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owl writes:

https://www.righto.com/2020/05/die-analysis-of-8087-math-coprocessors.html

Floating-point numbers are very useful for scientific programming, but early microprocessors only supported integers directly.1 Although floating-point was common in mainframes back in the 1950s and 1960s, it wasn't until 1980 that Intel introduced the 8087 floating-point coprocessor for microcomputers.2 Adding this chip to a microcomputer such as the IBM PC made floating-point operations up to 100 times faster. This was a huge benefit for applications such as AutoCAD, spreadsheets, or flight simulators.3 The downside was the 8087 chip cost hundreds of dollars.4

It's hard to implement floating-point operations so they are computed quickly and accurately. Problems can arise from overflow, rounding, transcendental operations, and numerous edge cases. Prior to the 8087, each manufacturer had their own incompatible ad hoc implementation of floating point. Intel, however, enlisted numerical analysis expert William Kahan to design accurate floating point based on rigorous principles.5 The result was the floating-point architecture of the 8087. This became the IEEE 754 standard used in almost all modern computers, so I consider the 8087 one of the most influential chips ever designed.

To explore how the 8087 works, I opened up an 8087 chip and took photos of the silicon die with a microscope. Containing 40,000 transistors, the 8087 pushed chip manufacturing to the limit; in comparison, the companion 8086 microprocessor only had 29,000 transistors. To make the chip possible, Intel developed new techniques. In this article, I focus on the high-speed binary shifter. The shifter takes up a large fraction of the chip's area, so minimizing its area was vital to making the 8087 possible.

Original Submission

AnonTechie writes:

Nvidia's New AI Racks Run on 45°C Liquid Cooling Without Traditional Chillers. Nvidia has unveiled a liquid-cooled AI data centre design that it says can bring water use close to zero. The company says higher operating temperatures and closed-loop cooling could cut power demand, even as Amazon's figures show how water-intensive current data centres remain. Amazon recently reported that its global data centres used about 2.5 billion gallons , or about 9.46 billion litres, of water in a single year.

Nvidia announced at CES 2026 that its next-generation Rubin AI GPU racks can operate using 45°C liquid cooling without requiring conventional water chillers, sparking significant stock declines among major data-center cooling equipment manufacturers.

At CES 2026, Nvidia's CEO Jensen Huang revealed that the company's new Rubin-generation Vera Rubin NVL72 GPU platform operates on 45-degree-Celsius liquid-loop cooling, eliminating the need for large-scale water chillers. Huang described this innovation as "basically cooling this supercomputer with hot water," positioning Rubin as the successor to Blackwell architecture.

[Source]: Aigazine

[Covered By]: INDIA TODAY

[Chip Details]: nVIDIA

Original Submission

An Anonymous Coward writes:

Brave Origin is a minimalist Brave edition that costs $59.99 on other platforms but is free for Linux users.

Recently, Brave introduced Brave Origin, a minimalist version of its browser whose main appeal lies in what it removes rather than what it adds. The new edition strips out many optional Brave services, including AI, crypto, VPN, Rewards, Tor, and several telemetry mechanisms, while remaining free for Linux users.

According to Brave, Origin is designed for users who want the browser's core privacy and security protections without the wider set of features included in the standard Brave browser. It keeps Brave Shields, ad and tracker blocking, frequent software updates, Chromium security patches, and ongoing security and privacy improvements.

The difference is that Brave Origin removes or disables a long list of extras. These include Leo AI, Brave News, Playlist, Rewards and browser-based Brave Ads, Speedreader, Talk, Tor, VPN, Wallet and Web3 domains, Wayback Machine, Web Discovery Project, email aliases, daily usage ping, crash logs, and privacy-preserving product analytics, known as P3A.

That makes Origin an unusual browser release. Brave frames it as a premium experience, but its main value is a smaller feature set. But what's even more interesting is that on Windows and macOS, Brave Origin is a one-time purchase priced at $59.99. On Linux, users can access Origin for free.

The browser is available in two forms. The first is a standalone desktop browser, available through a separate download. The second is an upgrade mode for the existing Brave browser on desktop and mobile devices.

There is an important technical distinction between the two. In the standalone Brave Origin app, the affected features are compiled out of the build. In upgrade mode, the features appear in a new Settings panel and are off by default.

According to Brave, future features outside the core Brave Shields experience will also be disabled by default in Origin.

For Linux users, Origin can be used as a standalone browser or as an upgrade to an existing Brave installation. The upgrade option requires Brave 1.91 or later. Linux users installing the standalone version can skip the purchase process during setup, while those upgrading an existing installation can proceed from the Brave Origin section in the browser's settings.

The regular Brave browser remains unchanged. Brave says the existing browser will continue to be free and fully supported for users who want the full feature set or do not want to use Origin. The company also notes that users can already hide or disable many Brave features manually, although doing so does not remove those components from the browser executable.

And finally, the idea behind all of this. Brave says Origin was created in response to users who wanted a more minimal browser while still supporting Brave's privacy and ad-blocking work. The company also says Origin uses a blind token protocol based on Privacy Pass to verify purchases without linking payment identity to browser use.

You can find instructions here on how Linux users can install it for free, depending on their distribution.

Original Submission

An Anonymous Coward writes:

In 2024, Dana A. Goward, founder of the Resilient Navigation and Timing Foundation, received a call from an anonymous British researcher, He said that interference from space was more than a possibility — he had observed it. Examining data from terrestrial reference stations operated by the International Global Navigation Satellite System (GNSS) Service, he had noticed instances in which GPS signal strength had decreased markedly. In each case it was for less than ten seconds, but the events had been recorded by stations across a very broad section of northern Europe. The researcher consented to the Foundation sharing these findings.

Todd Humphreys of the University of Texas at Austin and his student Zach Clements analyzed ground station data spanning from January 2019 to April 2026; they identified 75 days with at least one widespread GNSS interference event. The paper mentioned (PDF, HTML, abstract), "The interference peak is centered at 1577.5 MHz, about 2 MHz above the GPS L1 center frequency of 1575.42 MHz. In addition to tracked GPS L1 C/A signals, tracked Galileo E1 and BeiDou B1C/B1A signals also exhibited a concurrent drop in CNR during interference events." Humphreys and his colleagues calculated that the source had to be at least 1,200 kilometers above the Earth, But they couldn't go further.

Later, Humphreys received an email stating that radio stations in Amsterdam, Netherlands, and Trondheim, Norway, had captured raw interference signal data on February 11, 2026. By examining the difference in timing when that signal arrived at the two different stations, Humphreys and Clements calculated a "quasi-hyperboloid surface", stretching tens of thousands of kilometers into space where the interference satellite must have been located. The margin of error represented by the thickness of that surface was only five meters.

A comparison of suspect satellite orbits with the quasi-hyperboloid surface showed that only one satellite's orbit aligned perfectly—the Russian satellite Cosmos 2546, which are designed to provide early warnings when they detect ballistic missile launches. This discovery has raised concerns regarding Russian electronic warfare capabilities. An EU spokesperson told The New York Times that the EU has launched an investigation into these incidents but that the results remain classified, while The press office for the Russian Embassy in Washington, D.C. said they don't have a comment on that.

[...] Perhaps most damning, Humphreys' team has found that the same Russian constellation has been impacting signals from China's BeiDou satellite navigation system in an almost identical way since June 2020.

It is clear that one of this Russian constellation's primary capabilities is disruption and denial of America's GPS and China's BeiDou navigation systems, should the Kremlin decide to do so. A slight change in frequency and an increase in transmitted power is all that is needed to prevent reception of one or both systems across continental size areas.

Original Submission

An Anonymous Coward writes:

https://www.iflscience.com/its-official-voyager-1-will-cross-the-1-light-day-threshold-on-november-18-2026-82733

Voyager 1 is the most distant human-made object we have ever sent into space, and in just a few months, it will cross a pretty incredible threshold: it will be 1 light-day away from Earth, the first human-made spacecraft to reach this distance. Now, the Voyager mission team has just confirmed to IFLScience the exact date it will meet this milestone. Voyager 1 will be a record-breaking 1 light-day away on November 18, 2026.

Voyager 1 and 2 are the only human-made spacecraft that have reached interstellar space, which means beyond the heliosphere at the edge of the Solar System. One light-day away means it will take the spacecraft 24 hours or more to send a signal to Earth.

NASA did note that due to the motions of both the spacecraft and our planet, the exact moment when the signals between us and Voyager 1 will take 24 hours might be slightly different, but currently, it looks like the precise time will be 2:16:07 am PST (10:16:07 am UTC) on Wednesday, November 18, 2026.

I won't pretend that the Voyagers are not among our favorite missions. They have been in space for all my life. It is simply extraordinary that we got to see a human-made object cross the threshold of 1 light-day, an incredibly cosmic record.

The Voyager probes were launched in 1977, and after just shy of 50 years in space, they continue to give us a glimpse into the cosmos. On their way to the outer Solar System, Voyager 1 visited Jupiter and Saturn, while its twin took a deviation and got the only close-up look we have ever had of Uranus and Neptune. Voyager 1 was also the first human-made object to enter interstellar space in 2012, followed by Voyager 2 in December 2018.

Everything about these two spacecraft is truly unbelievable, particularly that they continue to send data back to Earth, providing insights into an unexplored region of space. However, most of their instruments had to be turned off to save battery, cameras included. The last-ever image taken by Voyager 1 was the Pale Blue Dot, snapped 36 years ago on Valentine's Day, 1990.

Soon, Voyager 1 will be more than 1 light-day away. This enormous distance makes it challenging to communicate with the spacecraft. There have been several problems with the Voyager probes, and yet, the mission team has been nothing short of incredible, troubleshooting and fixing a half-century-old machine in interstellar space.

On November 15, Voyager 1 will reach 25.9 billion kilometers (15 billion miles) away from Earth, a distance that takes light more than 24 hours to cover. The spacecraft has been beyond the influence of the Sun for a while, but it will take a very long time for it to be close to a star other than our own.

Original Submission

An Anonymous Coward writes:

https://arstechnica.com/security/2026/06/microsoft-spots-new-self-propagating-malware-for-stealing-cryptocurrency/

Microsoft says it has detected new self-propagating malware that spreads through USB drives in search of cryptocurrency credentials, which it then sends to attacker-controlled servers.

The company named the worm Crypto Clipper because it monitors the contents of device clipboards for patterns consistent with wallet addresses or seed phrases. When found, the malware also takes five screenshots over a 10-second period. Both the credentials and the screenshots are then sent to the attacker through Tor, a network protocol that provides anonymous routing by sending traffic through redundant nodes so logs can't capture both the sending and receiving IP addresses. Crypto Clipper establishes the Tor connection by using a SOCKS5 proxy, a network protocol that sends traffic through a proxy server, which then forwards it to its final destination.

A lightweight backdoor

"The execution of this clipper is notable because it does not depend on a traditional installer or exposed IP-based C2 infrastructure," Microsoft said Thursday. "Instead, it deploys a portable Tor client, routes traffic through a local SOCKS5 proxy, and blends data theft with remote code execution, turning a financially motivated stealer into a lightweight backdoor."

Microsoft said it observed Crypto Clipper spreading through .lnk file on a USB drive. These files store executable code. When an infected USB drive is plugged into a device, the code checks whether it is already installed on the machine. If it isn't, the malware downloads it through the Tor proxy. To better conceal evidence of the worm, the malware scans the infected USB drive and names the .lnk files with similar names.

Crypto Clipper monitors clipboard contents for patterns that are consistent with standardized 12- or 24-word seed phrases. When found, it uploads them, along with the screenshots, to the attacker's server. The stealer also replaces addresses it finds with ones belonging to attacker-controlled wallets. This allows the malware to divert payments to the attacker's pockets. Microsoft believes the purpose of the screenshots is to provide context that may be useful.

"This malware family shows how lightweight, script-based stealers can deliver outsized impact when paired with anonymized communications and runtime tasking," Microsoft said. "The combination of Tor-routed C2, clipboard targeting, screenshot capture, and remote code execution gives attackers both immediate monetization paths and continued control over compromised devices."

Original Submission

Arthur T Knackerbracket writes:

https://www.tomshardware.com/pc-components/cpus/intel-and-amds-new-ace-cpu-extensions-bring-an-efficient-ai-oriented-instruction-set-to-x86-a-new-design-makes-matrix-multiplication-more-power-and-density-efficient

ACE comes in by offering a technical standard [.PDF] that leverages the existing AVX10 registers but adds silicon dedicated to matrix multiplication. This brings multiple benefits, but the key advantages are better power efficiency, easier development and optimization, and leveraging AVX's 512-bit inputs. The latter makes for easy integration with existing designs by eschewing the need for ACE-specific inputs.

For the same number of input vectors, ACE can perform 16x as many operations, compared to AVX10. Note this doesn't necessarily mean a 16x speedup, as that will depend on each individual implementation, but it's reasonable to expect that Intel and AMD will dedicate more silicon to this task in future designs to improve performance. Plus, as each ACE instruction performs more work than its equivalent AVX10 loop, there's less CPU instruction overhead and potentially better RAM bandwidth usage right off the bat.

The benefits go far beyond just using fewer instructions for the same thing. ACE is intended to be implementation-agnostic, meaning that ML frameworks and their underlying libraries (PyTorch, TensorFlow) can just write one code path instead of having multiple variations depending on the underlying hardware and its degree of AVX support.

ACE native supports most every data type used in ML operations (including but not limited to INT8, INT32, FP8, FP16, FP32, BF16), but it also can use Open Compute Project's MX block-scaled formats natively, something that AVX10 does not provide. Developers will also be able to move some NPU-specific workloads back to CPU when they need something done now and fast. In those situations, not having to deal with the fact that each NPU is different is a huge boon, too, as ACE offers a consistent target across x86 hardware.

Original Submission

fliptop writes:

General Motors has delivered a stark lesson in modern American manufacturing: when government-pushed electric vehicle mandates meet market reality, it is the American worker who pays the price:

General Motors is facing renewed scrutiny over automation at its flagship EV assembly plant after adding dozens of robots to the production line months after cutting more than 1,000 jobs. The changes at Factory Zero in Detroit highlight the growing tension between automakers seeking greater efficiency and workers concerned about the future of manufacturing employment.

Factory Zero has played a central role in GM's electric vehicle strategy, producing models such as the GMC Hummer EV and Chevrolet Silverado EV. The facility was once promoted as a symbol of the company's transition toward an electric future and a source of new manufacturing jobs.

Instead, the plant has experienced a series of production adjustments, temporary shutdowns, and workforce reductions as EV demand has fluctuated. Those challenges have now been accompanied by a larger investment in automation technology.

From AutoBlog.com:

The UAW (United Auto Workers) Local 22 president, who represents workers at the plant, confirmed they are Fanuc-made machines and says his members are "disgusted." In an interview with Crain's Detroit Business, he said, "It's always a concern when you see a robot coming to a plant, especially after they have laid off over a thousand people. They say it's the wave of the future, and if that's so, they're taking away jobs from people." The union has filed grievances. GM has said the cobots improve safety and ergonomics. Both things can be true, and probably are.

To be fair, GM was never subtle about the direction of travel. At its GM Forward event in late 2025, Barra and her senior team spent considerable time outlining how AI and automation would shape manufacturing going forward. Earlier that year, when announcing a tie-up with NVIDIA to develop factory robotics, Barra said: "AI not only optimizes manufacturing processes and accelerates virtual testing but also helps us build smarter vehicles while empowering our workforce to focus on craftsmanship. By merging technology with human ingenuity, we unlock new levels of innovation in vehicle manufacturing and beyond."

Also at MSN and The New York Post.

Previously: General Motors Lays Off Hundreds Of US Workers

Original Submission

jelizondo writes:

..... but Many Celebrated Figures did Their Best Thinking in Just Four or Five Hours a Day — and That Deliberate Rest May Have Been Key

Silicon Canals has a very interesting opinion piece about working hours:

Sit down to do real work, the kind that asks something of your brain, and notice how long you can actually hold it. Charles Dickens wrote from roughly nine to two. Henri Poincaré, the mathematician, worked just enough to get his mind around a problem, about four hours a day. G.H. Hardy thought four hours was the ceiling for a mathematician, full stop. The Fields Medalist June Huh, according to Quanta Magazine, manages about three hours of focused work on a good day.

That is a strange pattern to sit with, given that most of us have built our days around eight hours, as if the brain runs on the same fuel gauge as a factory shift. For me it is a couple of hours before the words start coming out as mud, and I suspect I am not unusual. The figures we still talk about for their thinking seem, quietly, to have agreed.

The standard working week is a relatively recent idea, the product of decades of labor activism and given legal force in the US by the Fair Labor Standards Act of 1938, which capped the maximum workweek.

Charles Darwin is the case that sticks with me. As author Alex Soojung-Kim Pang tells it in a Nautilus essay, Darwin did a couple of focused stretches in the morning, and by around noon he would announce that "I've done a good day's work". The rest of the day went to walking, naps, letters, reading. He produced a body of work that reshaped how we understand life on earth, and he did the heavy lifting in roughly four hours.

The mathematician G.H. Hardy seems to have thought four hours was simply the ceiling. As Pang recounts, Hardy told his friend C.P. Snow that "Four hours creative work a day is about the limit for a mathematician." One mathematician's opinion is not a universal law. But hearing it from someone of his stature makes me feel a little less guilty about my own fading after lunch.

The argument Pang builds in his book Rest: Why You Get More Done When You Work Less is that the walking and the naps were not time off from the thinking. They were part of it. As he puts it in the essay drawn from the book, figures like Darwin and his neighbor John Lubbock "weren't accomplished despite their leisure; they were accomplished because of it." I think he is right. The busiest weeks of my own working life have rarely been the ones where I made anything I was proud of, and I have stopped pretending that is a coincidence.

The obvious objection is that Darwin had a private income and no inbox. Most of us cannot tell our boss we have done a good day's work and wander off to walk the dog at noon. Fair enough. I am not suggesting you try.

What I take from it is gentler than that. The interesting figures here did not do nothing for the rest of the day. They did the shallow, mundane work, the correspondence and the admin, in the lower-energy hours, and they protected a small window for the work that actually mattered. That is the lesson worth stealing.

So here is where I land. The eight-hour day, applied to work that asks anything real of your brain, is a mistake. It was designed for assembly lines and we kept it out of habit. If three or four hours is the genuine ceiling for the people doing the deepest thinking we have on record, then the rest of an eight-hour day is theatre — answering email, sitting in meetings, performing busyness for whoever is watching.

So, what is your experience with long working hours? Are you more productive or simply accumulating sitting-on-a-chair hours?

Original Submission

hubie writes:

AI Economics for Dummies:

As AI companies get ready to go public and we get a deeper look at their inner workings, it's only natural to have questions about their finances, like "Do they make money?" and "How?" Here are a few examples to help the average layperson understand the business side of AI.

1. Acquiring one grape costs Alex $2 billion. Alex offers to sell Mike one grape a month for the next 12 months for $1 billion per grape. Alex asks for the full $12 billion up front and provides Mike with one grape for the first month. Alex makes a $10 billion profit this month; his ARR is $120 billion, and his profits are trending up at an infinite rate. The Wall Street Journal's business editor moves into Alex's house, having accepted a part-time position as Alex's human footstool. He never asks to see the books.

2. Laura drives a taxi. Instead of charging her customers a fee for every ride, she charges them a $20/month subscription. Laura has 40 million paying customers, totaling roughly $13 billion in annual revenue. Laura spends $25 billion/year on gas. In a fit of late-capitalist bloodlust, hordes of tech and finance bros riot in the streets, firebombing every rideshare, bus, and pedicab they can find, declaring the transportation business officially "over." Also, Laura's taxi cost her $1 trillion to attain, and she'll have to replace it in four to eight years.

[...]

4. Benjamin owns a farm. He employs 100 workers plowing his fields. His total payroll is $10 million/year. One day, he buys a mule, which provides the worker who uses it with a modest 10 percent productivity gain. Benjamin fires 99 of his workers and purchases 99 mules, expecting a 1,000 percent productivity gain. The driverless mules cause plow damage to his property in excess of $50 million. Benjamin loses another $5 million due to the loss of productivity from his one remaining employee, who no longer guides a plow but instead spends 100 percent of his time shoveling mule shit.

[...] We hope these examples help clarify the inner workings of AI economics. But if you're still confused, all you really need to know is that everything is totally working and everyone is making a lot of money, and you should just stop asking questions, luddite.

Original Submission

Arthur T Knackerbracket writes:

https://arstechnica.com/ai/2026/06/leaked-financial-docs-show-openai-is-losing-billions-of-dollars-a-year/

As OpenAI files SEC paperwork ahead of an expected initial public stock offering, newly leaked financial documents show a company with quickly growing revenues that are currently being overwhelmed by even larger expenses.

The audited financial statements, obtained by independent journalist Ed Zitron, show OpenAI's reported revenue growing from $3.7 billion in 2024 to $13.07 billion in 2025. The Financial Times, which reviewed the same documents [Website paywalled. --Ed], writes that the company's monthly revenues had grown to nearly $2 billion by the end of 2025, suggesting that its ongoing revenue rates continued to grow throughout the year.

But the company's fast-growing revenues are still dwarfed by its even more significant expenses. OpenAI's total revenues in both of the last two years were outpaced by research and development alone, which grew from a $7.81 billion line item in 2024 to a massive $19.18 billion cost in 2025. Those numbers seem to reflect the significant costs OpenAI incurred in training new models and include $10.59 billion in R&D costs paid to Microsoft alone in 2025.

On top of that, OpenAI's "cost of revenue" (i.e., the money spent producing and distributing the product) increased from $2.65 billion in 2024 to $7.5 billion in 2025. This cost line likely reflects the significant compute costs incurred at "inference time" as the company's models respond to a growing number of user prompts. Costs associated with sales and marketing also grew from $1.11 billion in 2024 to $5.73 billion in 2025.

All told, OpenAI's day-to-day "loss from operations" increased from $8.78 billion in 2024 to $20.92 billion in 2025, a concerning direction for a company that is telling investors it hopes to be profitable by 2030. But measured as a percentage of revenues, the company's operating losses slightly improved year to year, from 237 percent in 2024 to 160 percent in 2025.

Operating numbers aside, OpenAI's headline "net loss" number of just over $5 billion in 2024 ballooned to nearly $39 billion in 2025. But the 2025 number includes a significant accounting charge related to investor valuations that shifted amid the company's 2025 conversion to a for-profit structure. The Financial Times cites "a person familiar with the matter" in reporting that this non-recurring charge was approximately $30 billion and that OpenAI's 2025 net loss amounted to a more reasonable-looking $8 billion without it.

As OpenAI tries to shift all these losses to eventual profits, it will have to start reining in its costs, especially the massive (and growing) R&D costs associated with model training. It will also have to deal with enterprise customers that are beginning to balk at token-based pricing and starting to demand a measurable return on investment for their AI spending. And on the subscription side, pressure from rival Anthropic may force the company to lower prices, which could further increase operating losses in the near term.

OpenAI shut down its Sora video generation model in March. Around the same time, OpenAI CEO of Applications Fidji Simo told employees that the company would be cutting back on "side quests" and focusing on its core coding and business users.

In March, OpenAI raised $122 billion of financing in a funding round that valued the company at $852 billion. The company reports over 900 million weekly active users of ChatGPT, though only about 50 million of those are paid subscribers.

Original Submission

hubie writes:

People underestimate how enjoyable everyday conversations really are, study says:

The small talk you try to avoid because you think it will be boring may actually be more enjoyable than you think, and good for you as well, according to research published by the American Psychological Association.

"We tend to assume that if a topic sounds dull, the conversation will be dull too," said Elizabeth Trinh, MA, a doctoral student at the University of Michigan and lead author of the research published in the Journal of Personality and Social Psychology. "But that's not what people actually experience."

In nine experiments involving 1,800 participants, researchers found that people consistently underestimated how interesting and enjoyable conversations about boring topics would be.

Participants were asked to predict how much they would enjoy talking about specific topics they identified as boring. Topics were many and varied, including World Wars I and II, nonfiction books, the stock market, cats, and vegan diets. In some cases, participants were asked to suggest a topic they found boring (responses included such topics as math, onions and Pokemon). Participants then had real conversations with strangers or friends, in person or online. Afterward, they reported how much they enjoyed the conversations.

Across experiments, the pattern was clear: people expected the conversations to be fairly dull, but afterward they reported enjoying them much more than they had predicted. This pattern held even when both parties agreed the topic was boring.

"We were both surprised and excited by how robust the effect was," said Trinh. "People consistently expected conversations about seemingly boring topics to be less interesting than they turned out to be."

The reason may be that people focus too much on the topic itself, according to Trinh. Before a conversation begins, the topic is the easiest thing to judge. But once people start talking, the interaction becomes more important.

"What really drives enjoyment is engagement," she said. "Feeling heard, responding to each other, and discovering unexpected details about someone's life can make even a mundane topic meaningful."

The findings matter because social connection plays a key role in mental and physical health. Strong relationships are linked to greater well-being and lower risk of loneliness. If people avoid conversations because they expect them to be boring, they may miss easy chances to connect.

"If we skip talking to a coworker at the coffee machine, a neighbor in the elevator, or a stranger at an event, we may be missing small moments of connection," said Trinh. "Even a brief conversation about everyday life may be more rewarding than we expect."

Journal Reference: https://doi.org/10.1037/pspi0000521.supp

Original Submission

owl writes:

https://www.sciencealert.com/ozempic-literally-came-from-a-monster-and-its-not-alone

The toxic bite of a Gila monster can kill a human, but a specific ingredient in the cocktail of the lizard's venom is the reason we have glucagon-like peptide (GLP-1) agonists like Ozempic and Wegovy.

At the end of the 20th century, endocrinologist Daniel Drucker was looking for a hormone similar enough to the human gut's GLP-1, which would have similar appetite-suppressing and blood sugar-regulating qualities, without being broken down by the human body so quickly.

Drucker had read about the work of endocrinologist John Eng, gastroenterologist Jean-Pierre Raufman and biochemist John Pisano, who had sequenced the proteins in Gila monster (Heloderma suspectum) venom and found two that looked like human GLP-1.

Drucker and his team from the University of Toronto acquired a Gila monster from the Utah Zoo's breeding program to dissect for further research. This work confirmed that the lizard species' unique genes produce a protein, Exendin-4, that fit the bill, mimicking GLP-1 while hanging round in the human body for far longer.

A synthetic version was created in the years after, but it took until 2005 for this GLP-1 agonist to become an FDA-approved treatment for type 2 diabetes. It's now also become a popular treatment for obesity, with further potential applications on the horizon.

Original Submission

Arthur T Knackerbracket writes:

Demand for AI systems plus the shortage of DRAM and NAND are shaping the global market:

Servers employing x86 chips from AMD and Intel now account for little more than half of server revenue, according to the latest figures from IDC.

In its Worldwide Quarterly Server Tracker for Q1 2026, the analyst firm says that non-x86 server revenue hit $58.7 billion, representing a startling increase of 107 percent over the same period last year.

The results mean that those non-x86 servers make up 47.9 percent of the market revenue, closing in rapidly on the amount of cash spent on x86 boxes.

The growth in non-x86 turnover is likely thanks to systems powered by Nvidia's AI chips featuring Arm cores. Although there is high demand for these, they also cost a pretty packet compared to an average datacenter box.

In fact, IDC noted a stark divide shaping the worldwide server market, which reached $122.6 billion in vendor revenue during this period, a 30.4 percent increase year-on-year.

On the one hand, AI infrastructure investment from hyperscalers and large cloud providers is "running at a scale that shows no sign of plateauing," while everything else - the non-accelerated segment - faces a supply-constrained environment, thanks largely to that AI infrastructure spending.

As Reg readers will know, memory chipmakers are prioritizing manufacturing capacity for higher margin products for AI servers and GPUs, starving the rest of the market of supply.

Component availability, particularly DRAM and NAND flash, is limiting near-term shipment volumes from vendors, IDC says, though order pipelines are strong. Supply of the right chips is therefore the chief limiting factor on server market growth.

Arthur T Knackerbracket writes:

"I consider this a success already, just from the fact that we're even going to try this." :

Just 10 months ago, NASA asked three companies if they could do something nobody had done before. Could they build and launch a satellite to save a $500 million astronomy mission at risk of crashing back to Earth? What's more, could they do it in less than a year on a tight budget?

Katalyst Space Technologies, a startup founded in 2020, presented the most compelling solution. "They came back with a response that was technically and programmatically plausible, and then we were like, 'Yeah, let's do it,'" said Shawn Domagal-Goldman, director of NASA's astrophysics division.

That was in August of last year. In September, NASA awarded Katalyst a $30 million contract to build, test, and launch a small satellite to chase down Swift and latch onto it with three robotic arms. Then, Katalyst's Link servicing spacecraft will boost Swift's orbit back to a safe operating altitude, allowing it to resume scientific observations. Easier said than done.

The Swift observatory is flying in low-Earth orbit, where the outermost layers of the atmosphere still exert some aerodynamic influence on satellites. The spacecraft launched in November 2004 on a mission to detect gamma-ray bursts, the most powerful explosions in the known Universe. Despite its age, astrophysicists still rely on Swift's multi-wavelength instruments to identify and locate gamma-ray bursts for follow-up observations by other observatories.

But there's a hitch. Swift lacks any thrusters to maintain its orbit, so aerodynamic drag has gradually caused its altitude to decay. The observatory launched into an orbit roughly 363 miles (585 km) above the Earth. As of Thursday, Swift was flying at 225 miles (363 km). The decay rate will increase as the spacecraft dips into denser layers of the atmosphere until Swift finally burns up during reentry.

Swift is losing altitude faster than anticipated due to a period of extraordinary solar activity in recent years. An active Sun puffs up Earth's atmosphere, creating higher drag for satellites in low-Earth orbit. Satellites and space debris routinely reenter the atmosphere, and most of Swift is likely to burn up before it falls to Earth's surface.

"But this was not just any spacecraft," Domagal-Goldman said. "This is an observatory with unique capabilities for astrophysics, similar to what its name would imply. It is a swift observatory that can quickly pivot across the night sky to find things that go boom in the night ... So we decided, yeah, we want to go save this one, this time, because of how special it is. But then we had a different challenge of time was running out."