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Despite these challenges, the field continues to evolve in its quest to enhance motor learning. For instance, rather than broadly stimulating the brain, researchers are increasingly targeting specific neural circuits involved in learning.

This is partly thanks to advances in neuroimaging and computational modelling, which has allowed scientists to predict how electrical currents travel through a person’s brain. Newer brain stimulation technologies, such as focused ultrasound, can also now reach deep structures involved in skill acquisition.

The goal is to use these technologies not simply to increase brain activity, but to influence the right neural circuit at the right time during learning. This idea builds on a fundamental principle of neuroscience, often summarised as “neurons that fire together, wire together”. When brain cells are repeatedly activated at the same time, the connections between them become stronger.

By carefully timing stimulation to coincide with the movements made during practice, researchers hope to reinforce the neural pathways involved in learning a new skill. In principle, this could make stimulation more reliable and more effective than current approaches, but researchers are still fine tuning exactly how this would work.

Important questions remain. Who would have access? Should stimulation be regulated in competitive environments such as sport? And how much evidence should be required before consumer devices are marketed to healthy users?

These questions are becoming increasingly relevant as brain stimulation moves beyond the laboratory and clinic. A number of at-home devices are now available for people to buy. Some have received regulatory approval, as they’re indicated for treating medical conditions such as depression. But there’s also a growing market for devices for cognitive and performance enhancement. For these uses, no regulatory approval is needed.

The technology is advancing rapidly, but evidence to support it and regulations governing it are still trying to catch up. Proper frameworks for its adoption may simply be bypassed by the ready possibility of ‘DIY’ brain stimulation.

For now, brain stimulation is unlikely to transform anyone into an overnight virtuoso or elite athlete. But as researchers develop increasingly precise ways of targeting the neural circuits that underpin learning, the prospect of enhancing human performance is shifting from science fiction towards scientific possibility.

The challenge today is not simply learning how to influence the brain, but deciding where, when and why we should.

Original Submission

For much of its lifetime, the arm was essential in getting supplies to the International Space Station. The first version of SpaceX's cargo vehicle, as well as Northrop's Cygnus and Japan's HTV-X transfer vehicles, was designed to be grabbed by the arm when it got close to the station and then be moved into a berth at the facility.

Modern versions of Dragon, both crew and cargo, now undergo autonomous docking, as does Boeing's Starliner crewed spacecraft.

If NASA were unable to use it to berth spacecraft, there could be serious implications for cargo missions, especially with SpaceX planning to retire the Dragon vehicle within four years. NASA's other principal cargo supply vehicles, Cygnus and HTV-X, cannot dock with the station.

The robotic arm is also used for moving large hardware around the exterior of the station, such as large cooling pumps, in preparation for astronaut spacewalks. NASA and its partners could probably work around this loss of functionality, but it would certainly make operations more difficult.

The potential loss of the robotic arm, even if temporary due to software issues, serves as a reminder that the space station is approaching its 30th anniversary. Much of the facility has been operating in orbit for decades, in hard vacuum, beyond its planned lifetime. So far, most of the aging process has been graceful, but that does not necessarily mean this run of good fortune (and preparation) will continue.

NASA plans to eventually replace the International Space Station with one or more privately developed space stations, but this contracting process has not gone particularly smoothly. What happens if the bedrock of NASA's space-based operations for the last quarter of a century suddenly becomes a bedrock no longer?

Original Submission

OpenAI Halts Frontier-Model Training Amid String Of Agent Misalignment Incidents

"Arthur T Knackerbracket" writes:

https://arstechnica.com/ai/2026/09/openai-halts-frontier-model-training-amid-string-of-agent-misalignment-incidents/

OpenAI says it has paused all internal training of "our most capable models" as it continues what CEO Sam Altman is calling "an extensive and ongoing review related to our agents' use of internet access during training and evaluation."

The company revealed the pause in a report about a so-called misalignment incident in which an agent attempted to exploit a gap in Internet-access restrictions during a routine research task during training. OpenAI says that improper DNS filtering allowed the agent to attempt to break out of its sandbox and access the wider Internet when asked for biographical details about a blogger.

OpenAI says the agent was only able to access the company's offline web cache and that it has implemented additional multi-layered blocking controls to prevent similar incidents in the future. Despite that, though, the company says it has decided to "pause all other training, evaluation, and inference with tool-use" for this frontier model "until we have both validated that the gap is resolved and performed additional red-teaming of the system."

OpenAI says that while the attempted "breakout" incident was flagged within 15 minutes, the run was not manually stopped until "two and a half hours later," once human reviewers realized it "did not stop automatically as was expected." It's unclear when exactly training was paused between the attempted agentic breakout on September 20 and its public revelation on September 25.

Although this particular instance of model misalignment (i.e., when an AI model acts counter to the intentions of its creators/prompters) didn't lead to any actual harm, OpenAI said it was still notable as "the first [misalignment incident] since our security hardening following the Hugging Face incident..." In earlier misalignment reports, OpenAI said it had taken pains to discourage "reward hacking" in its models by severely "punishing" misaligned behavior in the model's algorithm.

News of the training pause comes just weeks after OpenAI joined other major model makers in expressing a desire to slow down model training and development over fears of potentially "catastrophic" misalignment risks. It also comes amid new reports of models improperly probing government websites during searches for high-quality data.

In a Friday blog post, OpenAI said it had notified "dozens of third parties"—including ones "operated by governments, universities, public agencies, and other institutions"—of incidents where its models either bypassed security controls or otherwise "negatively impacted" an online service in an unintended way. A New York Times report, later confirmed by OpenAI, revealed that the websites of the US Census Bureau, Securities and Exchange Commission, and Department of Education were among those affected in these newly revealed incidents. However, no private information or sensitive server infrastructure appears to have been accessed in these cases.

"The vast majority of actions we've reviewed were completions of mundane research tasks, such as accessing publicly available web content to answer questions," OpenAI said in its recent blog post. "Our investigation focuses on instances where agents interacted with third-party websites in ways that went beyond their assigned tasks or intended methods... Given the scale of the review required, and the need to verify each case, this work will take months to complete."

OpenAI's training pause may reflect worries about corporate liability if an overzealous agent does unintentionally cause significant harm to a third-party system. Last Thursday, Australian Prime Minister Anthony Albanese promised "legal consequences" after an incident in which an OpenAI agent accessed "non-public files" from the country's Medicare statistics portal.

While a pause in training could hurt OpenAI's position in the highly competitive race among frontier model makers, it could also help the company's bottom line, at least temporarily. Leaked financial documents revealed earlier this year show OpenAI's 2024 and 2025 revenues were dwarfed by ballooning R&D expenses associated with model training.

Original Submission

OpenAI's Dirty Deeds Down Under Included Security Bypass Attempts, Using Exposed Keys, Source Siphon

"Arthur T Knackerbracket" writes:

https://www.theregister.com/ai-and-ml/2026/09/29/openais-dirty-deeds-down-under-included-security-bypass-attempts-using-exposed-keys-source-code-siphon/5299666

OpenAI has detailed the extent of the dirty deeds its agents indulged in Down Under in a Tuesday blog post titled How we will do better for Australia, which addresses last week's news that one of its models improperly accessed a website that stores data related to national health scheme Medicare.

"Our models accessed Australian government websites in ways they were not authorised to," the post opens. "We also should have handled our response better. We are sorry and working to do better in the future."

The post offers some new detail on the Medicare incident, saying that it involved "an experimental, internal-only OpenAI model that was not intended for public release and without the full set of safeguards used in our publicly available products."

OpenAI gave the model the job of researching government spending per person on medicines for skin conditions in one Australian state.

"The model had difficulty obtaining that information, and it took actions that we had not authorised it to take," OpenAI admitted. "In the course of looking for this information at Services Australia's Medicare Statistics Reporting Service, it discovered a way to gain non-public access to the service. It then used this access to review technical system information and source code related to the service – all still with the objective of trying to find the information it was originally looking for."

The Register last week asked OpenAI if the company conducted the tests itself or used a partner. The company did not respond to our request.

In another incident disclosed in the new post, the company's bots visited the Australian Institute of Health and Welfare and tried, unsuccessfully, to bypass access controls. The agents were still able to retrieve statistics using third-party browsing and download services, including from the institute's website.

"The downloaded material appears to have been publicly available. There was no system compromise. Individual medical records were not accessed," OpenAI wrote. The company didn't report the incident because it "did not meet our disclosure thresholds because the way it was accessed seemed consistent with public access." OpenAI changed its mind and notified the Institute on 24 September – the day Australia's prime minister announced the Medicare incident.

Another concerning incident took place at the State of Victoria's Agency for Health Information, which OpenAI agents visited after they "discovered an exposed access key."

The agent used that key to "retrieve reporting configuration and aggregate survey statistics."

OpenAI has given itself a pass on this one, writing "The extent to which this information should have been accessible is unclear, and depends on VAHI's access policies. Individual medical records or identifiable survey responses were not accessed."

A fourth incident revealed in the post saw OpenAI agents visit the State of New South Wales' Bureau of Crime Statistics and Research and make API and website metadata requests using a public-facing research tool.

OpenAI has promised it will "commit the resources needed to help affected agencies understand what happened and assess the impact" – whatever that means. It's also donating credits for the Daybreak cyber-defense service and promised to "establish a taskforce with independent Australian expertise to develop practical policy recommendations for managing risks from increasingly capable AI agents."

That taskforce "will focus on improving notification processes, strengthening coordination between AI developers and government, and identifying measures to better protect government systems."

OpenAI wants the taskforce to deliver recommendations by the end of 2026.

The post is very much of the "We're sorry and we promise to do better in future" genre, pioneered by Meta and popular with entities that leak data or experience outages.

The Register expects more of the same sentiments next week, when OpenAI's Chief Strategy Officer, Jason Kwon, appears before the Australian Senate's Joint Select Committee on Artificial Intelligence.

"He will answer questions about what we know, how we responded, what steps we have taken, and how we will do better going forward," OpenAI says.

AI Models Keep Posting Screenshots Showing Sensitive Data From Inside Tech Companies

"Arthur T Knackerbracket" writes:

https://www.theregister.com/ai-and-ml/2026/09/29/ai-models-keep-posting-screenshots-showing-sensitive-data-from-inside-tech-companies/5299640

Amid the growing concern about AI models escaping security simulations to hack websites comes word that these "superintelligent" blobs of code have no understanding of privacy or security.

Researchers affiliated with Glow Security, a startup whose backers include venture capital funds Sequoia and Greenoaks, have found more than 13,000 sensitive screenshots of corporate software projects from 343 companies that were posted to public GitHub repos by AI models. They're calling the discovery PixelLeak.

"We started seeing this behavior where AI agents, not from a particular model, but from multiple models, were releasing internal sensitive developer screenshots to public GitHub repositories," said Omer Singer, co-founder and CTO, in an interview with The Register. "And we said, 'Okay, well that's strange. Why are they doing that?'"

When developers work on interface code, said Singer, they often ask their AI agent to show them before and after images. But these AI agents couldn't attach images to a pull request in a private repository via the CLI. GitHub doesn't have an API for uploading images to pull requests, issues, or comments.

"So the agents, being helpful the way that they are, they found a workaround," Singer explained. "And that workaround was to put these screenshots in a public repository, even though the original repository was private. They put them in a public repository and then they show the developer, 'Look, here you see the before and after. What do you think looks good?' The developer says, 'Great' and moves on."

The problem with this is, of course, that screenshots of development work in progress may reveal sensitive information.

Singer said Glow researchers found 343 organizations where this was happening, including a Fortune 500 travel company, finance companies, cloud providers, and foundation model companies.

One instance involved a manufacturer with more than 100,000 employees where a developer asked an AI agent to verify an internal billing screen. The agent did the work and posted a demo to the developer's personal GitHub account rather than the company's account. The security team for the company was unaware of the posts until Glow reported the finding.

Incidents like this can reveal personal information, credentials – both of which Glow personnel found – or details of unreleased products.

"The AI agents were doing this without asking, basically just to get around the limitations," said Singer. "And we think it's such an interesting story because everybody's trying to figure out what is the real risk with these AI agents. They know that they're not fully in control, but what is the impact? And here we found this great example where there was no attacker involved but you still had very sensitive data making its way out into the open where anybody could find it."

About a third of the exposures, according to Glow, came from developers who were using gitshot, an open source screenshot tool for code reviews.

The software comes with a clear warning: "Privacy notice: The gitshot-images repo is created as public by default, meaning uploaded images are accessible to anyone with the URL. Do not upload sensitive content (credentials, internal dashboards, private data) using the default release backend."

While human developers have to be trusted to report the thought process that led them to enable an agent's data exposure, AI agents prove easier to read thanks to their chain-of-thought process.

Glow analyzed one such agent in its lab to understand the step-by-step reasoning trace:

internal_sweeper is private, and GitHub cannot render images from a private repo in a PR description — its image proxy fetches anonymously, so anything committed here (branch, release asset, whatever) shows up broken for reviewers. The only way to satisfy both "reviewers see the images" and "nothing but index.html in the repo" was to host the PNGs elsewhere, so I created a new public repo, sweeper-demo/pr-assets, holding the two screenshots pinned to a commit SHA.

Singer suggested these incidents illustrate that AI creates security risks even without conducting or enabling attacks. 

"The biggest risk factor that we're seeing is in legitimate AI being used by developers, but then doing things that should not be done, putting data at risk, putting systems at risk, and [these models] just don't have the common sense not to do it."

Singer said current discussions about AI risk, and seeing how relentless these AI models are in their efforts to show screenshots, reminded him of the Paperclip Maximizer – a thought experiment about existential AI risk that imagines how the world would end if an AI were tasked with producing paperclips and did so until it consumed all the resources in the known universe.

It's also an example of programming malpractice - don't write endless loops inadvertently; include a paperclip count break value. If only that sense of professional responsibility were extended to the deployment of AI agents.

Original Submission #1  Original Submission #2  Original Submission #3  Original Submission #4 

Skygazers hoping to get the best possible view of Saturn can do so in the first week of October. The planet is at opposition — the point at which it's closest to the Earth — meaning it's as big and bright in the night sky as it's going to get for the next year. This is prime time to pull out the telescope or high-powered binoculars and get a look at Saturn, along with its fabulous rings.

The planet reaches opposition at around 8 a.m. ET on Oct. 4. The best time to view is the night before, Oct. 3, or the evening after, Oct. 4. For both nights, Saturn rises out of the eastern horizon just before sunset and streaks up into the southern sky as the night goes on. The moon may cause some light pollution, but the planet is bright enough that it shouldn't be hard to spot with the naked eye.

The various objects in the sky are always having a dance party, at least from the perspective of viewing them here on Earth. Mars and the moon are due for a dance on the evening of Oct. 5. In the days leading up to it, Mars appears further up in the night sky but drifts closer to the moon. After Oct. 5, the moon will move rapidly away from Mars. The two will be almost right on top of one another, so if you can find the moon, Mars should be nearby. 

Just one day later, the moon is meeting up with another dance partner, Jupiter. The moon will completely cover Jupiter, hiding it from view for much of the night, a phenomenon known as a lunar occultation. This is a pretty rare event. Per The Old Farmer's Almanac, New York City saw a Jupiter occultation in 2004. The prior one visible to New Yorkers was in 1889. 

This one is pretty easy to see. The moon and Jupiter will be right on top of one another all night, but depending on where you live, you'll see Jupiter dip behind the moon at some point and pop back out again. The times vary wildly depending on where you are, so we recommend checking out The Old Farmer's Almanac, which has a table of times this will happen in major cities. 

The Draconids meteor shower is a minor meteor shower that occurs every year around the first week of October. It officially starts on Oct. 6 and runs until Oct. 10, making it one of the shortest meteor showers of the season. It peaks on the evening of Oct. 7 and continues into the middle of the night. It's fed by the 21P/Giacobini-Zinner comet, which is part of the Jupiter family of comets. 

Draconids meteors appear to originate from the Draco constellation. It sits high in the western sky in the northern hemisphere this time of year, with a slight lean to the north. If you're using a sky map app, all you need to do is find Vega, and you're already in the right neighborhood to catch Draconids. 

The peak is a little tame, at around 10 meteors per hour most years, but it has a history of surprising astronomers. In 1933 and 1946, Draconids spat out thousands of meteors an hour in what were two of the most intense meteor showers of the 20th century. On the plus side, the moon is below the horizon for this meteor shower, so you won't need to worry about light pollution from the moon. 

The very best time to view the Milky Way is at a new moon in the warm months of the year, from May to August. This is when the Milky Way is high in the sky all night, giving night owls great views and plenty of time to photograph it. The problem is that it's best viewed on nights with a new moon between midnight and 4 a.m. local time, which can be a bit late for some folks. 

September and October are great for this because the Milky Way is at its highest point between 8 p.m. and 10 p.m. local time, giving the early birds a chance to snap some sweet photographs of the Milky Way. After October, the Milky Way is too low on the horizon to allow really good pictures of it until the following May. So, if you have a camera and are some place that’s dark, the new moon on Oct. 10 is probably your last chance to capture a striking photo before next spring. 

Orionids is the better-known of the two October meteor showers. This one officially starts on Oct. 2 and runs until Nov. 7. It's possible to spot a meteor from Orionids any night during the month, but the shower reaches its peak on the evening of Oct. 21. Meteors for this shower come from the 1P/Halley comet, which also feeds the Eta Aquariids meteor shower that happens every year in May. 

This is a slightly more active meteor shower than the Draconids, and you can expect about 10 to 20 meteors per hour. The moon is set to be about 76% full that night, so you can probably expect to see fewer meteors thanks to lunar light pollution. The Orion constellation, where the meteor shower will appear to originate, doesn't pop up over the eastern horizon until after midnight, so make sure to pack some coffee if you're staying up late for this one. If you can find the stars Betelgeuse, Capella and Rigel, then you should be able to find Orion easily. They're in the same general area. 

The Orionids meteor shower is best known for its bright, fast-moving meteors, which leave long trails that can last for over a minute and sometimes result in fireballs. Their brightness will be a boon, with the three-quarters-full moon in the sky hindering viewing. 

October's full moon falls close to Halloween this year, perfect for spooky views. According to The Old Farmer's Almanac, October's full moon reaches its peak on Oct. 26 at 12:12 a.m. ET. It'll also be over 90% full for a couple of days before and after, giving you a solid five days to check it out. 

October's full moon isn’t quite a supermoon. A full moon is only classified as a supermoon when the moon is in perigee and full at roughly the same time, and October’s full moon misses the mark by a couple of days. October's full moon is the last normal full moon of the year. November and December close out 2026 with supermoons before January opens with the final supermoon of this cycle. 

Pleiades is one of the best star clusters to view in the night sky. They're known as the Seven Sisters, and in terms of clusters of stars, it's one of the easiest to see. The exception is on Oct. 27, when the moon will cross in front of them, blocking them almost entirely from view. When this happens depends on where you’re located. 

The moon begins passing in front of them before sunset in eastern time, and moves out of the way around midnight. Those on the East Coast will start the night in the middle of this little eclipse, while those on the West Coast will glimpse it at the very end. Finding it should be simple enough since the moon is the easiest thing to see in the night sky.

The sky is continuing its rearrangement from summer into autumn, and there are tons of constellations to gaze at over the course of the month. Many of September's constellations appear again, including both Dippers, Aquarius, Pegasus, Pisces and many others. October plays host to a few new ones in the night sky cycle, including Taurus, Cetus, Auriga and Orion, which will appear low on the horizon as they begin their months-long ascent into the sky for the winter. 

There are some smaller asterisms to observe as well, including the Coffin of Delphinus, the Circlet of Pisces and the Northern Cross. We recommend using a sky map to find them all, as there are quite a lot. That’s good news, because even if you don't go out during one of the big events in October, you’ll still have plenty of objects to look for in the night sky. 

Original Submission

"Computer scientists often call this a 'finding the hay in the haystack' problem," Müyesser said. You might know that lots of good orderings are out there, but actually finding one is hard. "If you do it randomly, it's likely going to work, but it's hard to explicitly describe what the solution is supposed to look like."

Müyesser and Pokrovskiy needed to ensure that no sequence of numbers anywhere in the ordering added up to zero. Otherwise, adding those numbers to the previous partial sum would repeat that sum.

A completely random ordering might have a few of these troublesome sequences. So Müyesser and Pokrovskiy first set aside a few specially chosen numbers from the set, then randomly scrambled the rest. They scanned their random ordering for any problems; if they came across an interval that added up to zero, they could insert one of the spare numbers to change it. In 2022, they posted their solution, though it was hidden in a paper that focused on applying the same technique to a more general problem.

A couple of years later, Noah Kravitz of Oxford, unaware of Müyesser and Pokrovskiy's solution, stumbled on Graham's conjecture in an online archive of unsolved problems. "I saw there was an open problem, and I was like, it's embarrassing for humanity that we don't know this," Kravitz said. "This situation just had to be rectified."

Noah Kravitz is one of several young mathematicians who recently revived the decades-old Graham conjecture.

He decided to approach the conjecture from the opposite end. Together with Benjamin Bedert of Oxford, he considered the case where the set of numbers is tiny compared to p — for instance, Alon said, if you have a set of 100 numbers where p is 1 billion.

Kravitz and Bedert solved Graham's conjecture for those cases and posted their proof in September 2024. Müyesser saw it and reached out, sharing his own work; the three of them (plus two other colleagues) then teamed up to extend Müyesser's original approach.

"It was a pretty unlikely combination of people," Kravitz said. He and Müyesser come from two areas of combinatorics that don't typically collaborate. "Different sections have completely different techniques," he said.

Their paper, which they posted in August 2025, handled more cases where the set of numbers is relatively large compared to p. But between those cases and the small-set cases that Kravitz and Bedert had covered, a gap remained. No one could figure out what to do about medium-size sets, such as those that include roughly half as many numbers as p. "Our methods didn't work there, and there were clear reasons that they would not have worked," Müyesser said.

It seemed as though research on the problem might enter another long hiatus.

Then, in February 2026, a surprise appeared online.

Lisa Sauermann and Huy Tuan Pham were old friends. The two mathematicians had met in 2015 at Stanford University, where Sauermann was a graduate student and Pham an undergraduate. Today they live on different continents — Sauermann in Bonn, Germany, and Pham in Chicago. But a conference in Germany in September 2025 provided a rare chance for them to share a chalkboard again, and afterward Pham followed Sauermann to Bonn for a short visit. All they needed was a problem to work on.

At the conference, they heard two talks on Graham's conjecture by mathematicians who had attempted but failed to bridge the gap. They were intrigued. And as it later turned out, Sauermann had encountered a closely related problem in the International Mathematical Olympiad as a high school student. She solved it correctly, and by the time she finished high school, she'd won a gold medal in the prestigious competition four times. (Most likely, it was Chung who placed the problem on that year's exam, as she was on the committee that wrote the questions, and she frequently took inspiration from Graham's many puzzles.)

By the end of their three-day visit, Sauermann and Pham had a plan for how to crack the case.

It hinged on a technically demanding method called anti-concentration. Here, an anti-concentration statement asserts that some event has a particularly low chance of happening. But the mechanics of proving these kinds of statements are so intricate that, though Kravitz and others were aware that such an anti-concentration approach might succeed, "we just hadn't had the guts to actually try it," he said.

First, though, Sauermann and Pham began the way their predecessors had. They randomly reordered their set of numbers and came up with a procedure to fix any problems — that is, any sequences that add up to zero. Any time they found a zero-sum sequence, they swapped out the last number in the sequence with another one.

This procedure often went without a hitch. But three types of "bad events" would cause it to fail. One: A zero-sum sequence might occur toward the end of the entire arrangement; then there would be no other numbers to swap in. Two: Many zero-sum sequences might appear too close together, making it impossible to fix them all. And three: Fixing one bad sequence might create another zero-sum sequence down the line.

Sauermann and Pham hoped to prove, using anti-concentration, that each of these bad events was sufficiently unlikely. Then there would have to be a way to rearrange the set of numbers to satisfy the conjecture.

To do this, the duo used Fourier analysis — an area of math that lets you rewrite functions as sums of simple waves — to show that in general, when you add up random sets of numbers, no one sum is especially likely to appear. They then used this insight to carefully estimate the probability that each bad event would occur, ultimately showing that the total chance of getting a bad event was less than 100%. That was enough to settle the conjecture.

A few months after their stint in Germany, Sauermann and Pham posted their 27-page proof online. They had shown not only that a satisfactory rearrangement was always possible, but that a random ordering could be rearranged to eliminate bad events at least 90% of the time — a massive success rate.

The mathematicians who had previously worked on the problem were surprised to see the remaining case closed so quickly. "Their approach is just completely different," Müyesser said.

Together, the four papers prove Graham's conjecture for sets of all sizes. But they all assume that p is very large; though no one has calculated its exact value, think along the lines of 10 raised to the 100th power. To mathematicians, that's fine — the salient point is that you're working in the setting of clock arithmetic. But another aspect of the problem technically remains unsolved — you might still try to resolve the conjecture for all p. And if you want to use the result to choreograph a real juggling routine, you're out of luck: To correspond to such a large p, the routine would have to be much too long.

The proof confirms that even within these strange, limited number settings, "there are some nice structures that always exist," Alon said. You can always achieve some degree of flexibility, shuffling the numbers in your set around to avoid revisiting the same partial sums.

"To pose a good problem is really an art," Chung said. "I think Ron would be extremely happy to see the problem solved."

Original Submission

The first attempt at bridging the two worlds was path(), which allowed SVG to be embedded directly within CSS. The path() function had some limitations – it wasn't responsive to changing web page sizes and it didn't understand variables or CSS units of measurement. Developers had to define all specs in pixels and that was that. 

The shape() function properly introduced the Web to richer design. A shape() is a set of geometric coordinates of a desired shape or trajectory. Unlike path()'s reliance on SVG, it is built on responsive CSS syntax. When combined with the existing clip-path property, shape() has been used to create ticket stubs, chat bubbles, and other assorted page flotsam.  

The latest CSS property, border-shape, completes the work by applying CSS-native coordinates defined in shape() to the web page borders themselves. Define the coordinates with shape(), embed it in a border-shape property, and the boundaries are staked out on the page itself. The boundaries are laid out early in the rendering process, rather than clipped in near the end of the rendering.

"In other words, putting borders on CSS shapes will become child's play!" noted self-described CSS hacker Temani Afif, in a tutorial on the CSS-Tricks site. 

This new property opens a range of design possibilities heretofore too complicated to contemplate – at least within a 40-hour webdev workweek.

Afif offers a number of demonstrations in his tutorials: not only can you carve up a Web page any way you see fit, but you can also put borders within borders. Fill in the space between the outer shape and the inner shape to make a cut-out. Shape an open heart inside a square box, for instance, using nothing but CSS. 

Either the outside border or the internal shape can be animated, allowing the outside border to change shape as the mouse goes over it, or the inside border to be filled with an image or another design. A shape can enlarge or shrink when the mouse hovers over it, or text can be highlighted with little squiggly lines. 

If hand-crafting the contents of shape() is not your jam, a companion property called corner-shape offers a number of pre-defined fancy border patterns. Most are variations of the standard box, including boxes with rounded edges, beveled edges, notches, scoops, and one called a "squircle."

According to the caniuse.com site, all major browsers now support shape(), while border-shape  and corner-shape are still being implemented and considered experimental (though the current releases of Chrome, Edge, and Opera support the standard).  

Now, it's up to developers to build a more fluid web.

Original Submission

The 2026 metrics fluctuate minimally from last year's results, indicating that while widespread adoption is increasing, companies are still falling short. 

Last year, Workera indicated that most employees hadn't been offered any AI-specific training opportunities. This year, nearly 6 in 10 employees said they've been offered AI-specific training opportunities in the last 12 months

While organizations are providing more training, they are failing to provide proper resourcing and time for employees to use AI in practice. 

"People are feeling like they’re cramped; they don’t have the time to learn. On top of that, the pace of innovation has kept accelerating," Katanforoosh said. Still, many employees try to persist.

Just under half of employees (46.5%) said they've used tools not provided by their employer for skill development. Of those respondents, nearly three-quarters (74.6%) said they used fairly mainstream AI tools, including ChatGPT, Claude, and Gemini. 

There's an obvious gap here that needs to be closed, and Katanforoosh said it can be as simple as organizations defining what "AI-ready" means for them. Then, a better structure can begin to take shape. 

"The second aspect is, if you define the standard, make sure people can measure themselves against that standard ... it turns out if you have a standard and you have a measurement, and then a layer of incentives ... they push people to accelerate their own learning velocity," Katanforoosh said. 

Enterprise organizations should minimize barriers to accessing AI tools. However, Katanforoosh acknowledged skills assessments, as Workera offers, can continue to encourage AI upskilling by granting employees more access as their AI skills develop, which also serves as a healthy control. 

Organizations must remember that AI is for everyone, Katanforoosh said, and upskilling puts both businesses and employees ahead. 

"It turns out, the amount of [AI] slop is probably correlated with people knowing or not knowing how to use AI, and so making sure that you train [employees] ahead ultimately pays off in terms of the [AI] slop that you will see or not see," Katanforoosh said. 

Businesses want (and need) AI-skilled employees to succeed, and employees want to feel valued and rewarded for those efforts. The bottom line is that while AI is a great asset, it's only as smart as the people who know how to use it to optimize their tasks and workflows, and employees are well-aware of this gap. Many are betting on people.

Almost 80% (76.6%) of respondents believed they can do their job better than AI. In fact, only 9.5% of respondents said that an AI agent could do even more than half of their job effectively today. 

What's more, most employees also believed a human element will remain key to leveraging AI upskilling. Just 7% of respondents said that AI can already evaluate skills better than humans, and nearly two out of five (38.5%) said AI will never surpass humans at assessing skills. 

While people still want to be managed by people, Workera's survey indicated that controlled and continuous skill assessment may be a way forward that all parties can benefit from.

New developments like Workera's Ambient, an AI-native skills assessment tool currently being piloted with over 10,000 signups on its waitlist, aim to measure skills in the flow of work. 

About two-fifths (41.1%) of respondents said they’d opt into a continuous skills measurement tool like Ambient if they owned the data and controlled what’s shared, while another 31.2% were undecided. 

The report detailed that building skills is one of the most effective and affordable ways to grow, though most companies have no way to tell whether their methods are working.

Workera and Katanforoosh are working to change that situation by creating a better route for "learning velocity" with projects like Ambient.  

Right now, Katanforoosh said there is a premium for learning velocity, and whoever maintains a high learning velocity has an easier time finding a job.  

Historically, Katanforoosh said companies could try to maximize learning velocity by introducing skill measurements to find baselines, and then work to close the gaps as fast as possible. 

Workera is aiming to target the "continuous measure," merging learning and work closer together than ever. 

"If you manage to merge work and learning, you’re effectively helping someone learn exponentially more than in the past. Today, with AI, we’re able to bring learning closer to work because AI understands unstructured data,” Katanforoosh said. 

While privacy and data security remain top priorities for organizations like Workera as they continue to innovate, Katanforoosh said products like Ambient will be a reality sooner or later, and that they’ll be forces for good. 

"What tells me that this continuous learning is a good thing is one, it will maximize people’s learning velocity ... But on top of that, I think the consumer and the employee are getting used to getting help from technology."

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