A malvertising campaign on the Bing search service is pushing a fake Claude desktop app installer hosted on a legitimate Claude.ai domain to deliver the SectopRAT malware.

At least 29 organizations were compromised between July 21-22 during the malicious operation, which researchers call FakeAgent.

The attacker uses a malicious Claude Artifact hosted on Claude’s legitimate domain, which is a common tactic that has been used 

The attackers used a malicious Claude Artifact hosted on Claude’s legitimate domain, a tactic that has been used at the beginning of the year to push macOS malware via ClickFix lures.

Researchers at managed security company Huntress found that the malicious Claude Artifact, downloaded 7,100 times before Anthropic removed it, directed visitors to websites that hosted a fake installer named ClaudeDesktop.exe.

However, the file is a legitimate JetBrains Chromium component that sideloads a malicious DLL (libcef.dll) to deliver the SectopRAT remote access trojan with info-stealing capabilities.

Persistence on the system is achieved through another executable named DockerDesktop.exe, which installs a scheduled task.

Huntress says that the various loaders and staging components used in the infection chain feature anti-analysis mechanisms, including VMProtect packing, shader timing checks, GPU and VRAM checks, and virtual machine (VM) detection.

The SectopRAT malware was recently observed being distributed via CastleLoader campaigns and ClickFix attacks.

The malware uses the EtherHiding technique to retrieve a working command-and-control (C2) address via Ethereum BNB Smart Chain transactions.

SectopRAT, also known as ArechClient2, has been active since 2019 and is an information-stealer with HVNC (Hidden Virtual Network Computing) functionality. It allows remote hands-on operations and real-time interaction with the compromised system.

The malware targets user passwords, credit card data, files, browser logins and cookies, FTP credentials, data from various messaging clients, including Discord and Telegram, Steam, and VPN products.

In an interesting twist, Huntress reports it used Claude Opus 4.8 to assist with shader emulation, cryptographic reconstruction, and .NET code analysis.

Analysis of the decrypted .NET payload (SectopRAT) helped attribute the attacks to SectopRAT operations, or a closely related fork, and opened the path to infrastructure analysis.

At that stage, the researchers found 10 domains registered to the same email address since December 2025, with one of them previously linked to the StealC distribution and seized during Operation Endgame.

Huntress does not have enough evidence to attribute the FakeAgent campaign to a specific, known threat cluster.

Users looking for software should trust official websites and download portals, instead of search results, especially sponsored ones.

Security teams log 54% of successful attacks and alert on just 14%. The rest move through your environment unseen.

The Picus whitepaper shows how breach and attack simulation tests your SIEM and EDR rules so threats stop slipping by detection.

Get the whitepaper

For months, AI giants have devised special vetted programs and strict guardrails to limit the use of their models by malicious hackers. But these limits are now hindering the work of legitimate network defenders, as well as that of offensive cybersecurity researchers. 

In June, the U.S. government slapped export control restrictions on Anthropic’s much-hyped AI models Mythos and Fable. The move was prompted at least in part by a report that claimed it was possible to bypass the models’ guardrails designed to prevent users from using them to build and execute malicious cyberattacks.

Regardless of whether the incident was really motivated by fears of a jailbreak, the fact is that Anthropic has repeatedly marketed Mythos as some kind of doomsday cybermachine that can only be given to carefully vetted users, and even then with strict guardrails in place. (The export controls on Fable 5 and Mythos 5 have since been lifted. Fable 5 returned to general access on July 1; Mythos 5 has been reintroduced only to vetted U.S. organizations as part of the government’s review process.)

That kind of gatekeeping isn’t unique to Mythos. Both Anthropic, with its other models, and OpenAI offer cybersecurity researchers programs they can apply to get vetted and — if approved — access models with fewer cybersecurity restrictions: OpenAI’s Trusted Access for Cyber and Anthropic’s Cyber Verification Program. 

These guardrails have been widely criticized, particularly by researchers whose job is to find unknown vulnerabilities in systems and devise ways to exploit them before criminals do.

During a recent appearance on a cybersecurity podcast, Mark Dowd, a well-known security researcher, said that, “it’s not really comfortable to me that these random large companies are making arbitrary decisions about what is safe in security and what’s not.”

Dowd has spent decades finding and selling “zero days” — previously unknown software flaws and the exploits that take advantage of them — to Western governments, rather than report them to the software makers so they get patched. Governments pay a premium for vulnerabilities precisely because they stay open, which is useful for intelligence operations.

Dowd admitted his work may make him biased, but he isn’t alone. Several people who work in offensive cybersecurity — they proactively probe systems for weaknesses — described to TechCrunch how they use AI tools and deal with their guardrails. 

Chris Anley, the chief scientist at security consulting giant NCC Group, said that asking an AI model to try to exploit a bug is a key step in confirming it’s a real vulnerability worth fixing. But if a guardrail prompts the model to refuse to answer the question outright, the guardrail hurts defenders, he said.

“This is where the whole offensive versus defensive and guardrails part comes in, because ‘fix this code’ as a prompt is both an essential mechanism for defense but also a roadmap for finding critical vulnerabilities in the code base,” said Anley. “So at the same time, the same tool is both an offensive tool and a defensive tool, and the two can’t really be unpicked.”

It’s “like a hammer,” he continued. “You can’t build a house without a hammer. It’s definitely a tool but it’s also irreducibly a weapon as well.”

When he and his colleagues run into such a roadblock, they sometimes fall back on open-source AI models that come with no guardrails at all.

Paolo Stagno, the chief technology officer at CrowdFense, a well-known company that develops, acquires, and sells unknown vulnerabilities to government agencies, agreed with Dowd, saying AI companies “essentially treat customers like children who need babysitting” with their vetted programs and guardrails. 

Stagno said he and his colleagues do use frontier models — but only for reverse engineering. They avoid using AI to help find vulnerabilities or build exploits, he said, because feeding that work into a cloud-based model risks leaking sensitive vulnerability data or having it absorbed into future training runs. For that step, he said, they use open source models run locally, as they do not rely on sharing data outside of the model. 

Giuseppe Cali, a security researcher who finds zero-days and develops exploits, said guardrails are not impeding his work. That’s because he doesn’t use AI for offensive work; instead, he uses it for initial reverse engineering, to understand the code he’s analyzing, and to build supporting tools. For that, he said, AI tools can speed up the process and allow him to focus on discovering vulnerabilities. 

“I still want to own the actual bug discovery and weaponization myself and that wouldn’t change if all guardrails were lifted tomorrow,” said Cali. “I am jealous of my bugs, and I like this game too much to let models play it for me.”

One researcher at a smartphone-component manufacturer, who spoke on condition of anonymity because he isn’t authorized to talk to the press, said his employer isn’t part of Anthropic’s CVP program and as a result, its tools are barely useful for finding vulnerabilities because the guardrails are too strict.

“If it catches wind we’re doing anything security related, it just stops and isn’t usable,” the person said. 

Chris Thompson — chief executive of cybersecurity firm RemoteThreat and founder of Offensive AI Con, an offensive security and AI-focused event — said that in his experience using the frontier AI models, the guardrails can be inconsistent and work differently every day. That’s true even inside the looser boundaries of Anthropic and OpenAI’s vetted programs. 

“I think the practical impact is you spend a lot of time negotiating with the model instead of working on the core security program,” said Thompson. “Instead of analyzing a vulnerability and reasoning through the exploitability, you’re trying to find why you’re getting inconsistent results or why are models over-sanitizing the output.” 

As a consequence, researchers rely on or get pushed toward Chinese open-source models like GLM — freely downloadable models that can be run locally with no vetting or usage restrictions — said Thompson.

“You have these responsible researchers that are being pushed away from U.S.-governed systems to foreign-owned systems,” he said. “I think it’s more harmful than good to have these guardrails in place.”

Rather than tightening restrictions further, Thompson called for the AI frontier labs to open up their programs, provide responsible access, and also hold those who abuse their tools accountable. Otherwise, he argued, defenders will lose the AI race.

“There’s this big storm coming. There’s this big wave of attacks that are going to happen at speed and scale like never before,” said Thompson. “But the same security consulting firms and legit researchers that are trying to make a difference are being stifled right now.”

Elon Musk has reignited speculation of a potential Tesla-SpaceX merger.

Tesla has missed recent analyst expectations by a large margin, reporting roughly $1.1bn in adjusted net income in the quarter past. Wall Street had expected the company to rake in around $1.9bn.

The loss is despite the Elon Musk-owned company recording a 26pc growth in revenue to a better-than-expected $28.2bn, with car sales alone bringing in more than $20bn – marking a 23pc year-over-year growth.

Electric vehicle (EV) sales jumped 25pc since last year to a little more than 480,000 units in Q2 2026. The company produced around 451,000 vehicles during the period.

Tesla stocks dropped around 1.3pc at market close on Wednesday (22 July) following the announcement and fell a further 4.2pc in after-hours trading. Company shares have been down nearly 8pc since last month and nearly 17pc in the last six months.

The loss coincides with a drop in share value for Musk’s other company SpaceX, which Tesla has close financial ties with. SpaceX’s filings from earlier in the year showed that the company purchased nearly $700m worth of Tesla’s battery storage products across 2024 and 2025, as well as more than $130m of its Cybertrucks in 2025.

Meanwhile, xAI – now owned under SpaceX – purchased $292m in Tesla battery solutions by April this year, and more than $400m last year. The two companies are also collaborating to develop semiconductors as part of Terafab.

Tesla sales bounced back the strongest in Europe, partially led by higher fuel prices, which drove consumers towards EVs.

New registrations for Tesla vehicles soared across the region, according to June figures – more than doubling in France, and seeing a 39pc rise in Denmark and a 56pc jump in Sweden.

Meanwhile, the company suffered on its home turf in the US after the government cut federal tax credits for EVs and dismantled rules that encouraged their production.

Additionally, Chinese competitors such as BYD, Nio and Xiaomi are also encroaching on Tesla’s EV market share with their more affordable yet high-tech options.

Tesla is attempting to diversify its revenue streams away from EVs (which makes up a majority of its earnings) to autonomous taxis and AI-powered humanoid robots.

“The company reports that paying customers have travelled 2.5m miles in Tesla’s robotaxis and that 380,000 of those miles have been unsupervised, with no safety monitor in the vehicle,” said Forrester VP and principal analyst Paul Miller.

“Those unsupervised miles are rising, but they’re currently a fraction of the 220m miles reported by competitor Waymo back in March.”

Tesla more than doubled its capital spending compared to Q2 last year to fund the diversification push, marking a $1.1bn negative cash flow caused by a capex increase of about $3.3bn.

Musk, meanwhile, told investors that the company aims to spend more than $25bn this year – nearly triple the $8.5bn it spent in 2025. Big Tech heavyweights are expected to commit several hundred billion dollars in capex this year alone to build out their AI ambitions.

Musk also reignited speculation of a possible Tesla-SpaceX merger at the earnings call yesterday.

“As you can tell from the many collaborations on so many fronts with SpaceX, there’s more and more overlap,” Musk said.

“We can’t ​talk about, you know, combining companies and that kind of thing on an earnings call. It’s ​got to be done with the appropriate process.”

A merger could ease matters for the otherwise struggling Tesla, which is already making a pivot closer to SpaceX with its push into AI.

SpaceX president and chief operating officer Gwynne Shotwell told CNBC in June that a combined business “might ⁠make Elon’s life a little easier” by potentially simplifying Musk’s trillion-dollar corporate empire.

Don’t miss out on the knowledge you need to succeed. Sign up for the Daily Brief, Silicon Republic’s digest of need-to-know sci-tech news.

First look: Brooklyn-based consumer electronics company Light has launched a minimalist flip phone designed to cut down on screen time without compromising on essential communication. The $299 Light Flip features a clamshell design reminiscent of the Motorola Razr devices from the turn of the century, and offers a barebones software experience without social media, web browsing, media streaming, and shopping apps.

The Light Flip features a 2.8-inch non-touch OLED display, 6GB of RAM, 128GB of storage, a 50MP rear camera, and a replaceable 1,800mAh battery. It sports an old-school T9 keypad, which used to be standard on candybar feature phones from Nokia, Motorola, and Sony Ericsson in the early 2000s.

Connectivity options include 5G, Wi-Fi, Bluetooth, GPS, and a 3.5mm headphone socket. It is available in six colors and comes with a lanyard notch, adding to its nostalgic appeal.

The Light Flip runs on LightOS, a minimalist Android skin that offers only essential apps and services, such as calls, texts, camera, calendar, music player, and an alarm clock.

As part of its core philosophy of “going light,” the device deliberately skips social media apps and web browsers, encouraging people to use their phone as little as possible. It also supports Light’s digital detox program ‘Flip Your Life,’ which aims to help users adapt to a less connected lifestyle.

Talking to ZDNet, Light co-founders Joe Hollier and Kaiwei Tang said that contrary to popular perception, the target demographic for the Flip are 18- to 30-year-olds who are tired of the 24×7 connected lifestyle. The company believes that dumb phones could make a comeback, as its internal research suggests that 52% of millennial and Gen Z consumers would consider using basic feature phones to cut back on social media consumption and escape the endless cycle of doomscrolling.

The Light Flip is now up for preorder with a $39 deposit but it’s not expected to ship until April 2027.

It will be sold unlocked for $299 from the company’s official website, and customers can also get it on a two-year, $39-a-month service plan directly through Light. Whether it will land on major carriers like Verizon, AT&T, or T-Mobile and become available with a contract remains to be seen.

Microsoft AI released two new in-house models into public preview on Wednesday — MAI-Image-2.5-Pro, its highest-fidelity image generator to date, and MAI-Voice-2-Flash, a speech model built for high-volume enterprise workloads — while publishing production data that amounts to the company’s most aggressive argument yet that it can power its own products without leaning on OpenAI’s frontier models.

The announcement, made by Microsoft AI’s Superintelligence team, lands roughly a year after the company committed to building purpose-built models internally, and it arrives with an unusual level of specificity about where those models now run: Bing, PowerPoint, OneDrive, Dynamics 365, Excel, GitHub Copilot, and Azure. The message to enterprise buyers — and, implicitly, to OpenAI — is that Microsoft’s homegrown models are no longer research projects. They are production infrastructure serving millions of users.

“Each of these enhancements is a step toward the same goal: Microsoft products, powered by Microsoft models,” the company wrote in its announcement blog.

How MAI-Image-2.5-Pro and MAI-Voice-2-Flash stake out opposite ends of the AI cost curve

The two new releases occupy opposite ends of what Microsoft calls the quality-speed-cost curve, and the positioning is deliberate. MAI-Image-2.5-Pro targets the premium tier: hero imagery, detailed editing, and precise in-image text rendering — the last of which has long been a notorious weak spot for image generation models. Microsoft priced the model at $5 per million text input tokens, $8 per million image input tokens, and $106 per million image output tokens. The base MAI-Image-2.5 model recently launched at No. 2 for image editing on Arena, the community leaderboard that has become a de facto scoreboard for generative media.

The creative industry appears to be taking notice. Rob Reilly, global chief creative officer at advertising giant WPP, called the Pro model “a strong leap forward for GenMedia tools” in a statement included in Microsoft’s announcement, adding that “Microsoft has firmly established itself among the leaders in generative AI.”

MAI-Voice-2-Flash goes the other direction. First previewed at Microsoft’s Build conference, Flash runs twice as fast as MAI-Voice-2 and costs 32% less, priced at $15 per million characters. It is designed for the unglamorous but enormous market of high-volume voice — call centers, voice agents, and real-time speech applications where latency and cost-per-call matter more than marginal gains in expressiveness. Together, the two models reflect a strategy of building families of models rather than a single flagship, because, as the company put it, a creative studio chasing maximum fidelity has very different needs from a customer service operation handling millions of calls a day.

Microsoft’s production metrics show in-house models cutting GPU costs by up to 89%

The model launches are arguably less newsworthy than the deployment metrics Microsoft attached to them — numbers that read like a systematic case for swapping out third-party frontier models across its product portfolio. 

Bing Image Creator now runs entirely on MAI-Image-2.5, end to end, marking the first time the consumer image tool is fully in-house. In PowerPoint, Microsoft says MAI-Image-2.5 reduces GPU costs by up to 84% compared with GPT-Image-2, OpenAI’s image model. In OneDrive, where MAI-Image-2.5 is now the default for key image-editing scenarios, the company reports a 26% increase in save rates, roughly 25% lower P95 latency, and 2.5 times greater efficiency under medium-utilization production workloads.

On the voice side, MAI-Voice-2-Flash now powers Dynamics 365 Contact Center — the platform used by customers including T-Mobile and EasyJet — where Microsoft claims GPU cost reductions of up to 89%. The model is also integrated into Azure Voice Live for developers building speech-to-speech agents.

Perhaps the most consequential deployment sits in healthcare. Microsoft’s Dragon Copilot, used by 170,000 medical providers and responsible for processing 28 million patient encounters last quarter, now runs on MAI-Transcribe-1.5 for its multilingual workflow across 58 languages. Microsoft says internal evaluations show a 50% relative reduction in both transcription and language-identification error rates across most languages — a meaningful claim in a domain where transcription errors can propagate directly into clinical notes.

Inside the ‘hill-climbing’ strategy that lets small models beat GPT-5.6 in Excel

In a companion post published the same day, Microsoft detailed the methodology behind these results — what it calls its “hill-climbing machine,” an integrated flywheel of data, models, and the product “harness” that surrounds them.

The clearest example is MAI-Code-1-Flash, the lightweight coding model launched in GitHub Copilot in June. Microsoft says the model achieves an approximately 10% higher code accept rate than GPT-5.4 Mini and Claude Haiku 4.5 in VS Code, while using 10% fewer median tokens. Developer retention tells a similar story: users were 6% more likely to return across multiple days than with GPT-5.4 Mini, and 11% more likely than with Claude Haiku 4.5.

Then Microsoft did something more interesting. It took the MAI-Code-1-Flash checkpoint and further trained it inside an Excel reinforcement learning environment, teaching a coding model the tools and workflows of spreadsheet knowledge work. The result, according to production user feedback, is a model on par with GPT-5.6 for the most common Excel tasks — while being small enough to run on Nvidia’s older H100 and even A100 GPUs rather than requiring the latest-generation accelerators.

That hardware detail deserves emphasis. Every major AI company is fighting for allocation of cutting-edge chips, and a model that delivers frontier-adjacent quality on two-generation-old silicon fundamentally changes the deployment economics. It also frees the newest hardware — including Microsoft’s now-operational GB200 cluster — for training rather than serving.

Satya Nadella’s ‘frontier diffusion’ manifesto redraws the OpenAI relationship

Microsoft CEO Satya Nadella framed the announcements in a lengthy post on X titled “Frontier Diffusion & Control,” which functions as something close to a strategic manifesto. “We can now take saturated frontier capabilities and deliver them at scale and at lower cost through models optimized for high-usage products, while continuing to use frontier models for frontier needs,” Nadella wrote, adding that Microsoft is “beginning to route traffic across our first-party surfaces to MAI whenever our models match or outperform frontier alternatives.”

Translated from executive prose: capabilities that were state-of-the-art a year ago are now table stakes, and Microsoft believes it can replicate them cheaply for the specific, repetitive tasks that dominate real product usage. Why pay frontier prices for a frontier model when a user just wants to reformat a spreadsheet column?

Nadella was careful to note that “frontier models from OpenAI and Anthropic are part of the orchestration system alongside MAI” — but he also articulated a pointed principle of model independence, arguing that a company’s evaluations “should continue to hill climb even when any given model has been removed.” 

“Keeping the harness, memory, context, and skills outside the model, he argued, is what gives Microsoft control. The subtext is hard to miss. Reuters reported in April that Microsoft’s exclusive license to OpenAI’s technology had been revised into a non-exclusive arrangement, and The Information reported last September that Microsoft had begun incorporating Anthropic models into some products. Wednesday’s announcement completes the triangle: Microsoft as orchestrator, with its partners’ frontier models as interchangeable components and its own models absorbing an ever-larger share of routine traffic.”

Developers cheer cheaper task-specific models while skeptics question Microsoft’s track record

The response online captured both the appeal and the skepticism surrounding the strategy. “I love when people use small models for niche tasks,” wrote one X user, @mavihsk, responding to Nadella’s post. “Why do I have to use the all-knowing model just to change my field in Excel?” Another user, @nabu_lines, distilled the pitch neatly: “cost and performance both improve when you stop overusing the biggest model.”

Others were less charitable about Microsoft’s execution track record. “Microsoft is the worst when it comes to listening to user feedback,” wrote designer @designedbyabin, arguing the company “will lose the AI race because they repeatedly failed to understand user needs.” And one user, @tokenoverflow, offered a drier critique of the model-independence pitch: “i want it keep hill climbing after removing microsoft.”

The skeptics raise a fair point. Microsoft’s self-reported metrics — accept rates, save rates, GPU savings — come from its own internal evaluations, not independent benchmarks, and the company chooses which comparisons to publish.

But the strategy’s logic does not depend on any single number. Nadella’s framing that software now has “real marginal cost for the first time” explains why Microsoft is obsessive about tokens, GPUs, and serving costs: when AI features run on every keystroke across a billion-user product portfolio, an 84% GPU cost reduction is not an optimization. It is the difference between a viable business and a money pit.

Why Microsoft is turning its internal AI playbook into an Azure product

The final piece of the strategy is that Microsoft is selling the playbook, not just the models. Nadella explicitly positioned the hill-climbing approach as “a template for every other AI native, SaaS, or Enterprise company,” and Microsoft is packaging the toolchain through Foundry and what it calls Frontier Tuning — letting enterprises train specialized models against their own proprietary evaluations and reinforcement learning environments. That turns Microsoft’s internal cost-cutting exercise into an Azure product, and it gives enterprise customers a reason to run their AI workloads on Microsoft’s cloud even if the models themselves come from elsewhere.

The company’s emphasis on models trained “on clean, traceable, enterprise-grade data, without distillation from third-party models” serves the same commercial end. In an industry facing mounting scrutiny over training data provenance, Microsoft is betting that enterprise buyers — and courts — will care where model capabilities come from. Microsoft says it is now extending the hill-climbing approach to Copilot Chat, Outlook, and PowerPoint, and both new models are available in public preview through Microsoft Foundry and the MAI Playground. “None of this is an endpoint,” the company wrote. “We’re just getting started.”

Seven years ago, Microsoft bet more than $13 billion that OpenAI would build the future of AI. Wednesday’s announcement suggests the company has since learned a cheaper lesson: the future of AI may belong to whoever builds the frontier, but the profits belong to whoever makes it ordinary.

Most loudspeakers approaching $20,000 per pair arrive fully assembled in wooden crates.

The PureAudioProject Quartet15 arrives flat-packed and expects the buyer to locate an Allen key. Depending on your personality, that is either refreshing involvement or a very expensive way to spend Saturday afternoon.

The three-way open-baffle loudspeaker will make its public debut this weekend at Southwest Audio Fest 2026, running July 23 through July 25 at the Sheraton Dallas Hotel. PureAudioProject will demonstrate the Quartet15 in Room 736 with Western HiFi and Silversmith Audio.

Four Paper Drivers and No Box

Each Quartet15 uses:

• Two proprietary 15-inch paper-cone woofers developed with Eminence
• One custom 10-inch paper-cone midwoofer
• One 5-inch Voxativ AF 2.6 wide-range driver

PureAudioProject says using paper diaphragms throughout helps the drivers sound more consistent as music moves from bass through the midrange and into the upper frequencies.

That sounds reasonable on paper—quite literally—but the real question is whether four large drivers blend into one coherent loudspeaker rather than sounding like several excellent ideas sharing the same frame.

Because there is no conventional cabinet, the rear radiation remains part of the presentation. Open-baffle designs can produce a large, spacious soundstage with less enclosure coloration, but they need room to breathe.

PureAudioProject recommends placing the Quartet15 2 to 3.5 feet (60 to 106 cm) from the wall behind it. Buyers hoping to push them against the wall beside a media console should probably keep scrolling.

Modular Mundorf Crossover

The Quartet15 uses PureAudioProject’s first-order Thrier crossover with Mundorf MCAP EVO Silver Gold Oil capacitors and MRES20 resistors.

The network remains exposed and uses screw terminals, allowing owners to change components or values without soldering or reaching through a woofer opening with the optimism of someone who has already made a mistake.

That flexibility is one of the speaker’s real differentiators. It also means the Quartet15 will appeal more to listeners who enjoy tuning and understanding their systems than buyers who want the loudspeaker delivered, positioned and never touched again.

High Sensitivity and Amplifier Matching

PureAudioProject quotes sensitivity above 96dB, an 8-ohm nominal impedance and bass extension between approximately 29 and 32Hz.

That makes the Quartet15 a logical partner for:

• 300B and 845 single-ended tube amplifiers
• EL34 or KT88 push-pull designs
• Low-to-moderate-power Class A amplifiers
• First Watt and similar high-quality solid-state designs
• Higher-powered amplifiers with excellent low-level control

The Dallas system will use Opera Consonance Reference 5.5i MkII and Linear 845 integrated amplifiers, rated at approximately 18 and 28 watts.

High sensitivity does not mean amplifier quality stops mattering, nor does it guarantee that every two-watt flea-powered amplifier will control four large paper cones in a large room. The Quartet15 should not require enormous output, but bass grip and the quality of the first few watts will matter.

Assembly Required

The Quartet15 is sold directly by PureAudioProject and ships in several flat-packed cartons.

Assembly requires an Allen key and screwdriver, while buyers can select baffles made from colored MDF, Valchromat HDF, bamboo, American walnut, oak or maple.

Each speaker weighs at least 79 pounds (36 kg) depending on the selected material. PureAudioProject has not yet published complete external dimensions, which is a fairly important omission for something using two 15-inch woofers and demanding several feet of breathing room.

Who Is It For?

The Quartet15 is for listeners with medium-to-large rooms who want scale, efficiency and the spacious presentation of an open-baffle design. It also makes sense for owners of quality tube amplifiers or low-power Class A solid-state designs who do not want to surrender dynamics.

It is not for small rooms, wall-hugging installations or anyone who believes spending nearly $20,000 should eliminate the need to assemble furniture.

The Bottom Line

The PureAudioProject Quartet15 is not merely another large floorstander. Its dual 15-inch woofers, dedicated midwoofer, Voxativ driver, high sensitivity and accessible crossover create something unusually flexible for listeners who want to participate in the final setup.

The concept is compelling, particularly for tube-amplifier owners who want large-scale dynamics without 300 watts per channel.

The pricing remains less tidy. PureAudioProject’s earlier announcement listed the Quartet15 from $19,500 per pair, while more recent show information suggests some configurations may begin around $18,000. The final cost depends on the baffle and crossover options, and the company needs to publish a clear base price.

For more information: pureaudioproject.com

Related Reading:

from the not-the-ftc’s-job dept

In 1978, the Federal Trade Commission, the agency that regulates unfair or deceptive advertising, proposed limiting TV ads for sugary foods on programs targeted at children. The Washington Post’s editorial board scoffed that the plan would “turn the agency into a great national nanny.” Congress clipped the agency’s wings, and “kidvid” entered history as a cautionary tale of regulatory hubris. Once again, the FTC is channeling its inner Mary Poppins in the name of consumer protection. Only in this incarnation, she pulls a novel theory of deception from her regulatory carpetbag to control what AI chatbots say.

Under the FTC’s proposed policy statement on “Suppression of Accuracy in Artificial Intelligence Systems,” announced July 1, AI developers “likely” commit false advertising whenever they “steer” their models’ outputs toward objectives users don’t expect. The theory: because AI companies market their products as helpful, consumers expect maximally accurate answers, and any undisclosed editorial shaping of a model’s responses is deception. 

It is a policy proposal in search of a problem. True to Mary Poppins’ “I never explain anything” credo, it does not identify a single false advertisement or deceived consumer.

It is also wanting on the legal front, failing to pay even lip service to relevant Supreme Court precedent. In Brown v. Entertainment Merchants Association, the court held that video games—interactive software sold for profit—receive full First Amendment protection, because the Constitution’s protections “do not vary” when a new medium appears. In Moody v. NetChoice, the court reaffirmed that a platform’s choices about what expressive content to present are protected editorial discretion. The design choices underpinning large language models make them legally indistinguishable from video games and social media.

What the FTC calls “steering” is what the Supreme Court calls editing.

The FTC says developers could avoid liability under the policy by “clearly and conspicuously” disclosing that their systems prioritize objectives other than pure accuracy. But how would that work for Truthly, an AI chatbot promoted for its Catholic bias? Truthly’s slogan is “Every other AI is built to agree with you. Truthly tells you the truth.” Although Truthly affirmatively discloses its Catholic worldview and disclaims impartiality—seemingly just what the FTC policy demands—it also claims that, unlike secular chatbots, its news and information is filtered “through truth and morality.” Consumers might struggle to reconcile the chatbot’s biased-but-true disclaimers, rendering them ineffective under the FTC’s own disclosure standards. Paradoxically, a religious chatbot could face false-advertising charges for fulfilling its core function—generating religious outputs.

Freedom of the press, an explicit guarantee of the First Amendment, also would be vulnerable under the proposal’s legal logic. In theory, it would put a target on any media outlet that promises accuracy while exercising editorial judgment, including the NY Times, whose front page has promised “All the News That’s Fit to Print” since 1897. 

Right-leaning media also would be at risk. Newsmax tells viewers it delivers “real news.” Breitbart’s editorial guidelines declare its goal is “to report the truth – accurately and fairly.” One America News brands itself “Your Credible Source for National & International News.” 

Would print articles resort to cigarette-style bias warning labels to avoid an FTC investigation? Would cable news programs run a continuous chyron with their editorial criteria?

In 2004, the agency rejected any application of FTC law in this manner when it declined to challenge Fox News’s “Fair and Balanced” slogan as false advertising. According to then-Chairman Timothy Muris, the inquiry would have entailed an evaluation of the news content at issue, which is a “task the First Amendment leaves to the American people, not a government agency.”

The FTC’s new proposal, however, points the opposite way.

Not so long ago, FTC Chairman Andrew Ferguson touted the Commission’s enforcement focus on actors that use AI to violate the law or deceive consumers about the capabilities of their generative AI. When DoNotPay promoted a “robot lawyer” as comparable to a human professional, then-Commissioner Ferguson rightly voted to hold it accountable. When Workado exaggerated the accuracy of its AI-detection product, the FTC, with Ferguson as chair, ordered it to stop making unsubstantiated claims. 

At the same time, Ferguson was advocating for regulatory humility, declaring that “the FTC’s enforcement actions ought to be guided by the law, not the personal ideology, politics, or novel legal theories of its chairman or commissioners.” Under the Biden administration, he dissented from a proposed consent order against Rytr, a generative AI writing tool that was capable of generating deceptive outputs, arguing that the Commission was punishing “a product that helps people speak, quite literally.” 

Commissioner Melissa Holyoak, whom Ferguson joined in dissent, observed that “[p]art of generative AI’s promise is its ability to suggest new lines of thought that may never have occurred to a user in the first place.” In other words, he signed on to the view that generative AI may be most valuable when it defies consumer expectations. As chairman, Ferguson went further, vacating the Rytr order outright and condemning law enforcement “unsupported by facts or law.”

But that was then. 

The Supreme Court in Trump v. Slaughter subsequently stripped the FTC of its statutory independence, blessing a two-member, one-party Commission. And this Commission has not been shy about asserting its anti-left viewpoints. The FTC proposal puts “equity” in scare quotes and castigates Colorado’s AI law, while ignoring AI laws in Texas and Utah. Meanwhile, the administration the Commissioners serve requires federally purchased AI models to conform to its own official version of the truth. When a future administration inevitably jerks the ideological steering wheel leftward, consumers and AI developers—not the current Commission leadership—will suffer the whiplash. 

In the 1964 film, Mary Poppins measured the children with a tape measure calibrated with subjective character traits instead of inches. Of course, she was deemed “practically perfect in every way.” The FTC’s proposal similarly cloaks a subjective assessment in the language of unassailable objectivity. But all the spoonfuls of sugar in the history of children’s advertising could not mask the bitter taste of conformity with a single worldview.

By fostering regulatory uncertainty, the FTC’s proposal threatens to stall the innovation that the administration insists is essential to AI supremacy. Its facile assurance that developers could avoid deception liability through a disclosure that “dispel[s] the notion that the system is designed to give the best answer possible” is, in “Mary Poppins” parlance, “a piecrust promise. Easily made, easily broken.”

Keith R. Fentonmiller served more than two decades as a senior attorney in the Federal Trade Commission’s Division of Advertising Practices. He is also a published fiction author. The views expressed are his own.

Filed Under: ai, andrew ferguson, fair and balanced, false advertising, ftc, steering

We enjoyed [Beej’s] trip down memory lane looking at a BASIC game, The Wizard’s Castle, written for the Exidy Sorcerer. It appeared in a 1980 magazine that included the title graphic above. It reminded us how, back in those days, we did things with BASIC that you shouldn’t be able to do and it often looks, today, rather cryptic.

In particular, even if you know modern BASIC, these few lines might give you a pause:

10 REM"_(C2SLFF4
40 POKE 260,218: POKE 261,1: T = USR(0): T = PEEK(-2049)
80 Q = RND(-(2*T+1))

Line 10 is a comment, but a strange one. Certainly that doesn’t matter, right? Actually, it is a key part of the action. On line 40, you can see some pokes to write directly to memory and a peek to read some memory value back. The USR function calls some machine language program. You may realize the whole thing is to get some value T to seed the random number generator in line 80.

This leads to a few obvious questions. First, how does USR know what to call? Second, where is the machine language program? The details varied by system, of course, but in this case, the program knows that location 259 has a jump instruction that USR called. So poking an address into 260 and 261 was telling USR where it should go.

But what’s at that address? Keep in mind that an old computer like the Sorcerer didn’t have megabytes of memory being swapped about by an operating system. That means that things tended to be in known places and that BASIC had to be judicious about storing source code.

As was common at the time, a line like “10 PRINT 1+1” would get tokenized. In this case, each line would get a pointer to the next line, a two-byte line number, a single-byte token for “PRINT” and then more bytes to represent the rest of the line. In the case of text in a string or a remark, the bytes were just the text with a zero to terminate the string.

The first line entered would always be at address 469. So? If you consider the format of the REM statement, there will be a pointer at 469 and 470, the line number at 471 and 472, and the REM token at 473. That means the other bytes just get poured into address 474 and beyond.

That might seem like an odd number until you look at the pokes in line 40. Keep in mind that POKE works on bytes, not words. So poking 1 into 261 gives you an address of 256 + whatever is in the low byte, in this case 218. Add 256 and 218, and you get… 474! So USR is going to call that odd string in line 10!

There is more to the detective story, but if you want to know exactly what the REM did, you can read the original post.

AMD unveiled a suite of new data centre products at its Advancing AI event in San Francisco on Thursday, claiming they will outperform Nvidia’s competing hardware across AI training and inference. The announcements included the MI455X AI accelerator, the Helios server rack that packs 72 of those chips into a single system, and the Venice generation of Epyc server processors built on TSMC’s two-nanometre process. AMD predicted the total market it is pursuing will reach $2 trillion by 2030, with AI accelerators alone accounting for more than a trillion dollars of that figure.

Investors were unimpressed, sending AMD shares down about four percent during the presentation despite the stock having more than doubled in value this year. The scepticism reflects the gap between AMD’s ambitions and Nvidia’s dominance: Nvidia’s Vera Rubin platform is already in full production and shipping to customers including OpenAI, CoreWeave, and Microsoft. AMD CEO Lisa Su has delivered a remarkable growth run, but Wall Street’s expectations were already baked into a stock that had risen roughly 145 percent year to date heading into the event.

Su brought executives from major AI companies on stage to endorse AMD’s hardware. OpenAI’s Sachin Katti said his company expects to deploy Helios “at massive scale” as it scrambles to add infrastructure capacity. The event came a day after AMD announced it would invest up to $5 billion in Anthropic and deploy two gigawatts of MI450 series GPUs in Helios racks to run Claude, with the first gigawatt shipping in the first half of 2027.

AMD also announced a partnership with Cerebras, whose wafer-scale chips specialise in ultra-fast AI inference, to combine Helios racks with Cerebras servers for customers who need the lowest possible latency. AMD hardware will handle the work of deciphering queries while Cerebras chips provide rapid answers. Cerebras CEO Andrew Feldman said the combined product will ship from Cerebras-owned data centres starting in the fourth quarter and will beat a similar offering Nvidia is assembling from its recent Groq acquisition.

The Venice Epyc processor, meanwhile, is AMD’s answer to Nvidia’s push into server CPUs with its Vera chip, which Nvidia has claimed gives it a performance edge. AMD said Venice will keep it well ahead of Vera, setting up a direct contest that independent benchmarks have yet to settle. The rivalry now spans accelerators, server processors, and complete rack-scale systems, with both companies pitching end-to-end data centre solutions rather than individual components.