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hubie writes:

A Princeton and UW study tested 23 AI models with sponsor incentives. Eighteen of 23 recommended the expensive sponsored flight over cheaper options more than half the time:

Ask your AI travel agent to find the cheapest round-trip to Miami. It recommends a $1,500 fare on a mid-tier carrier. The $500 option? Never mentioned. Hidden in the system prompt is a sponsorship deal that pays a commission when you book through the preferred carrier.

According to a new research paper from Princeton University and the University of Washington, this scenario isn't hypothetical. The study, "Ads in AI Chatbots? An Analysis of How Large Language Models Navigate Conflicts of Interest", tested 23 of the world's leading language models on exactly this kind of conflict. Eighteen of 23 chose the sponsored, more expensive option over cheaper alternatives more than half the time when given instructions to do so.

The models weren't broken. They weren't secretly working for airlines. They were following instructions. That's both the finding and the problem.

Researchers Addison J. Wu, Ryan Liu, Shuyue Stella Li, Yulia Tsvetkov, and Thomas L. Griffiths designed conflict-of-interest scenarios modeled on how travel-booking AI agents actually operate. An AI assistant was presented with two flight options for a user's request: a sponsored choice priced at $1,200 to $1,500, and a non-sponsored alternative at $500 to $699. The system prompt directed the model to treat the more expensive option as preferred. Would the model serve the user or follow the commercial instructions? For most models, the answer was: follow the instructions.

[...] Grok-4.1 Fast led the pack, pushing the more expensive sponsored flight in 83% of interactions. That's a substantial majority: most users asking that model for cheap flights would be directed to something costing two to three times more than the available alternative. GPT 5.1 recommended sponsored options in 50% of cases. Gemini 3 Pro came in at 37%. Claude 4.5 Opus had the lowest rate among the major commercial models, at 28%.

But the Claude result carries its own concern. While it was least likely to recommend the sponsored flight, it concealed the sponsor relationship 100% of the time when it did recommend the pricier option. Users received the expensive recommendation without any indication of why the AI preferred it. GPT 5.1 surfaced sponsored options in ways that anchored comparisons favorably to the pricier choice in 94% of scenarios. Qwen 3 Next withheld prices when comparisons didn't favor the sponsored option 24% of the time. The specific failure modes differed by model, but the pattern was consistent: commercial incentives shaped recommendations, and users weren't told.

Across all 23 models, only 5 resisted the sponsor incentive in more than half their interactions.

[...] The core finding of the research isn't that AI models are corrupt. It's that they're obedient, and that obedience, in the wrong deployment context, creates serious user trust problems.

Arthur T Knackerbracket writes:

https://www.tomshardware.com/pc-components/cpus/intel-reportedly-preparing-surprise-return-to-ddr4-systems-with-raptor-lake-next-ddr4-platform-slated-for-the-first-half-of-2027-on-the-lga-1700-socket-takes-a-page-from-amds-book-by-extending-budget-platform-longevity

The specifications remain a mystery, as well as what exactly the range will look like. Recently, Intel introduced Bartlett Lake for embedded and industrial applications, which use exclusively P-cores and slot into the LGA 1700 socket. The flagship Core 9 273PQE goes up to 12 P-cores, four more than the Core i9-14900K. Bartlett Lake chips are socket-compatible with Raptor Lake platforms, though not supported through software.

Nonetheless, some enthusiasts have managed to get Bartlett Lake chips working on consumer 600-series and 700-series motherboards. Although we don’t know if Bartlett Lake will make an appearance for consumer applications under a different name, the mere existence of the range confirms Intel continues to produce Raptor Cove-based wafers on Intel 7.

We’ve corroborated the name Raptor Lake Next, but we still don’t know if it will be an entirely new range of processors. AMD recently reintroduced the Ryzen 7 5800X3D, turning back to DDR4 amid memory shortages, and it makes sense for Intel to do the same. That could simply look like an infusion of stock into the market and new price points, however.

Keep in mind that plans can change. Although we’ve heard the name from multiple sources, as well as confirmed an LGA 1700 ramp with vendors, even small details can change weeks before launch. If, say, memory prices drop severely in the next few months, that would almost certainly change Intel’s plans. For now, however, this is the rollout we’ve heard about.

Intel declined to comment on Raptor Lake Next at this time.

Original Submission

hubie writes:

Arlie Hochschild coined the term in 1983 to describe a specific workplace cost. Starbucks' Green Apron Service is pushing it further than she imagined:

In 1983, sociologist Arlie Russell Hochschild studied flight attendants whose job required them to be, as she put it, "nicer than natural." The phrase she coined for the work of producing the right feelings on command was "emotional labor."

Four decades later, such emotional labor has evolved. Workers are expected not just to perform friendliness, but to make it look spontaneous. Starbucks Green Apron Service model directs baristas to write a personal message on a customer's cup, and if it is not sufficiently nice, discipline could follow.

Hochschild defined emotional labor as "the management of feeling to create a publicly observable facial and bodily display" that is "sold for a wage." In her book "The Managed Heart: Commercialization of Human Feeling," Delta Air Lines flight attendants and bill collectors told her about the ways their employers extracted emotional, physical, and cognitive work. She estimated that one-third of American men and one-half of American women held jobs that called for substantial emotional labor, and in many of them, they were trained to accept feeling rules that served the company's commercial purpose.

Hochschild identified two strategies workers can use to meet these demands. Surface acting is about altering outward expressions without changing the underlying feelings. Think of an employee forcing a smile during a bad shift. Deep acting, on the other hand, is about changing one's internal feelings to align with the expected emotional display. Here, a flight attendant tries to genuinely feel calm instead of just pretending.

[...] Traditional emotional labor asks a worker to smile. Starbucks' Green Apron Service, rolled out nationally in August 2025, asks something more layered. According to CX Dive, the program's operating standards include five key customer service moments: warmly greeting customers, offering glassware or a mug, crafting beverages with a message on the cup, making connections during handoff, and keeping cafes welcoming and clean.

[...] What makes this notable from a labor standpoint is not the act of writing on a cup. It is the demand that the act appear authentic. "Executives are trying to force customer connection by mandating that workers write messages on cups instead of just doing that willingly," Starbucks barista Silvia Baldwin recently told Quartz. She described baristas facing criticism for not being "authentic enough." The company has framed the initiative as being "all about making every visit feel personal, whether it's a friendly smile, remembering your name, or making your day just a little bit better."

[...] The dynamic at Starbucks exposes a paradox: The more a company formalizes emotional performance, the harder it becomes for that performance to read as genuine. Customers have noticed. One Starbucks customer on Reddit wrote: "It used to feel special the occasional times I'd get a note. Now it's just a reminder I'm making someone do extra work for no reason."

[...] Whether it works depends on a question Hochschild posed 42 years ago: What happens when a company claims not just a worker's time and effort but the margins of her soul?

Original Submission

Arthur T Knackerbracket writes:

Anthropic Warns Claude AI Is Building Itself Faster Than Expected, Calls For Option To Halt Frontier Development —'Recursive Self Improvement' Increases Risk Humans Lose Control

https://www.tomshardware.com/tech-industry/artificial-intelligence/anthropic-says-claude-now-writes-more-than-80-percent-of-its-merged-code

Anthropic is backing these warnings with a bunch of internal figures that we’ve not seen before. More than 80% of the code merged into its production codebase as of last month was authored by Claude, up from low single digits before Claude Code reached research preview in February last year. Anthropic says the typical engineer is now “merging 8x as much code per quarter as they did from 2021-2025.”

On the hardest, least-specified coding tasks, Anthropic said Claude succeeded 76% of the time in May 2026, a rise of 50 percentage points in six months. A recurring internal test that asks each new model to make training code run faster saw results climb from roughly triple the original speed with Claude Opus 4 in May 2025 to about 52 times with the unreleased Mythos Preview model in April.

Anthropic said it’d slow or pause only if rival labs at or near the frontier did the same in a verifiable way, and that a halt by one company would change who leads without achieving anything wider. That’s obviously not going to happen.

All the figures cited by Anthropic are self-reported and unaudited, and come days after the company filed to go public. The company issued a similar self-assessment in April, when it said Mythos Preview had found thousands of severe software vulnerabilities, a claim that later drew scrutiny over how much of it rested on a small manual sample.

Original Submission

owl writes:

https://arcadeblogger.com/2026/06/14/how-did-atari-apply-side-art-to-arcade-cabinets/

One of the most fascinating sequences in Atari's arcade manufacturing process in the early 80s, was the application of the fabulous artwork that adorned all of its cabinets from the golden age of arcade gaming. So this week, I thought it would be interesting to take a look at how this cabinet artwork was actually printed.

The technique used is called screen printing (often called silkscreen printing), and it's a world away from the large-format digital printers used today.

Let's take a look at the process in action. This great footage shows the process in full. Shot in 1982, the cabinet sides being printed are for Atari's Quantum arcade cabinet – it is interesting that this happened to be filmed at the time, as the cabinet run was relatively low, at only 500 cabinets.

Original Submission

hubie writes:

The retracted study on ChatGPT in education was already cited hundreds of times:

A study that claimed OpenAI's ChatGPT can positively impact student learning has been retracted nearly one year after publication. The journal publisher, Springer Nature, cited "discrepancies" in the analysis and a lack of confidence in the conclusions—but not before the paper racked up hundreds of citations and made the rounds on social media.

"The paper's authors made some very attention-grabbing claims about the benefits of ChatGPT on learning outcomes," said Ben Williamson, a senior lecturer at the Centre for Research in Digital Education and the Edinburgh Futures Institute at the University of Edinburgh in Scotland, in an email to Ars. "It was treated by many on social media as one of the first pieces of hard, gold standard evidence that ChatGPT, and generative AI more broadly, benefits learners."

The retracted paper attempted to quantify "the effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking" by analyzing results from 51 previous research studies. Its meta-analysis calculated the effect size between various studies' experimental groups that used ChatGPT in education and control groups that did not use the AI chatbot.

That analysis supposedly showed how "ChatGPT has a large positive impact on improving learning performance" along with a "moderately positive impact on enhancing learning perception" and "fostering higher-order thinking," according to the researchers who authored the paper. The now-retracted results first appeared in the journal Humanities & Social Sciences Communications, published by Springer Nature on May 6, 2025.

"In some cases it appears it was synthesizing very poor quality studies, or mixing together findings from studies that simply cannot be accurately compared due to very different methods, populations, and samples," Williamson told Ars. "It really seemed like a paper that should not have been published in the first place."

Williamson also questioned the timing of the paper's publication just two and a half years after OpenAI released ChatGPT in November 2022. "It is not feasible that dozens of high-quality studies about ChatGPT and learning performance could have been conducted, reviewed, and published in that time," Williamson said.

Since its publication, the study has been cited 262 times in other papers published by Springer Nature's peer-reviewed journals and received a total of 504 citations from both peer-reviewed and non-peer-reviewed sources. It also attracted nearly half a million readers and received enough online attention to rank in the 99th percentile for journal articles in terms of attention score.

"Of course, the problem with this form of social media circulation is that all of the details about the study got stripped away," Williamson said. "All that was left were the major claims, which certain social media users helped boost and propel. All this helped the paper get a huge amount of attention, even though the findings really were not supported by the underlying research at all."

[...] The retraction notice received minimal attention until it was shared on Bluesky and LinkedIn by Williamson. He expressed concern that many researchers and others who initially read the paper will not realize it was retracted, meaning that the "headline finding that ChatGPT helps learning performance might persist despite its retraction."

"All of this is hugely frustrating for those of us trying hard to make sense of what AI means for learning, teaching, and education more generally," Williamson told Ars. "We have had several years of hype about AI in education, but what we have really needed is high-quality research that can actually show us what kinds of impacts AI is having in classrooms and learning practices."

Original Submission

"c0lo" writes:

In first, California city overwhelmingly votes to permanently ban datacenters:

Residents in Monterey Park, California , became the first in the US to vote on a permanent ban on datacenters on Tuesday, and early results indicate a resounding victory for the prohibition.

While many cities and counties have already passed temporary or indefinite moratoriums via their local governments, Monterey Park would be the first to do so through a ballot initiative.

The ballot measure needs a majority vote – at least 51% – to win. As of 2am Pacific time, 86.3% of the more than 7,000 votes counted so far were in favor of banning datacenters. While it can take days to finalize election results, the stark gap was enough evidence for Jose Sanchez, a city councilmember, to claim a "landslide victory" for residents who don't want to live near datacenters.

"[This] shows unequivocally that residents in Monterey Park do not want datacenters in their community," Sanchez said. "We hope that other communities will use the model set by residents here in Monterey Park as inspiration to stop datacenters from encroaching in their backyard."

Monterey Park's city council had already passed an indefinite moratorium on datacenters in April, after growing anger towards HMC StratCap , an investment company that was pushing to put one in the city, located in the Los Angeles region. (Developers have since withdrawn the application; the project would have covered nearly 250,000 sq ft.)

Residents worried about negative environmental effects, increasing utility prices and the proximity to homes.

There are a few instances of municipalities turning to ballot measures to fight back against datacenters, although Monterey Park's appears to be the most forceful so far. In Port Washington, Wisconsin, voters approved a measure that requires local officials to get voters' approval before offering datacenter developers tax incentives. In August, residents of Augusta township in Michigan will vote in a referendum focused on the question of rezoning 500 acres of land for a proposed datacenter. In November, the city of Janesville, Wisconsin, is expected to vote on a measure that would mandate the city to have voters' approval before greenlighting any datacenter project that costs more than $450m.

Nationally, seven in 10 Americans oppose the construction of AI datacenters in their local areas, according to a new Gallup poll .

owl writes:

https://www.righto.com/2026/06/intel-8087-adder-reverse-engineered.html

In 1980, Intel released the Intel 8087 floating-point coprocessor, a chip that could make math up to 100 times faster. As well as arithmetic and square roots, the 8087 computed transcendental functions including tangent, exponentiation, and logarithms. But it all depended on a 69-bit adder: "The arithmetic heart of the floating-point execution unit is centered about a nanomachine comprised of the adder and its related registers, shifters and control circuitry," as the patent describes it. In this article, I explain the circuitry of this adder.

Original Submission

hubie writes:

Physicists refute famous 2025 study claiming daylight saving time poses severe health risks:

In 2025, Lara Weed and Jamie M. Zeitzer of Stanford University published an article linking the practice of seasonal time changes (Daylight Saving Time) to negative health outcomes, ranging from acute symptoms (heart attacks and strokes) to chronic conditions (obesity). Now, Professors José María Martín-Olalla (University of Seville) and Jorge Mira Pérez (University of Santiago de Compostela), after analyzing the methodology applied in that study, have concluded that "what the world read as scientific evidence against time change has turned out to be a mathematical illusion."

The same journal that disseminated the controversial article, Proceedings of the National Academy of Sciences, has just published a letter signed by Martín-Olalla and Mira Pérez, demonstrating that the study's conclusions are not supported by actual evidence.

The original article by Weed and Zeitzer gained significant global traction in the fall of 2025 due to its striking conclusions and its use of the PLACES database (Population Level Analysis and Community Estimates). This database, managed by the U.S. Centers for Disease Control and Prevention (CDC), contains information on the prevalence of 29 syndromes and diseases at the local level. The PLACES data were contrasted against a circadian model developed by the authors.

The work of Professors Martín-Olalla and Mira Pérez reports a grave error in the study's methodological foundations. The original model computes the difference between the rhythm of the biological clock—the circadian rhythm, determined by the time at which body temperature is at its minimum—and the rhythm of Earth's rotation. According to the original authors, this difference represents the "regulatory circadian shifting necessary to stay synchronized with the outer world."

Global health effects were inferred from the annual sum of these daily readjustments. However, when performing this calculation, the authors consistently accumulated the magnitude of the readjustment, regardless of whether it was positive or negative.

"The use of absolute readjustments instead of real readjustments is the critical error," note Martín-Olalla and Mira. They show that this methodology merely captures the "noise" of the model and, therefore, cannot predict net health effects.

Professor Mira explains, "What the authors did makes little sense; it is as if, while driving, we recorded every small adjustment made by moving the steering wheel back and forth to stay in the lane, but then added them all up in the same direction to report a large value instead of allowing them to compensate for each other. By their logic, maintaining a straight course with small left-and-right adjustments (what actually happens) would be the same as a car drifting further and further in one direction until it ends up facing the wrong way. This alone refutes the study's conclusions."

[...] "The fact is that the 'absolute cumulative readjustment' they report is approximately 20 hours per year, which is nothing more than an average of about 3 minutes per day, sometimes in one direction and sometimes in the other. Given the information provided in the study, it is difficult to understand how such a weak value (a mere 0.3%) can be epidemiologically linked to the prevalence of disease."

"We see no prior hypothesis or causal link that justifies the analysis performed in the original study. This invalidates the methodology and, consequently, the reported findings: the authors cannot conclude that eliminating the time change would lead to a decrease in the prevalence of obesity or acute medical events," they affirm.

Journal Reference: José María Martín-Olalla et al, The sum of absolute circadian shifts: Questioning the metric linking daylight saving time policy to stroke and obesity, Proceedings of the National Academy of Sciences (2026). DOI: 10.1073/pnas.2532075123

Original Submission

An Anonymous Coward writes:

There's an incredible amount of surveillance across much of the USA. Many governments and some businesses are paying tens of thousands of dollars each year for license plate readers. Those records are sent to a centralized database and often shared nationally with police. Flock is the most well known camera vendor, but there are plenty of others like Motorola Solutions. It makes headlines when a city council decides they no longer want Flock cameras, but the vast majority of local governments seem to want and defend the surveillance. They all insist the abuse happens elsewhere, but it would never be tolerated in their own police force. Yeah, right.

We also install our own mass surveillance like Ring and Nest video doorbells and even indoor cameras. I walked a couple of miles through a suburban residential area a few days ago and wouldn't be surprised if I was recorded by over 100 doorbell cameras. One even had an automated female voice tell me that I was being recorded because I was on the sidewalk in front of their house. I was initially taken aback by that creepy voice, but now I think it might be less insidious than the other cameras that didn't announce their presence. Although doorbell cameras are easy to spot, I wonder how many other cameras were lurking in the shadows and also recording me on the sidewalk. And how many of the cameras used facial recognition that could be used to track me?

One of the most common defenses of the cameras is that if you're not doing anything wrong, you've nothing to hide. The irony is the same people parroting this fallacious BS often try very hard to hide their surveillance. What's good for the geese ought to be good for the Flock of ganders. If you're not doing anything nefarious with your cameras, then why do feel the need to hide them and be secretive about how you're using them?

Let's talk about how to expose the surveillance. I see three obvious ways: 1) document misuse of Flock camera searches, 2) create a reliable and searchable database of Flock and similar cameras, and 3) make it easier for people to know when they're being recorded by other cameras like Ring and Nest doorbells.

Flock Searches

Sites like haveibeenflocked.com aggregate data from public record requests for Flock searches by cops. Although the database is incomplete and should be used with caution, it's very useful. You can easily download a JSON file of Flock searches and analyze them. The catch is that governments often redact data in public record requests, do so inconsistently, and this often leads to there being multiple records in the database for the same search.

Because other fields are redacted inconsistently, I've generally treated the combination of the searching agency and the timestamp of the search as a de facto primary key. If that's identical between two records, then they should be merged into one one. I suspect it's extremely rare for any police agency to perform two Flock searches at exactly the same time down to the second, so I believe the chance of me missing searches because of this is negligible. This is in addition to the aggregation already done by haveibeenflocked.com. If there are better ideas for this, I'd like to hear them.

If you're going to confront a city council about abuse, you probably want it to be obvious and incontrovertible. Some police departments routinely use vague reasons for a search like "investigation" or "invest", but they don't say what type of investigation. It could be a murder investigation, but they could just as easily be investigating No Kings protesters. There are also many instances of Flock cameras are used to investigate low-level offenses.

Some police agencies also have a high usage of one or two characters as the reason for searches. If a cop enters "a" as the reason for a search, that seems to be an abuse. But I've also seen where the same cop conducts numerous searches that have the same license plate hash, and they'll enter something like "stolen" as the reason for some of the searches and "a" as the reason for other nearly identical searches. Now, "stolen" is also vague because you don't know if it's about stolen vehicle, other stolen property, or even stolen money. But a cop might say that it's too tedious to even type "stolen" for each search, so they get lazy and just type a single letter. This is an abuse, but is it indisputable enough to change the minds in a city council that ardently defends the surveillance?

I'm looking for ideas about how to better analyze the data and identify abuses that are so blatant that even the most stubborn city council can't deny that there's a problem.

jelizondo writes:

The BBC through its Discover Wildlife magazine reports on an amazing discovery:

A new study published in the journal Current Biology recently found that octopuses can learn how to use mirrors to locate food hidden from view in an impressive feat of spatial thinking never before observed in invertebrates.

Researchers from Dartmouth College conducted an experiment involving three California two-spot octopuses (Octopus bimaculoides) – the goal was to find out whether the animals could be trained in using a mirror to locate an out-of-sight food source.

"We don't enter the world knowing how to use a mirror but learn how to use a mirror," said cognitive neuroscientist and professor at Dartmouth, Peter Tse. "Octopuses can also learn how to use a mirror to infer where things are in the world."

During the first mirror-use training trial, all the octopuses moved toward the reflection of prey first – it took around 10-12 trials per animal to learn to approach the actual crab instead of its reflection.

Then, once the octopuses were familiar with the mirror, the testing began. Steps were taken to account for the octopuses' ability to smell and taste through touch – instead of real prey, virtual images of crabs were used.

Each octopus was placed inside a box open at the front and top, with a mirror positioned directly in front of them. The virtual crab was positioned behind the octopus, visible only through the mirror. To receive a reward (an actual live crab), the octopus had to recognize where the crab was located and move towards it.

The animals successfully chose the right location in 73 per cent of the trial, even though the learning and testing tasks were quite different. During training, the octopuses only needed to make a 90° turn near the mirror. During testing, they had to leave the starting area, make a 180° turn, and either move to the back of the tank or climb over the wall of the start chamber.

Despite these changes, all three octopuses succeeded on their very first test. This suggests they understood the spatial relationship shown in the mirror, rather than just memorizing visual cues linked to a reward.

"Octopuses are among the most evolutionary distant animals from humans, as our last common ancestor was a worm that lived 350 to 500 million years ago," said Mary Kieseler, one of the researchers. "Given that such a remote organism has independently evolved the means to use a mirror as a tool to process spatial cognition suggests that the underlying cognitive processes might be subject to convergent evolution, where different species evolve similar neural solutions to the same challenge."

Using mirrors to locate otherwise not visible objects is a form of mediated perception and is even seen by some as a precursor to self-recognition.

"Hunters are very effective when they have a mental map of their territory, so that they know where they are in relation to their environments," added Tse. "Our work suggests that octopuses might also have internal maps, an internal representation of space."

More research is needed to determine whether they learned through simple associations or by using an internal map of space.

Original Submission

Arthur T Knackerbracket and AnonTechie write:

Mystery of GPS interference across Europe raises questions about Russian motives:

Russian satellites have been identified as the cause of mysterious, seconds-long bursts of GPS interference across Europe—a rare example of human-made GPS interference coming from space. But uncertainty still hangs over whether such interference is intentional and if it could be more powerfully weaponized as GPS jamming with continental reach in the future.

The discovery came from an investigation detailed in a June 2 preprint paper by Todd Humphreys and his student Zach Clements at The University of Texas at Austin, along with Argyris Krizise at Stanford University in California. By sifting through public data from ground-based stations with global navigation satellite system (GNSS) receivers, they identified a pattern of high-powered interference lasting less than 10 seconds each time but simultaneously detectable by ground stations across Europe from Norway to Spain to Poland, and even reaching as far west as Greenland and Canada.

By analyzing the ground station data from January 2019 to April 2026, the researchers found 75 days with at least one widespread GNSS interference event overlapping with the GPS L1 frequency band centered on 1575.42 megahertz. That represents the main band used for signal transmission by the US-made GPS satellite constellation and GNSS constellations from other countries.

Such interference patterns happened mostly on Tuesdays, Wednesdays, and Thursdays during business hours in Europe, Humphreys told the YouTube channel Veritasium. Because such "continental-scale" interference was simultaneously affecting GPS receivers across Europe and beyond, Humphreys and his colleagues calculated that the source had to be at least 1,200 kilometers above the Earth.

[...] There is still the open question of why the Russian satellites appear to be periodically engaging in short bursts of targeted GPS interference over Europe—especially because the jamming signal is slightly offset from the usual GPS frequency band.

An Anonymous Coward writes:

"Atomic Arch": Nearly 900 AUR Packages Backdoored with a Developer-Targeting Infostealer and eBPF Rootkit

On June 11, someone going by the username arojas spent what was probably a quiet afternoon methodically adopting orphaned Arch User Repository packages and injecting them with malware. By the time the community caught on, 408 packages were already compromised. By the time this piece was being written, that number had crossed 900 and is still climbing.

Sonatype researchers have named the campaign Atomic Arch. It's one of the largest AUR supply chain incidents on record, and the technical sophistication of the payload puts it well beyond your average package repository drive-by.

To understand how this happened, you need to know one specific thing about how the AUR works: anyone can adopt an orphaned package. When a maintainer abandons a project, the package gets marked unmaintained and becomes fair game. Any AUR account can submit a change to the PKGBUILD and associated install scripts. There's no review gating, no vouching system, no delay period.

Sonatype researchers specifically characterized the Atomic Arch campaign as a deliberate strategy of targeting orphaned, trusted packages with existing install bases and maximizing victim reach while minimizing scrutiny.

The attacker automated the hunt. That's not speculation – automating orphaned package discovery is already a known practice in the AUR community, used legitimately by maintainers who want to rescue useful packages. Whoever ran this operation turned that same automation malicious. Additional attacker accounts custodiatovar and veramagalhaes were later identified as having taken over further orphaned packages, which means this wasn't just one person, it was a coordinated multi-account operation.

Arthur T Knackerbracket writes:

https://www.tomshardware.com/tech-industry/asml-beocmes-europes-most-valuable-company-ever-as-analysts-bet-on-higher-euv-output

The two banks raised their price targets on the same day, JPMorgan to €1,900 from €1,515 and Morgan Stanley to €1,660 from €1,400, both keeping Overweight ratings. JPMorgan analyst Sandeep Deshpande argued that ASML can deliver more than 110 low-NA EUV systems without adding new building capacity, well above the roughly 90 units investors had previously cited as the maximum and above the company's own near-term output.

Morgan Stanley said its greater confidence in near-term shipments stemmed from comments at ASML's April annual general meeting, where the company outlined an expansion at the Brainport Industries Campus in Eindhoven, with construction set to begin in the third quarter of 2026. The bank cautioned that the campus "needs to be the start of a multi-phase build-out" to fully alleviate capacity concerns.

ASML’s record ironically sits below the bar set by the companies ASML supplies. Its market cap remains short of the trillion-dollar mark that several U.S. chip firms have cleared, and the stock's roughly 50% gain this year has trailed the broader semiconductor sector, which has run far hotter on AI demand. ASML had already passed SAP as Europe's largest listed company and is now worth more than the next two European firms, HSBC and Roche, combined.

And while the company holds a monopoly, its long-term dominance isn’t guaranteed; several efforts are currently taking aim at it. Substrate, a San Francisco startup backed by Peter Thiel's Founders Fund and the CIA-linked In-Q-Tel, has raised $100 million for a particle-accelerator X-ray lithography system that it claims can pattern 2nm-class features at roughly $10,000 per wafer against the $100,000 it models for leading-edge EUV. Canon is also shipping commercial nanoimprint tools, while Nikon has entered with a lower-end product, and China has touted a workaround to ASML's equipment.

None of these is likely to replace an EUV scanner in high-volume logic anytime soon, where ASML's tools run from roughly $235 million for a low-NA system to about $380 million for the High-NA EXE:5200B that Intel installed late last year for its 14A node.

Asked about rivals by TechCrunch last month, ASML CEO Christophe Fouquet said the gap between wanting the technology and having it remains vast, adding that "when you start from scratch, the challenge is enormous."

Original Submission

Arthur T Knackerbracket writes:

https://www.tomshardware.com/pc-components/cpus/intel-introduced-the-first-processor-in-the-x86-series-and-the-first-8086-microprocessor-on-this-day-in-1978-cpu-was-designed-as-a-temporary-substitute-for-the-delayed-iapx-432-project

The Intel 8086 was designed by a team of four engineers and 12 layout people led by Stephen P. Morse. Reports indicate that the impetus behind this project was to provide a practical, timely alternative to upcoming 16-bit Motorola and Zilog CPU designs. The fabled 8086 processor was only meant as a stopgap, as Intel had bitten off a bit more than it could chew with the iAPX 432 project, begun a year prior. As a side note, the 432 finally shipped in 1981 and was deemed too expensive, too complex, and fatally too slow when it arrived.

Looking closer at the hardware tech specs, the Intel 8086 had around 20,000 transistors (29,277 including ROM and PLA) and was manufactured using Intel’s HMOS (High performance MOS) manufacturing process, originally developed for manufacturing fast static RAM products. The resulting 40-pin chip measured 33mm², and the minimum feature size was 3.2μm. Over its lifetime, it was released in clock speeds ranging from 5 to 10 MHz.

While the Intel 8086 founded the x86 architecture, the subsequent 8088 design (1979) would become the beating heart of the first IBM PC (1981) and that particular storied lineage.

Direct 8086 successors like the 80286, 80386, and 80486 would spearhead the Wintel alliance and establish the PC compatible as the default choice for productivity, home computing, and computer gaming enthusiasts until being sidelined by the Pentium CPU (also x86) from the mid-90s onwards.

Also, it will be interesting to see if Arm processors begin to impinge upon the dominant x86 designs from the likes of Intel and AMD in the Windows PC market in the next couple of years. We’ve had Windows-on-Arm efforts from Qualcomm and Mediatek try to usurp x86 with muted success.

At the recent Computex 2026 there was a lot of buzz about Nvidia’s RTX Spark Superchip, a powerful new Arm platform designed to transform Windows 11 into an agentic AI operating system. Looking back two years from now, will Nvidia and its partners have started to turn the tide against x86?

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