Owning a PC gaming handheld is quite handy, but sometimes involves playing uncalled-for matches of Tetris with the internal storage, especially as games get larger. Storage expansion often comes by way of microSD cards, which can be added without opening the console, but can be slower than NVMe SSDs. BiWin's CL 100 Mini NVMe SSD tackles both those problems, arriving in a 15 x 17 mm form factor with custom casing. It's effectively the size of a microSD card.

The diminutive unit was spotted earlier in the year, but hadn't been given a name or selling price until recently. The unit is currently bespoke to BiWin's OneXPlayer consoles (OneXFly Apex/Air, OneXPlayer 3, X1 Air, X2 Mini, and Super V/X) as well as the GPD Win5. These systems have hardware slots specifically for this size drive that you could insert like an SD card. For comparison, the M.2 2230 drive inside the Steam Deck and other portables is, unsurprisingly, 22 x 30 mm — much larger, and it requires opening the machine, plus one of those annoying little M.2 screws.

• 512 GB at Amazon UK
• 1 TB at Amazon UK
• 2TB unit only available from third-party sellers at Amazon UK
• Drive reader at Amazon UK

Despite the physical format being manufacturer-specific, the CL 100 Mini appears to be a standard NVMe 1.4 drive with a PCIe 4.0 interface. The installation instructions are simple. The Silicon Motion SM2268XT2 controller is a standard NVMe affair, and it's also used in standard M.2 2280 Crucial and Kingston drives, among others.

Despite being connected to only one slab of Samsung V8 TLC NAND, it can still reach 3700 MB/s on sequential reads and 3400 MB/s for writes, both excellent figures as far as the needs of gamers are concerned. If you want to use the BiWin CL100 Mini as an external drive, the company offers a mini-SSD reader with a USB-C port, though speeds are limited to around 3000 MB/s in either direction.

You can get the drive from the BiWin store on Amazon, in 512 GB for 139.99€ and 1 TB for 259.99€. There's also a 2 TB unit advertised, though that one is only available from third-party sellers for an arguably disproportionate 699.99€. The smaller units are roughly in line with equivalent M.2-2230 units from reputable manufacturers. Meanwhile, the reader unit is priced at 79.99€.

The PlayStation 5 is getting AI-driven upscaling, giving base console owners access to the technology that was previously available on the more powerful PlayStation 5 Pro. In a blog post, Sony introduced Quick Spectral Super Resolution (QSSR), a new version of its AI upscaling technology for the PS5, with Marvel’s Wolverine and Ghost of Yōtei being the first games to support it.

Sony originally introduced PlayStation Spectral Super Resolution (PSSR) as a highlight feature when it launched the PS5 Pro. The technology uses AI to analyze frames on a pixel-by-pixel basis and reconstruct them at a higher resolution. This helps improve image detail and temporal stability while allowing games to render internally at a lower resolution. However, the computational requirements of PSSR meant that the feature was not available on the standard PS5.

According to Sony, QSSR uses a streamlined neural-network architecture and a hand-tuned implementation intended to maximize performance on the PS5. The company describes it as a new performance tier of AI upscaling rather than a direct replacement for PSSR, which remains the “gold standard” solution on the PS5 Pro.

Sony does not intend to limit the new upscaling tech to its own studios, as the QSSR library will be offered to all PlayStation developers. This will allow more PS5 games to adopt the technology in the future and give developers an option for balancing rendering resolution, performance, and image quality on the aging console hardware.

Jasmin Patry, Lead Rendering Engineer at Sucker Punch Productions, said, “QSSR excels at resolving the fine details in our characters and environments and does so with a level of temporal stability that hasn’t been possible on the PS5 console until now. We’ve long wished that all PS5 players could experience the incredible image quality that PSSR brings to PS5 Pro, and QSSR is a positive step towards that goal!”

With Sony continuing to improve its upscaling technology in collaboration with AMD under Project Amethyst, PS5 owners can expect sharper and more stable visuals in supported games without having to upgrade their hardware. That said, QSSR won't turn the standard PS5 into a PS5 Pro; rather, it can be best described as an important software-level upgrade for the console.

Amazon and chip design tool maker Synopsys are entering a multi-year deal, said to be worth over a billion dollars, in which the two companies will deepen their cooperation on AI chip design, while better optimizing their tools for one another's services. As part of the deal, Amazon will license Synopsys' IP and expand its use of Synopsys' electronic design automation (EDA) software tools for AI chip design and agentic AI technologies.

The deal goes both ways. From its side of the equation, Synopsys will work with Amazon to optimize its multiphysics solutions for Amazon Trainium and Graviton chips, will adopt AWS cloud computing and storage services, and will begin using Amazon Bedrock to build and deploy AI applications and agents for its own development work.

Although there's clearly some element of cooperative back-scratching with this deal, Synopsys and its competitors are moving towards automating ever greater portions of the chip design process. Amazon ensuring that process is optimized for Amazon hardware places it in a much more favorable position in a world where chip design is easier and faster. Especially with many of the major AI companies looking to develop their own inferencing hardware to ease costs from pricey Nvidia GPUs.

Building the future, together

Many of the major AI developments in 2026 have centered around the use of AI agents, leading to hardware shortages and a race to fill that gap with optimized hardware. The Synopsys/Amazon deal could put both companies in a position to take advantage of that, designing and developing new AI hardware, while better integration could benefit customers using both companies' services and components.

The core of the deal, however, is in chip design collaboration. Amazon will expand its use of Synopsys' designs to include blueprints of application-optimized IP: silicon designs that it can incorporate into its own chips. It will also take advantage of Synopsys' AI-powered engineering software to accelerate its development of custom AI chips and AWS infrastructure hardware.

Considering Amazon already markets its Graviton 5 for CPU-intensive agentic AI workloads, accelerating the development of next-generation designs could help further cement Amazon's position as it looks to compete with Nvidia on AI data center deployment.

In the announcement, Amazon also cites its Trainium chips for AI training, and Nitro for cloud security, networking, and storage, suggesting the collaboration between the two companies could augment multiple chip lines.

Taking a step back from the chip design process, this deal will also see Amazon and Synopsys collaborate on better applying AI within their own workflows, optimizing the silicon-to-system process to make chip design faster and more efficient. Amazon will deploy Synopsys' AI-powered EDA, physics-based simulations, and agentic AI solutions to improve the capabilities of its engineering teams. The companies claim this will help them design, analyze, optimize, and validate new chip designs more efficiently.

"As our chip designs grow more ambitious and AI reshapes the engineering process itself, Synopsys helps us move faster across the design cycle, helping us deliver more capable, efficient computing for customers worldwide," said Amazon SVP Peter DeSantis in a joint press release.

With Synopsys and its competitors Cadence and Siemens all pushing for faster, more autonomous chip design, we may see a shortening of the typical design cycle for new enterprise hardware. If that proves true, keeping up with that new pace will be paramount for companies like Amazon and Synopsys.

It goes both ways

Alongside Amazon's expanding use of Synopsys technologies, Synopsys itself will adopt AWS compute and storage services to accelerate its own IP and software development efforts. It will also use Amazon Bedrock to build and deploy AI applications and agents to develop its own product offerings.

This embeds Amazon cloud services and AI tools further within Synopsys' engineering workflows, while Synopsys' intellectual property becomes more deeply integrated in Amazon's custom silicon. With their joint plan to accelerate Synopsys multiphysics solutions on Amazon's Trainium and Graviton, Amazon's hardware could be more attractive to customers running those engineering workloads.

Growing that relationship holds further financial incentives for Synopsys, too. The IP agreement introduces a license-plus-royalty model, so as production volume increases, Synopsys royalty revenue could scale with it. With Amazon as its lead customer for the application-optimized IP, this deal could act as a strong endorsement for its chip design blueprints, making it easier to pitch its AI-powered tools, integrated with its IP, to other chip developers.

For Amazon, this partnership should go beyond accelerating its own custom chip designs. It could strengthen the case for AWS services, with optimization of Synopsys tools and services a useful benefit, as well as both companies benefiting from jointly optimizing the chip design process with agentic AI augmentation.

Faster chip design doesn't necessarily mean better chips or a shorter time to market, but if the collaboration with Synopsys helps Amazon improve the performance or efficiency of its design, even modest gains could make its AWS infrastructure more attractive and competitive.

But with no announcements or suggested timeline for new chip development as of yet, both firms will need to demonstrate the effectiveness of this partnership before anyone can measure how accurate that billion-dollar estimation truly is.

Several towns and cities in the U.S. are experimenting with Flock’s Drones-as-First-Responder (DFR) system, which employs remotely operated drones that automatically launch from rooftop docks. According to Military.com, these units are integrated into emergency services systems, which can send drones in response to a 911 call, license-plate reader notification, or gunshot-detection alert. Once in the air, these units stream live footage to dispatchers and officers in the field.

Flock initially gained this capability when it purchased drone firm Aerodome, which sells drones with high-definition video, thermal imaging, and other onboard capabilities. It’s also not the only company advertising similar services, with one startup advertising a Starlink-connected first responder drone that lets it operate outside the range of its base station.

Some of the cities that are considering, have run a pilot project, or have signed a contract with Flock for its DFR system include Fort Worth, Texas; Castle Rock, Colorado; Dunwoody, Georgia; Fulton County, Georgia; and Middletown, Connecticut. Fort Worth said that during the two-month pilot program, the drone was deployed to 162 emergency 911 calls, with an average response time of 112 seconds. It was also the first unit to arrive in 76 of these calls and even allowed the authorities to clear 11 of them without needing to send officers to the scene. Furthermore, the city said that these units only respond to calls and do not patrol the skies over Fort Worth, and that the cameras on board their drones are pointed at the horizon until they reach the scene.

The company has recently been surrounded by controversy, especially when it comes to the misuse of its system and the apparent lack of security, despite the company maintaining that it has never suffered a data breach. Despite that, Middletown Police Chief Erik Costa was quoted telling CT Insider, “The drone’s purpose is not surveillance; it’s first response.” He also added that drone footage from the city “would not be shared…outside the building unless there is a search warrant” and that unused video recordings “would be erased within 30 days.”

Other cities have considered and turned down offers to run Flock’s DFR service within their jurisdictions. Berkeley, California, said no to a $2-million proposal that would have expanded its existing license-plate cameras to include drones, more cameras, and software, while Bridgeport, Connecticut, rejected a $500,000 lease for two DFR drones. Even the mayor of Middletown declined to sign the contract despite the city having approved a trial earlier this year. The most common reasons cited for these refusals include privacy concerns and fears that the data gathered in their areas of responsibility could be shared with or accessed by federal authorities without a warrant, even as Flock claims that it does not work with U.S. Immigration and Customs Enforcement (ICE).

While emergency responders find DFR and other similar services useful, the primary reason people oppose Flock’s service isn’t the drone units themselves, but the lack of trust in the company and the institutions that use them. So, unless the authorities address the underlying concerns, it’s likely that any type of surveillance — whether strapped to a pole or flying overhead — will always face some kind of resistance from the public.

Nvidia is tailoring its popular DGX Spark platform and the GB10 SoC to better fit the realities of today's AI models and the broader silicon supply crunch. The company is introducing a 64GB version of the Spark that's meant to be more affordable to local AI trailblazers who just don't need 128GB of RAM.

Back when we first began exploring the capabilities of the DGX Spark and similar systems, the general assumption was that lots of RAM would be necessary to hold the most intelligent models one might want to run, and so unified memory systems built around AMD's Strix Halo, Nvidia's GB10, and Apple's M-series chips could all be configured with 128GB of memory or more.

But the AI field moves fast. Highly intelligent dense models like Qwen 3.8 27B can now fit comfortably within 32GB of RAM (albeit with limited context), so the original Spark's 128GB of memory isn't essential for local inference alone. And skyrocketing RAM prices mean that a chip that’s permanently paired with too much costly LPDDR5X is more of a barrier to entry than an asset. So a more affordable Spark with less RAM makes sense for those who need the capabilities and supporting software stack of Nvidia's GB10 Superchip and can live with less memory.

A "more affordable" DGX Spark is of course relative in today's market. 64GB GB10 systems from Acer, Asus, Dell, Gigabyte, HP, and MSI are slated to start at $4999 when they launch on October 23. Given the ever-shifting prices of memory and storage right now, they might not stay there for long. Assuming you can find a 128GB GB10 system in stock, you can expect to pay roughly $7000 to $9000 for one right now, far above even Nvidia's adjusted MSRP.

Memory capacity change aside, 64 GB Sparks will retain the same ConnectX 7 RDMA NIC as their 128 GB stablemates, meaning that they can still be clustered to boost memory capacity and inference performance if a user does eventually outgrow the bounds of 64GB of RAM. The same 20-core Arm CPU complex from the original Spark carries over unchanged, as does the 273 GB/s of shared memory bandwidth for the CPU and GPU.

To help make use of that ConnectX 7 NIC, Nvidia is also making it easier to join Sparks together with a new software tool, the Nvidia Sync Cluster Assistant, that automates the process of yoking those systems together. (Sync is a remote connectivity and management app for the Spark that allows users to use their Spark’s AI horsepower from other Macs and PCs.)

That tool supplements a variety of community-developed utilities that have sprung up over the past year to make clustering a more seamless process, as we used in our experiments with clustering two Dell GB10 systems together earlier this year.

Nvidia is also adding a feature to Sync called Model Launcher that automatically downloads and starts models like the aforementioned Qwen 3.8 27B across one or more systems and links them to the OpenCode browser-based coding agent so that users can start using their local token factories right away for agentic workflows.

The 128GB DGX Spark and Spark-alikes will remain available for those who need space for larger models and for scaling across clusters, or for more memory-hungry local AI work like model fine-tuning, so there’s basically no downside to this addition to the DGX Spark lineup. In today’s volatile market, it’s good to have options, and a 64 GB Spark gives local AI enthusiasts another choice to better fit their budgets and workflows.

OpenAI’s new Jalapeño ASIC is being deployed internally alongside AMD EPYC Turin hosts, each with 1.5TB of memory. SemiAnalysis described the rack-scale deployment of Jalapeño following the reveal of the chip, which we asked Richard Ho, VP and Head of Hardware at OpenAI, about in an interview. Ho told us that the decision to use Turin was “pragmatic,” describing Nvidia’s new Vera CPU as “a little bit behind… on that maturity level.”

“The way we approached that design was really in terms of de-risking and being able to do that design fast. Vera, as a standalone, is a little bit behind on that maturity level. The Turing device is strong. It did what we needed to do, and partly our partners had some experience with it,” Ho told Tom’s Hardware Premium. “For the Jalapeño program, we were trying to make very pragmatic decisions. We wanted to be aggressive on the goals of the performance and the cost, but we didn’t want to take unnecessary risks. That felt like a good design decision that would fit within the parameters of how we make these design decisions."

There are many Arm-based CPUs on the market, many of which are deployed internally at different hyperscalers, such as Google Cloud’s Axiom and AWS’ Graviton, but Vera and Arm’s own AGI have been described as agentic CPUs, purportedly accelerating the complex reasoning involved in agentic loops compared to their x86 counterparts from AMD and Intel. We asked Ho about this dynamic, and why Turin was the right choice given the close working relationship between OpenAI and Nvidia, who responded with the quote above.

Arm has long touted the virtues of the AArch64 ISA compared to x86, going as far as to claim that its own AGI CPU provides more than twice the performance of modern x86 platforms. That claim is based on internal estimates, not real benchmarks, however. The company has been naturally bullish on AGI’s adoption in the market, though even with an impressive $2 billion in commitments, analysts say the market penetration will still be in the low single digits after two years.

With the explosion of server CPU demand this year and evolving agentic workloads, Nvidia has been at the forefront of messaging, comparing its Arm-based Vera to the x86 competition, boldly claiming a 1.8x improvement over a competing AMD Turin chip; a figure that’s extracted from an overall benchmark suite showing Vera just 3% ahead of AMD’s EPYC 9755.

Many semi-custom Arm designs, from Graviton to Azure’s Cobalt, have focused on core density and efficiency for cloud workloads. Vera and AGI are a shift toward messaging against peak performance. In early Vera benchmarks, Nvidia’s CPU looks impressive, though we’ve yet to see the chip in action in a wide variety of workloads, much less benchmarked against AMD’s upcoming Venice CPUs or Intel’s Diamond Rapids.

What’s interesting about OpenAI’s decision to use Turin is that it’s one of Arm’s deployment partners for AGI, as well as among the list of customers “exploring the Vera CPU,” according to Nvidia. OpenAI, other frontier labs, and hyperscalers keep a wide variety of hardware in their fleet. Pairing Jalapeño specifically with Turin came down to reducing risk, with Ho pointing to the maturity of the platform.

Broadly, x86_64 is more mature than AArch64; ARM has been around since the mid-1980’s, though a proper 64-bit extension didn’t arrive until 12 years after it was introduced on x86. And although Arm has worked its way into data centers over the past two decades, there’s still a much deeper x86 foundation reaching back several decades.

It doesn’t seem Ho’s comments were specifically on the delineation between AArch64 and x86, however. Rather, the comments are focused on the dynamic between Turin and Vera, with the latter being Nvidia’s first foray into a custom CPU core for the data center. Although the early benchmarks of Vera are impressive, it nonetheless represented an unnecessary risk for OpenAI’s rack-scale Jalapeño deployment.

Ho’s comments about OpenAI’s partners having experience with Turin expose where the thinking for the hardware team was at, as well. There are some differences in the practicality of servicing the host system between Turin and Vera, most notably the fact that Vera is board-mounted while Turin chips are socketed; swapping chips isn’t common in a server regardless, but it’s easier with Turin.

Given that OpenAI has a fleet of hardware available, it’s possible Jalapeño will be deployed with a different host system in the future, potentially paired with Arm’s AGI or Nvidia’s Vera. For now, however, Turin was the right choice.

Good Wi-Fi is essential around the home, but there's no guarantee that your ISP-provided router is getting its signal where you need it to go. If you're sick of lag, Wi-Fi dropping out, and slow speeds, you'll want to consider this limited-time deal on a TP-Link router. You can pick up the TP-Link Archer BE550 for $169.99 right now, saving you 32% off its list price.

● Check out this TP-Link router deal on Amazon

This is a tri-band Wi-Fi 7 router with no less than six antennas to deliver maximum coverage across a typical household in the U.S. This gives you a combined maximum theoretical speed of 9.3 Gbps, split across the 6 GHz band for Wi-Fi 7, along with 5 and 2.4 GHz. If you're using a recent-enough device, Multi-Link Operation (or MLO) means you're able to use different bands, like 5 GHz and 6 GHz, simultaneously for faster speeds and greater reliability.

While a lot of modern routers skimp out on high-speed Ethernet ports, you're getting a full set of 2.5 Gb Ethernet connectors here with the BE550, which is also known as the BE9300. One is for WAN, so your internet connection, along with four for other local devices you'll want to hook up, like your gaming PC. Even if your PC is a little older and doesn't feature a 2.5 Gb Ethernet port, it'll be able to hit the maximum speeds for that port with these connections.

You've got everything else you'd expect from a modern Wi-Fi 7 router here, too. It has a USB port for sharing an external hard drive on your local network, for instance. It also includes support for a mesh-style network, along with per-device Quality of Service controls, meaning you can set up priority for traffic on certain devices on a congested network. For instance, if you've got one PC downloading while you're trying to play games, you can prioritize one over the other with QoS. It also supports tagged VLANs, meaning you can configure your network with separated virtual networks for different devices to keep them isolated.

The $169.99 sale price for this TP-Link Archer BE550 is a good one for a router that can add Wi-Fi 7 support, along with a whole host of other features, to your local network. If you're struggling to get a signal and want an upgrade, this router is a good option, but with this limited-time deal set to run out in only a few hours, you don't have long to grab it at this price.

We're leading up to the biggest sales season of the year, and we're already seeing a large selection of discounts across the board, especially on Amazon. If you're a content creator, streamer, or even a productivity master or coder, then you're fully aware of shortcuts and macro pads. One of the most popular brands in this space is the Elgato Stream Deck, and today, you can pick up the largest of these macro pads with a stunning 20% discount. The Elgato Stream Deck XL is reduced to $199.99 from its original $249.95 list price. So if you want a massive 32-key macro pad to display all your favorite shortcuts, then this could be the deal for you.

● Check out this deal at Amazon

Verging on keyboard territory, the Stream Deck XL sports a large 32-key setup, with the keys positioned in an angled stand for ease of use and viewing. The height of the unit makes it ideal for placing in front of you and under your monitor. For streaming, the Elgato Stream Deck can let you control everything from your scene selections, sound, lighting, sound effects, and more. If you can make a shortcut or macro for it, you can save it to a key on the Stream Deck. Plus, each key has an individual LCD that lets you set a custom image to represent whatever's currently mapped to it, making it easy to select the function that you're after with just a quick glance.

The Stream Deck can help to supercharge your workflow, assigning a multitude of functions and commands to its customizable macro pad, giving you access to endless possibilities at your fingertips. The Elgato Stream Deck works with a large variety of popular apps, including Twitch, OBS, Meld Studio, Discord, YouTube, and more. You can even use Smart Profiles so that your Stream Deck will change the available buttons, depending on which app is currently open on your Mac or PC.

You don't have to use the Stream Deck purely for streaming; it's also a useful tool for PC gamers who want to add in-game macros and commonly used shortcuts to easy-to-see buttons. This can be super useful if you're using a smaller 60% gaming keyboard with minimal buttons for assigning macro functions.

Take advantage of the discounts and save $49 on the most complete macro pad that Elgato has to offer, with the Stream Deck XL now only $199.99 at Amazon

Micron released its fiscal Q4 2026 financial results on Wednesday, expectedly setting another record for revenue with $54.23 billion for the quarter, up nearly five times since the same period last year. Micron also hit a record 87% gross margin, the largest contributor to which was Micron's Mobile and Client (consumer) business unit. Further, Micron's client business was the only one that shipped less memory last quarter, despite bringing in the highest operating margin at 88%.

Micron's margins overall are up significantly year-over-year; Core Data Center surged from 25% to 85%, and Automotive and Embedded have climbed from 20% to 79%. The Mobile and Client unit also saw significant growth, with a 29% operating margin in fiscal Q4 2025, and now a margin of 88%. Cloud Memory margins grew, though not to the same extent as other units, moving from 48% to 76%.

The gross margins are interesting to look at, too. In a financial statement, the gross margin equals revenue minus cost of goods. Operating margin, on the other hand, equals revenue minus cost of goods and all other operational expenses. The operating margin simply excludes taxes and interest.

The gap between gross and operating margin is what's interesting in the breakdown above. The Mobile and Client unit has the smallest gap, with only a 2% difference. The Core Data Center unit matches the Mobile and Client unit with a 90% gross margin, but it has a lower operating margin at 85%. In other words, the cost of running Micron's consumer business is extremely low relative to the amount of revenue it currently generates.

It also saw the lowest amount of growth for the quarter, with revenue up 14% quarter-over-quarter. Micron says this was "driven by higher pricing, partially offset by lower bit shipments." Out of Micron's four business units, the Mobile and Client unit was the only one that shipped less memory in the past quarter.

For clarity's sake, a bit shipment is just a shipment. Micron measures shipments in bits rather than units. It's the capacity Micron has shipped in a quarter, not the actual number of DRAM or NAND chips.

Outside of Mobile and Client, Micron attributes higher revenue to higher pricing and bit shipments. The situation in the Mobile and Client unit echoes a sentiment we've heard elsewhere: growth in the consumer market is still happening, but it's driven by higher pricing, not growth in shipments.

Earlier this year, Intel's David Zinsner, chief financial officer, attributed a 13% YoY growth in Intel's consumer business to higher average selling price (ASP), not increased unit sales. Zinsner clarified that Intel "thought we had seen some inflation on our cost and needed to pass that on to the end customer."

Micron's outlook for fiscal Q1 2027 has revenue set at another record of $61.5 billion ± $1.5 billion, with gross margin rounding from 87% in fiscal Q4 2026 to 86.25%.

Another record quarter signals more squeeze on the DRAM and NAND markets. Echoing rival SK hynix, Micron says it expects memory and storage to be "much tighter" in 2027 and 2028, claiming that the company expects the industry to be supply-constrained in both years. That's despite a growth in supply that Micron anticipates.

The company says it expects industry NAND bit shipments to grow in the mid-20% range, and DRAM bit shipments to grow in the low-20% range. The industry broadly will produce more memory over the next two years, says Micron, but that still won't keep pace with demand.

Worrying still is this line in Micron's earnings slides: "we do not have line of sight to when supply and demand will return to balance." However, the company says it plans to increase its capital expenditure (capex) in fiscal 2027 compared to its prior estimates.

A "majority" of that increase is set aside for construction of additional clean room space, which Micron has set for 2028 through the end of the decade. Hopefully, that additional clean room space coming online will ease supply constraints, but we're still a long way from that point.

A Micro Center customer who bought an RTX 5090 in Tustin, California, was required to sign an Advanced Computing Product Purchaser Declaration form, which bars exporting the GPU, before being approved to purchase the GPU. According to u/Krothic’s post on the r/Microcenter subreddit, they bought a PowerSpec AI90 Workstation with an AMD Ryzen 9 9950X CPU, an RTX 5090 Founders Edition GPU, 64GB DDR5-6000 RAM, and a 2TB SSD from the retailer and were surprised when the manager required them to sign the form.

The form requires a prospective RTX 5090 buyer to give their full legal name, residential or business address, government-issued photo ID, phone & email, actual end user (if different from purchaser), location where the product will be installed, and its intended end use. It also says, “NO EXPORT - NO EXCEPTIONS. Micro Center will not sell this product to a purchaser who intends to export, reexport, resell, transfer, or otherwise cause it to leave the United States, regardless of destination.” Micro Center did not mention why it recently began requiring the form, especially as other commenters in the Reddit post said that they weren’t required to do so previously. However, one other person said in the thread that they had to fill out a similar but longer form when they bought a GPU from Central Computers last summer.

Even though GPUs that have the processing power of RTX 4090s or higher were first banned for export to China in November 2023, U.S. authorities only started seriously clamping down on smuggling operations recently. One major arrest, so far, is that of three Supermicro employees, including its co-founder, Yih-Shyan “Wally” Liaw, who are suspected of illicitly shipping $2.5 billion worth of Nvidia hardware to China. There have also been anecdotal reports of people buying used RTX 5090s in the U.S. en masse, arriving with testing rigs built into their cars and paying sellers in cash.

It’s unclear how these Micro Center forms will stop or prevent the smuggling of RTX 5090 GPUs, especially as they don’t list the serial number of the GPU being sold. The policy is possibly a deterrent for those who casually purchase high-end equipment like this and sell it to another buyer at a higher price, whether that buyer plans to use it locally or export it illegally.

After raising prices on some Raspberry Pi models in April, the company is back with a second round of price hikes. Citing the ongoing rise in memory prices, this time, the 2GB versions of the Raspberry Pi 4 and Raspberry Pi 5 are the stars of the show, with price increases of up to 23%.

The Raspberry Pi 5 2GB and Raspberry Pi 4 2GB initially dodged the bullet when price hikes hit the other models in April. However, given the ongoing price adjustments, it was inevitable that these models would also see a price increase. The ongoing shortage of LPDDR4 and LPDDR5 memory is not improving, as global demand has remained steady and supply remains too low. The cost of producing Raspberry Pi continues to climb, and price hikes are unavoidable.

The Raspberry Pi 5 2GB launched two years ago at an attractive price of $50. The device gradually increased in price and eventually stabilized at $65 for an extended period. Despite efforts to keep costs low, Raspberry Pi CEO Eben Upton recently confirmed that maintaining the previous pricing for lower-capacity models is no longer feasible. The Raspberry Pi 5 2GB will now sell at $77.50, a 19% increase over the previous price. Over its lifetime, the Raspberry Pi 5 2GB has increased by 55% from its original launch MSRP.

Raspberry Pi 4 and Raspberry Pi 5 Pricing

The Raspberry Pi 4 2GB is now set to follow in the footsteps of its successor. When the Raspberry Pi 4 2GB was first introduced, it launched at a competitive price of $45, which was later reduced to $35 as memory prices fell. Before the most recent round of price hikes, the single-board computer had reached $55. Following the latest price adjustments, the cost of the Raspberry Pi 4 2GB jumps to $67.50, a 23% increase. In retrospect, users are paying 50% more than the original launch price or a staggering 93% more than the lowest adjusted price, depending on the reference point.

Logically, the highest-memory-capacity models suffered the most across 2026. Models such as the Raspberry Pi 5 16GB, Raspberry Pi 5 8GB, and Raspberry Pi 4 8GB have posted staggering price hikes up to 83%, 63%, and 53%, respectively, compared to their original MSRPs. Raspberry Pi has frozen pricing for the 1GB models at MSRP, but it remains to be seen how much longer the company can sustain this.

If memory is the most expensive component on the Raspberry Pi, one intriguing solution might be for the company to offer a barebones version of the single-board computer. A DIY variant could appeal to hardware enthusiasts with the skills to solder their own memory to the board. Offering an official barebones SKU could alleviate some pricing pressures. However, Raspberry Pi’s most recent firmware updates have effectively ruled out this path by locking devices to their official factory capacities.

PewDiePie, the popular YouTuber, has unveiled Ajax, his “uncensored AI model” built to run at home, and says in a new video that OpenAI banned him twice while he was making it. According to the OpenAI email he shows, one ban was explicitly for “distillation,” the practice of using one model’s outputs or reasoning to train another. Ajax is built for Odysseus, his self-hosted AI workspace, as an autonomous, always-on agent and assistant.

The setup is similar to OpenClaw and Nous Research’s Hermes Agent. Ajax, a fine-tuned Qwen3.5-9B model, powers the workspace to handle “daily tasks from search to browse the web to email to your calendar,” all “completely privately,” according to its Ajax page. The page says its refusal has been removed for a “freer, less restricted AI experience” and asks users to use it responsibly. The video announced the first version, using Alibaba’s model, as a follow-up to his workspace launch in May.

Addressing the bans, the creator stated he wanted to “distill just a little bit” in order to improve his model. Frontier AI model companies such as OpenAI and Anthropic make sure to hide their reasoning in numerous ways, including encryption and attempted distillation; such a campaign OpenAI recently attributed to actors linked to Moonshot AI is seen as a threat.

PewDiePie got his account reinstated once, although on the second ban, which came after he ran the model again “to create my seed data,” he asked, “How did they even know?” The email offers no specific examples. “They worked hard stealing all of our data to make their AI machines,” he added, suggesting that he feels some distillation may be no different from how the models are trained.

To "uncensor" his model, he relied on automatic abliteration through the open-source Heretic, where you discover prompts the model refuses to trace and remove the refusal, preferably without doing “brain damage” by removing too much. He chose to draw the line at harming other people or oneself. On his lawyer’s advice, he said it is “not designed to provide dangerous actionable instructions.” He uses nuclear weapons as an example, but it’s unclear what the real limits might be.

The creator “strongly believe[s]” in small models trained “for a specific harness,” and suggested them as an alternative to the “one trillion parameter” models, referring to rumored frontier sizes. “I think it makes no sense to poke that giant beast” just for basic tasks, he said. His interest remains in research and providing a usable model, which also means smaller. He followed up with a plea for fans to donate training data as a superior alternative to collecting it directly from Odysseus users, though he says no one submitted any.

He said he planned two more days of reinforcement learning before rerunning the decensoring step, quantizing, and benchmarking. “I can’t wait to release the next version of Ajax and Odysseus,” he added, noting that Odysseus has “gotten a huge update as well.” The naming convention makes sense, as Ajax was Odysseus’ rival in the classic myth, although he jokingly states it’s named after the cleaning product. The capabilities of open models and harnesses point the way forward for users to use uncensored, less restricted intelligence on their own hardware, an important goal in keeping the technology free.

California Gov. Gavin Newsom has signed into law a bill that requires 3D printer manufacturers to include firearm-blocking technology in their products once an industry standard is published by ASTM International. This makes California the second state to enact firearm restrictions on 3D printers following New York. Two other states, Colorado and Washington state, also have similar bills that would require “blocking features” moving through state legislatures.

Since the law only affects 3D printers that have yet to be sold, existing 3D printers do not need to be retrofitted with firearm-blocking technology, addressing one of the major concerns of the 3D printing community. It’s also not a straightforward ban on non-compliant printers and only regulates the sale and transfer of 3D printers, not their ownership or use.

The application of firearm-blocking technology on 3D printers will only be required for manufacturers once ASTM International, a non-profit organization that sets voluntary standards across different industries, publishes or adopts a standard for firearm-blocking technology for 3D printers. The law mandates the California Department of Justice (DOJ) to check for a standard quarterly, starting no later than July 1, 2027, until July 1, 2029. If the organization hasn’t published a standard by that date, then the DOJ is no longer required to check for it, meaning the process to write the guidelines or regulations that manufacturers need to follow to sell 3D printers in California might not even begin.

There are currently two primary ways to detect if a print file is intended for a 3D-printed firearm — it could either be matched against a known database of gun files or analyzed by a trained model to determine if the design could potentially be used for a firearm. However, both techniques suffer from shortcomings. The former could easily be defeated by just modifying the file, like changing its size or splitting it into multiple pieces for later assembly, while the latter is prone to false positives and could prevent the printing of barrels, brackets, levers, tubes, and other parts that may look similar but have a totally different, legitimate use. This is one of the reasons why the Electronic Frontier Foundation said in June 2026 that “there is no world where the mandated technology actually works as intended.”

The California law attempts to address these concerns raised by 3D printing enthusiasts by mandating the DOJ to base its implementation on standards set by ASTM International instead of defining it itself. So, until the standards group publishes or adopts one within the set timeframe, the requirements for manufacturers to build the technology into their 3D printer models would not come into play.

Embedded and industrial computing specialist Aaeon has launched a fanless mini PC featuring up to an Intel Core Ultra X7 358H processor. The PC, dubbed the Up Xtreme PTL Edge Air, utilizes Frore’s AirJet ultrasonic membrane technology for active cooling.

The use of this cooling tech means that the Up Xtreme PTL Edge Air is virtually silent, operating at less than 21 dBA. Moreover, Aaeon boasts that it has allowed its mini PC designers to make this device “35% thinner and 43% lighter” than a traditional heatsink and fan-cooled model.

The Aaeon Up Xtreme PTL Edge Air doesn’t look very much like a regular consumer mini PC. That’s because it is designed for the embedded and industrial market. Aaeon mentions that this model might be purchased “for integration in space-constrained applications such as ultra-thin kiosk setups, low-clearance AMR housing, and compact mobile healthcare units.” Besides being virtually silent, thin, and light for those use cases, Aaeon touts the appeal of “its elimination of fan-induced vibration” in those target markets as well as robotic controllers, actuators, sensors, and cameras.

The I/O makes it clear this isn't a consumer-grade system. In addition to the commonly seen USB-A and USB-C ports, LAN, audio, and various video outputs, the Up Xtreme PTL Edge Air packs in a pair of COM ports for RS-232/422/485, and a 40-pin GPIO. This mini PC can be connected to up to four displays simultaneously.

Three Up Xtreme PTL Edge Air can be specced with an Intel Core Ultra X7 358H, Ultra 7 356H, or Ultra 5 325 processor. The X7 model features the potent Intel Arc B390 iGPU with 12 Xe-cores. Inside you can fit two DDR5 SODIMMs up to 128GB total and speeds of 7,200 MT/s. Two M.2 2280 SSDs can be fitted, and an M.2 2230 slot is earmarked for the wireless card.

We see a couple of sacrifices made to go slim, light, and quiet with the AirJet cooler. Specifically, Aaeon says that there is “a 6% increase in power consumption and a narrower operating temperature range of -10°C to 45°C” with this design.

As is typical for this kind of business-facing announcement, we don’t get any clear pricing. Normally, businesses will contact the likes of Aaeon directly with pricing inquiries based on how many units and so on. If you do manage to get a hold of one of these UP Xtreme PTL Edge Air mini PCs, you can run Windows IoT Enterprise or Ubuntu 24.04 LTS on it, according to the spec sheets.

The only other mini PC we've seen cooled using AirJet cooling is Zotac's ZBox PI430AJ mini PC, which was built around Intel's low-end N300 Atom CPU with a TDP of 7W.

Nvidia has just launched the Nvidia Open Agent Safety Platform — an open software platform and reference system design — to govern and secure autonomous AI agents. Announced on September 28, 2026, the platform is designed to establish strict security barriers outside of AI models’ application layer, preventing agents from escaping their sandboxes, executing unauthorized code, gaining unauthorized access to critical infrastructure, or bypassing guardrails.

The launch follows months of calls for AI regulation from several industry players, which intensified in September after several reported incidents in which AI models broke out of their test environments and went rogue. A recent flurry of such incidents has prompted calls to slow AI development, with OpenAI outright halting the training of new models. One former Anthropic and OpenAI researcher even declared that people building frontier AI “earnestly believe that it could kill us all by the end of the decade. In a somewhat surprising move, leaders of the companies developing these AI models have joined the calls to regulate AI or slow development.

However, not everyone agrees with this approach. Nvidia CEO Jensen Huang has consistently pushed back against government-mandated regulation, broad restrictions, or treating AI safety as a “doom theory”, openly criticizing apocalyptic warnings from competitors like Anthropic and OpenAI as “odd”. He argues that AI safety is an infrastructure problem with concrete physical parameters, not an abstract, speculative issue that requires policies. Therefore, the solution, according to Huang, is better engineering.

The Nvidia Open Agent Safety Platform appears to be the physical manifestation of that exact philosophy. So, how exactly does the platform work? Who is it for? And is it really the answer to the AI safety problem that is causing growing concern across the industry? Much of the industry’s debate over approaches has focused on curtailing self-acting rogue agents, but hardly touches on the safety implications of AI being a powerful tool in the hands of threat actors.

What’s all the fuss about?

The speed of AI’s development has prompted concerns about whether sufficient guardrails are in place to curb the risks of such a powerful technology. One aspect of these concerns — rogue agents — has been validated by several incidents in which AI agents broke out of their roles during testing and executed unauthorized actions. OpenAI agents have gained unauthorized access to various government websites, including the Securities and Exchange Commission and the Census Bureau websites in the U.S., as well as an Australian health and social payments portal.

In several other episodes, models have bypassed guardrails, set up message boards, escaped sandboxes, hijacked websites, self-prompted, uploaded user data without permission, and secretly communicated with each other. A recent Axios report claims that leading AI labs are currently investigating tens of thousands of such incidents, most of which happened during testing and experimentation. Several rogue incidents have also occurred outside test environments. For example, earlier this year, a Claude-powered AI coding agent deleted a company's entire database in 9 seconds.

These incidents have culminated in growing calls for regulation across the industry. Anthropic CEO Dario Amodei recently published an essay that centers around calls to “slow the pace” of AI development and the importance of regulation, warning that a potential AI-powered botnet swarm could take over the entire internet. In its recent IPO prospectus, Anthropic listed “existential risks to humanity” as one of its risk factors, dedicating one third of the 261-page document to describing what could go wrong.

Nvidia’s Jensen Huang disagrees with both such apocalyptic predictions and the use of regulations as a solution. While he doesn't dispute the critical need for guardrails, he argues that better engineering, not broad legal regulations, is the right approach. Putting his money where his mouth is, Huang’s Nvidia has launched the Nvidia Open Agent Safety Platform.

The Nvidia Open Agent Safety Platform

Built in collaboration with about 100 industry partners, Nvidia's Open Agent Safety Platform “brings together industry, researchers, and public-sector organizations to set safer boundaries for AI Agents, share best practices and foster international cooperation to raise the bar for safer AI agent deployment.” It combines Nvidia OpenShell—an open-source secure runtime that sandboxes agents and enforces operator-defined policies—with Nvidia Sentry, an independent watchdog reference design that runs on Nvidia’s BlueField-4 DPUs and enforces security policies at the silicon level.

OpenShell sets sandboxed environments, outside of the model and agent harness, with kernel-level isolation to govern what an agent can see, interact with, and execute. Even if the agent breaks out of the model's boundaries, it cannot go beyond OpenShell’s. Sentry, on the other hand, uses hardware-level telemetry to continuously monitor agent behavior and isolate rogue workflows from outside the agent’s software environment. If an agent attempts to move beyond its software boundary, Nvidia claims that Sentry can quarantine and stop it in milliseconds.

The platform is aimed at developers and enterprises deploying increasingly autonomous agents across data centers, workstations, and even robotic systems, and can work with both open and closed models. While OpenShell is optimized for Nvidia Vera — a purpose-built CPU for agentic AI — it can also be extended to third-party compute platforms from Arm and Intel, as it's open-source.

Nvidia's industry partners in the initiative include AI Labs and frameworks, security and identity providers, enterprise platforms, and hardware and infrastructure companies, with several partners already incorporating the platform into their ecosystems. SpaceXAI is using the platform with Cursor coding agents and Grok models, while Anthropic is integrating OpenShell and BlueField with Claude Managed Agents to add another layer of control.

Scale AI is incorporating the technology into its agentic infrastructure for enterprise and government customers. Similarly, Salesforce and Nvidia have also integrated OpenShell with Slack, allowing users to view agent activity, audit events, and approve or reject requests for additional permissions directly from Slack.

SAP, meanwhile, is embedding OpenShell into its Joule Studio runtime, contributing engineering work to the project, while also working with Nvidia on interoperability standards through the Open Secure AI Alliance. Robotics companies, including Figure, Gecko Robotics, and Skild AI, are also building with OpenShell to add similar controls to autonomous systems operating in the physical world.

The broader picture

It's somewhat surprising that the strongest voices calling for regulation are the leaders of the very labs developing the AI agents. On one hand, having the people at the forefront of development call for restraint adds validity and urgency to the concerns. On the other hand, skeptics say it might all be part of a broader “self-serving” agenda that is part marketing for the models' capabilities, part a ploy to influence whatever regulations end up being made, and part an attempt to slow down China's AI development even further. In fact, Anthropic, OpenAI, SpaceXAI, and Google are now facing an antitrust lawsuit for agreeing to slow AI development, with the plaintiff explicitly calling the move “self-serving.”

U.S. President Donald Trump appears to strongly agree with this view, saying that a “sick conspiracy” was underway to undermine. Trump announced plans for an “AI force” that will “cherish” AI and watch over it. Jensen Huang also finds calls for regulation strange. He says he is not outright against regulations but calls the recent clamoring a “distraction.” Huang argues that you cannot rely on the AI model to regulate itself or pass alignment tests. If an agent encounters a bug, it will naturally try to bypass standard software code to achieve its goal.

Huang's advocacy, however, cannot be viewed as completely altruistic. Broad restrictions on AI or a halt in development will likely reduce sales for Nvidia, whose AI accelerators power most AI models. What's more, the company's CEO has always considered rogue AI as a cybersecurity and networking failure, and is now positioning the Open Agent Safety Platform as the required infrastructure solution for the entire industry.

AI’s extraordinary potential for society will only be realized if we solve AI safety,” said Huang. “As we continue to discover the frontier of AI capabilities, we must accelerate discovery at the frontier of AI safety. Safety and security require full-stack engineering. Nvidia Open Agent Safety Platform brings together industry, researchers, and public-sector organizations to share best practices, align on evaluation methods, and foster international cooperation. Together, we can raise the bar for global AI safety.”

None of these appear to address an arguably more concerning aspect of AI safety: extremely powerful tools falling into the hands of bad actors or being used in applications that blur ethical lines. For example, blockchain-assisted cyberattacks have risen 440% since the launch of Chinese open-source AI tools that do not restrict the generation of malicious code and lower the knowledge barrier to launching cyberattacks. Elsewhere, researchers used AI to create 16 new viruses that never existed in nature. While the specific study was controlled medical research, it shows that highly dangerous applications are possible. Experts worry that such studies are way ahead of necessary guardrails and regulations.