you going for years to come.

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.

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Anthropic spent less than a third of its $8.9 billion first-half compute bill on serving customers, with most of the rest going into training, research, and running Claude internally, its S-1 filing shows. It has meanwhile signed up for hundreds of billions of dollars more in TPU and GPU capacity. By our math, it has meanwhile signed up for roughly $800 billion more in TPU and GPU capacity, and revenue per compute dollar jumped from $0.40 in Q1 2025 to $2.08 in Q2 2026, which the company links to capacity purchased 12–18 months ahead.

Anthropic’s IPO is likely to beat that of rival OpenAI, which filed confidentially before CEO Sam Altman said its own won’t come this year, amid widespread doubts about an AI bubble. Whether AI labs can cover what it costs to serve their models is an open question.

Anthropic appears to do so easily: it spent $2.4 billion serving customers in the first half of the year, versus $16.3 billion in revenue. Paying for what it has on order is more difficult. Its five compute deals with the biggest stated amounts would cost an estimated $103 billion a year on average once delivered, more than twice its revenue at Q2’s pace of about $46 billion a year. The company also warns that margins may compress as new hardware lands ahead of the revenue it will support.

The company has three main costs: the cost of revenue, which is the inference cost of running Claude for paying customers; R&D, which is mostly compute for training and research into new models plus any internal use; and sales and marketing, which covers the cut taken by channel partners and cloud platform resellers.

Comparing the first half of last year to this year’s, cost of revenue is up about 2.3x, but with revenue up 14x, that means Anthropic keeps an estimated 85 cents for every dollar after serving costs, up from just 12 cents. R&D is up about 5 times, with 66% or more of the compute going to it, and that’s the main source of its operating loss. Sales and marketing is up 16x, and doesn’t show up in cost of revenue, but its partner fees and marketing would lower what it keeps to 66 cents a dollar. The trend here is that serving Claude now more than covers its costs.

Serving has kept getting cheaper for customers since June: in September, Anthropic’s Opus 5.5 model cut costs per task by about 40% versus Opus 5, says the company, and Fable 5.1 cut the cost of typical workloads by about 25% against Fable 5; each came from “a combination of a lower price and improved efficiency,” made “while preserving our margins.”

As for compute, which runs through all three costs, Anthropic runs it as “a single, shared resource across all workloads” and doesn’t disclose how it decides the split between training, research, and serving. Serving was limited, as the company has “consistently operated in a compute-constrained environment where customer demand exceeds available compute,” which suggests serving costs were muted by a lack of supply, not just efficiency.

Anthropic runs a “multi-chip compute architecture across AWS Trainium, Google TPUs, Nvidia GPUs, and AMD GPUs,” without giving any percentage splits or GW measurements. For TPUs, it has five-year equipment leases with Broadcom for “TPU-based compute capacity,” with rent of “approximately $161.2 billion excluding interest”; some of that capacity came online this year, with more from 2027. Another $114.5 billion or more is with Google “between April 2026 and July 2033,” with more from Google and other TPU providers from 2028 to 2031, estimated to be in the $125–$150 billion range.

Trainium comes through Amazon, with a deal for “at least $110.0 billion between May 2026 and April 2036,” though the filing doesn’t split AWS spend by chip. Nvidia GPUs come through a series of deals, including “up to $120.5 billion … extending into 2029” with SpaceXAI, “at least $44.9 billion” extending into 2033 with Microsoft, “up to approximately $44.6 billion” also into 2033 with Nscale, and about $41.3 billion into 2034 with Lambda. There is also an AMD hardware supply agreement which, in aggregate, may “exceed $20.0 billion” if Anthropic takes the full initial capacity. These deals each have their own conditions for termination.

Anthropic’s minimum purchase commitments, which exclude equipment leases such as Broadcom’s, have gone from about $55 billion at the end of last year to about $265.6 billion by the end of Q2 this year. Since then, it has signed about $245 billion in new minimums, leases included, and TPUs are the biggest line overall. The Nvidia capacity, Microsoft’s aside, is also the easiest for Anthropic to walk away from.

With all these multi-year commitments, bought well ahead of delivery, expenses are paid and recognized “as compute becomes available to us,” the company says. That compute base “did not fluctuate with in-period demand,” so revenue per compute dollar increased as demand surged. When demand outruns that capacity, Anthropic buys short-term compute, which “typically carries a higher cost per unit.”

Our $103 billion estimate is straight-line, spreading each deal evenly over its term except SpaceXAI, which is at its full monthly rate, while payments ramp up as capacity is delivered. Also not included in the estimate is interest on the Broadcom leases. Compute also does not necessarily have to earn revenue, especially in R&D.

The filing has many blanks, such as the total commitment figure, the contractual obligations table, Q3 revenue, and Run-Rate Revenue, which an amended S-1 would normally fill in before pricing. No date for that has been given. Microsoft’s minimum starts in November, and SpaceXAI’s full delivery is expected in December, with non-cancelable periods ending in January and March 2027, after which either side can cancel on 90 days’ notice. More Broadcom TPU capacity and AMD’s first tranche follow next year, by which point most of these deals will be billing.

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.