The Linux Foundation's director of IT infrastructure says they now spend more CPU cycles "rendering commits for scrapers than we spend on all other kinds of legitimate access." At any one time, across 5 geo-distributed nodes, there are 14 CPU cores doing nothing but rendering git commits as html....

[W]hen a source is guaranteed to be LLM-free, like the entire history of kernel commits, it's worth its weight in gold as a source of training data... At the time of writing, linux.git is about 1.48 million commits. Oh, and we have about 922 forks of it on git.kernel.org — but don't worry, it's actually extremely efficient on the backend, since it's mostly the same objects in every fork. Unless, of course, you're a scraper, in which case you have, oh, several BILLION valid URLs you can scrape, only to get 922 duplicates of the same 1.48 million commits — which is exactly what the scrapers are doing. But wait, it's not just commits itself. You can also ask for patches, plain renders, diffs between arbitrary commits — cgit is happy to let you, which was perfect for the times when the Internet was for humans or crawlers who obeyed robots.txt, and is AWFUL right about now, because we can generate 1.2 METRIC BAJILLION valid URLs just for a single fork of linux.git.

Initially, this was the solution — look through the logs, find out which IPs are obvious scraper bots, and fail2ban them. At first, this was easy, because the bots helpfully told you who they were via their user-agent. Then, they wised up and started pretending that they were random vanilla browsers. So, we started banning them by IP — after all, it's easy to figure out that an IP that is trying to grab every possible commit in a 8-year-old abandoned fork of linux is not really some lone Chrome on Windows user who is just furiously clicking every link that comes across their screen. The bots then started fanning out to entire subnets, but this was still meh, because obviously an IP coming from Google Compute is just pretending to be a Firefox user...

And... that's when things turned really, really ugly. Suddenly, the crawlers were coming from millions of random residential or mobile IPs, all pretending to be random modern browsers. An IP like that would make 4-5 requests and then never show up in the logs again... They descended like swarms of locust, hit hard and fast until the system fell over and then moved on to the next target until you recovered. Then, they returned. Rinse. Repeat. They still do that — welcome to the wonderful world of "proxy SDK monetization." It's big business, and your TV is probably doing it...

Today, git.kernel.org receives about 6M daily requests demanding to see random commits. Of these, 66% are still immediately batted away with the Anubis challenge, but 33% are now solving the math and getting through to the main site — because apparently what we have to offer is worth spending a ton of cycles to calculate the Anubis challenge... With a bunch of generous assumptions, legitimate requests are only about 2% of git.kernel.org traffic — everything else are scrapers...

[W]e're turning off features to reduce the number of crawlable URLs and to gate off actions that are expensive for us to run. Expect to lose some functionality, at least when accessing our resources anonymously. Trust me, we hate it just as much as you, but at this point it's a necessity... [W]e promise to still offer all of our data for download to anyone who asks. You just may have to jump through more hoops to get it.

Sorry.

America's public education system has become "an avenue for multitrillion-dollar companies to enact their agendas," argues a NewYork Times reporter who's covered that tech industry influence for over 10 years. Big tech companies "advocate for new laws to require schools to teach industry-aligned subjects. They sit on committees that help shape learning standards for public school students..."

The reporter concludes that "Silicon Valley's efforts to capture the attention of education professionals and provide student training materials can also double as marketing or indoctrination." They enable some of the most powerful corporations on the planet to spin rosy technology stories for administrators, teachers and students, giving them a sanitized view of industry practices and product risks. Many parents would likely object if, say, Exxon Mobil wrote their children's environmental science curriculum. Or if Purdue Pharma trained their children's biology teachers. Yet, over the last decade, schools have often embraced the technology industry in similar ways with few questions asked.
The article excerpts the soon-to-be-released book " Coding Kids: Big Tech's Battle to Remake Public Schools," with the reporter adding more thoughts on LinkedIn: Every few years, Silicon Valley has urged American public schools to quickly adopt the latest technology or teach the latest tech subject: Big Data analytics; laptops for every child; algorithm-driven math and reading apps; compter programming; virtual reality. And each time the urgent arguments sounded remarkably similar: if schools just adopted the latest tech tool, or taught the latest tech subject, it would democratize learning for every child and equip students with valuable career skills.
Despite good intentions, "today we know some of those classroom tech drives did not exactly pan out as advertised." Now some of the same companies that urged schools to provide laptops and coding lessons are employing similar arguments to urge schools to quickly adopt A.I. tools. I've spent years covering Chromebooks and student chatbot use. And I'm troubled by our collective amnesia around school tech.
Thanks to long-time Slashdot reader theodp for sharing the article.

An anonymous reader quotes a report from CNBC: U.S. President Donald Trump's administration is reportedly considering new tariffs on semiconductors used in the U.S., as tech giants race to beat China in the artificial intelligence infrastructure build-out. The duties will be imposed on an expanded range of tech products made alongside chips, including laptops, data center servers and gaming hardware, eight people familiar with the matter told Politico in a report published Thursday.

The measures, which are still in the early phases and subject to significant changes over the next few months, will be introduced via a staggered rollout period, according to the report. The White House did not immediately respond to a request for comment from CNBC, but told Politico: "Reshoring semiconductor manufacturing is a top priority for President Trump, whose policies have already secured hundreds of billions of dollars of investments in this key sector." The tech industry is pushing back on the tariffs, warning they could raise chip costs, slow data-center construction, and undermine the very AI expansion the administration wants to promote. "This may be the single dumbest way imaginable to pursue American dominance in AI," said one tech official from a major industry group that also served in the first Trump administration. "It's like kneecapping yourself at the starting line."

Lobbyists say they have repeatedly urged officials to "tread carefully" because U.S. demand is surging while domestic chip capacity remains far too limited. The industry argues that factories take years to build and current domestic production cannot come close to satisfying demand.

One tech representative said "the math literally just does not work," noting that proposed duty-free allowances "wouldn't cover the hyperscalers alone." Meanwhile, the Computer and Communications Industry Association warned that higher costs and uncertainty could put the massive data-center buildout "in jeopardy."

"AI went from being very terrible to seemingly genuinely quite good at a professional level in a very short space of time..." says the Verge's AI reporter. But AI systems "are still truly, truly terrible at some areas of math... even the days of the week." If you look at academic math papers, a lot of the time you won't see numbers... So they're still terrible, but they're now also very good at this other part. As to why, at some point you reach a critical mass of what these systems can do. We saw it with writing, we've seen it with programming. They're very good at forging connections between different areas, applying old methods in new ways, those kinds of things. It appears that the newer models they're training have apparently reached that level where it clicks, and now it can do math...

There are areas now where it seems to be producing work that is on par with good mathematicians, alongside other parts where yeah, it can't count. All caught up in that is whether it's going to rewrite employment structures or funding structures... One would hope [math researchers] would branch out into all of these new exciting areas or pose new questions. But AI won't do that, and that's the concern. And then that would leave the field quite sterile, and it will have all of these things that have been done, and maybe nothing left to pursue... [A] lot are scared that it's closing off the field. So by definition, those breakthroughs that lead to something surprising and new that you can say, "Oh, this works here," may not be happening anymore...

There were some bleak responses from graduate students I saw in essays posted online. Where's their place in this as future researchers? Do they have a place in this? Is it as glorified AI proof checkers? That will be quite an unsatisfying career, I imagine. Or maybe not, I don't know. We will see. I think anything used properly will be a net boon.
They also had an interesting response when asked if AI democratizes access to high-level mathematical proofs: A lot of the mathematicians I spoke to were almost quite weary of this, actually. They love the idea, in theory, of democratizing access. They're also quite fed up with AI-generated or -assisted papers that are flooding every publication imaginable, as well as the pre-print servers that they use in these fields. Some of those I spoke to said things like, "Oh, I got three emails this week alone with people being like, 'Hey, is this legit?'" Because they thought they'd solved something with ChatGPT or with Claude, and they also don't have the mathematical skills to check whether they've actually solved something.

On the flip side, there are parts where they said, "Well, we've got a talented undergrad who's done something that a talented undergrad would probably have never managed, and here they are doing grad-level work and they've produced a paper that is legit." And in the bigger scheme of things, a few I spoke to said, "Well yeah, a lot of these are in the ivory tower. Having access to this kind of thing globally could really boost access to the kind of things here." On the flip side, the cost. These things cost a lot to run.

An anonymous reader quotes a report from The Guardian: A math professor at the University of California, Berkeley, criticizing a "severe" math deficiency among students in an op-ed for the San Francisco Standard, admitted to using artificial intelligence to help edit the piece. The Standard published a 2,000-word piece by Zvezdelina Stankova last week, in which the professor said some of her math students were "five to eight years" behind and lacked a "middle school" education on fractions and basic algebra. Stankova said the UC system's test-blind admissions were to blame, suggesting that students who weren't sufficiently prepared for the rigor of Berkeley's mathematics program were admitted because a longstanding benchmark like the SAT had disappeared.

Over the weekend, journalists at Berkeley's student newspaper, the Daily Californian, noticed the op-ed's language sounded like AI. According to Berkeley sophomore Francis Luo, they ran it through AI-detection software Pangram, which claimed 33% of the op-ed had been generated or assisted by AI. In response, Stankova admitted she had used AI software to "help edit the piece" but that the article was "the result of several hundred person-hours of intensive human work, of which about 80 hours are my own."

Stankova said by email she had used AI to locate "numerous documents and articles related to the initiative" but that "all analysis is the result of the team members." When asked about their AI usage policy, the Standard -- which published the op-ed -- shared this statement by email: "While AI may assist, our expectation is that humans are behind every article we publish and take responsibility for every word. Our understanding in working with the author of this op ed was -- and continues to be -- that this piece reflects her and her colleagues' extensive original analysis, research and expertise." Should the op-ed have been entirely written without artificial assistance or is the use of AI a distraction from the argument at hand? That's the question at the heart of the debate playing out online.

According to the op-ed, Stankova and her peers found that the number of students with a "severe deficit" in their readiness for a calculus I class had tripled after the University of California adopted test-blind admissions. Some students were allegedly five to eight years behind and struggling with basic algebra and fractions, among other concepts.

Much of that decline, Stankova argues, is from the removal of standardized tests like the SAT and ACT, which served as important benchmarks for identifying whether applicants were prepared for Berkeley-level coursework.

An anonymous reader quotes a report from Forbes: The cost of producing new results on ten longstanding mathematical problems just fell to $2,000, according to OpenAI, which says its Astra model generated machine-checkable proofs for questions that had resisted human progress for decades. OpenAI published the work on August 1 and used it to give its next major model family a name: Astra. The results run across group theory, high-dimensional geometry, coding theory, quantum complexity, lattice cryptography and extremal combinatorics. They arrived as a 249-page manuscript collection and, alongside it, something the field has not seen attached to an AI claim before at this scale: a machine-checkable certificate for every single result.

The problems were not textbook exercises dressed up as discoveries. Each had been open for at least ten years, most of them far longer, and several sit at the center of their subfields:
- A construction establishing the existence of non-sofic groups, a question that has occupied group theorists for years.
- A disproof of Connes's rigidity conjecture, a long-standing problem in the theory of von Neumann algebras.
- An improvement to the general upper bound on sphere-packing density in high dimensions, a bound that had stood since 1978.
- Three problems come from the catalogue of open questions left behind by Paul Erdos. The announcement follows another result from May, when OpenAI used a similar reasoning model to produce an original mathematical proof disproving a famous unsolved conjecture in geometry, which was first posed by Paul Erdos in 1946.

Anthropic's Claude Mythos Preview has "found flaws in a weakened version of a digital encryption standard that is in pervasive use throughout the internet," reports The New York Times. Researchers said the model discovered novel attacks against weakened versions of AES and the experimental post-quantum HAWK system, including one that was 200 to 1,000 times faster than previous human-developed methods. From the report: The flaws identified do not concern a cryptographic standard currently in use today, which means that modern banking and communication systems are not subject to immediate potential intrusions from A.I. Instead, Anthropic's technology cracked a watered-down version of an algorithm for Advanced Encryption Standard, or A.E.S., a ubiquitous protocol that safeguards web traffic, wireless networks, data storage and more. It is common to perform tests on weaker versions of encryption algorithms to understand whether more powerful computers could someday crack the actual standards, akin to solving a simpler math problem to identify whether patterns may exist for a more complicated one. In the testing, Mythos was able to break the weaker version of Advanced Encryption Standard in a way that Anthropic said made an assault 200 to 1,000 times faster than what previous human research had managed to do. While the immediate ramifications are minimal, the long-term implications could be significant. In previous tests, large-language models seemingly could not match or best what humans can do in the mathematically dense field of cryptographic research, but their rapid advances could suggest a future in which top models can surmount traditional internet security protections that are foundational to just about everything that takes place on the internet.

[...] In addition to the attack on the encryption standard, Mythos also orchestrated another improved attack against a different digital cryptographic system known as HAWK that is designed to be bulletproof against both traditional and quantum computers. HAWK is not currently in use, but under consideration by the National Institute of Standards and Technology to become a new standard. The HAWK attack was validated by its authors, and independent cryptographers reviewed the Advanced Encryption Standard attack, Anthropic said, adding that it had shared its findings with the U.S. government and industry partners ahead of publication. Mythos devised the cryptographic attack on A.E.S. almost entirely autonomously, Anthropic said, but only after first refusing to contemplate the problem because it believed it was impossible to improve on existing methods of analysis. But after some coaxing, the chatbot sat with the puzzle for about a week before engineering its novel attack. Two human researchers then worked for nearly a month to verify that the method appeared correct. "Given that we are constantly underestimating the power and time of availability of future models, are we really comfortable that two years from now strong encryption won't be threatened?" said Glenn S. Gerstell, the former general counsel at the National Security Agency.

"Mathematicians would tell you that it shouldn't be possible given current computing powers to break strong encryption in any meaningful time," added Mr. Gerstell, who helped write a report on cryptology in 2022. "But I don't think the capabilities of future models in the medium term -- before quantum computing or quantum-proof cryptography -- should be dismissed as trivial in this context."

Futurism reports: in a new essay for The Chronicle Higher Education, university-level literature and writing instructor Tyler Jagt recalls how not a single one of his students could get through an assigned 20-page article, something that he had read "without complaint" as an undergraduate a decade ago.

One student confessed that the reason they didn't finish was that they kept losing track of what the paper was about. And there's no doubt that they're not alone. Jagt cites the 2024 National Assessment of Educational Progress reading assessment results released last year. It showed that 12th grade reading scores were at the lowest level since the assessment began in 1992. Nearly a third of those 12th graders scored below the assessment's "basic" level in reading, meaning they likely "cannot draw general conclusions based on concepts presented explicitly in a text." Younger children aren't better off: a recent report from the Annie E. Casey Foundation found that 70 percent of fourth graders, or around two million kids, can't read at a proficient level.

"What I am seeing in my classroom is no longer a hunch," Jagt writes. "There is a measurable, generational collapse in sustained reading and writing, and the academy is responding to it with improvisation and exhaustion rather than the structural overhaul it requires...." Jagt cites an MIT study that found users who used ChatGPT during cognitive tasks like writing essays showed lower brain activity in areas associated with creativity compared to students who only used a traditional Google Search or didn't lookup information at all. An astonishing 83 percent of the AI users couldn't quote a single line from the essays they had just written, and capstoning the alarm, the brain activity in the AI users didn't return to normal when they were later asked to write without AI...

On our pernicious pocket devices, Jagt touted a 2017 study that found that simply having a smartphone physically nearby — even if it's face down or turned off — reduced available cognitive capacity and impaired cognitive functioning. "So when a student tells me they 'kept losing track' of a 20-page article, I have to acknowledge that they may be describing a measurable neurological condition," Jagt wrote. "The neural pathways that support sustained attention are built by use, and they atrophy without it. Your body is a use-it-or-lose-it system, and the brain is no exception."
Sunday an "Ask Reddit" question went viral — drawing over 11,000 upvotes — for its question to any teachers reading Reddit. "Is the 'Gen Alpha can't read (write, or do math ext)' crisis real? If so how bad is it?" Some responses...

• "The run of the mill non-honors kids have gotten really bad," posted one high school teacher. "Very low tolerance for working hard, very short attention span, very short stamina for active listening... It's the group that is the most worrying because a decade ago, I'd estimate that maybe 10-20% of kids at a school are like this, and now it's probably 40-50% of each graduating class... Then there's of course the bottom 10-20% kids (excluding the special ed/severe/moderate learning disability kids). This is what the viral videos are about and it's not an exaggeration. They can't read, write, or do very basic math like multiplication or division as a 17 year old."

• "This is the first year the MAJORITY of my class cheated on their first essays...." posted one high school English teacher. "It was also the first year a kid yelled 'We don't care about your fucking books, Miss!' while I was in front of the class presenting books they might be interested in for their book reviews... Almost all of them cheated on the book review they had to write."

Thanks to long-time Slashdot reader schwit1 for sharing the article.

Xbox CEO Asha Sharma and Chief Content Officer Matt Booty told staff that Xbox's current economics "cannot continue," citing more than $20 billion in spending over five years, declining revenue outside Activision Blizzard King, console supply constraints tied to RAMaggedon, and an overextended studio portfolio. The memo stops short of announcing layoffs, but a Bloomberg report says substantial Xbox cuts are expected after Microsoft's fiscal year ends on June 30. Engadget reports: The takeaways are pretty grim. For starters, the simple math of Xbox's revenue isn't adding up to success. "Excluding Activision Blizzard King, over the past five years, we have spent over $20 billion on ongoing investments in our content, platform, and hardware subsidy, but our annual revenue has declined nearly half a billion during that time," the execs state. "Going forward, this cannot continue." They also acknowledge the impact of RAMaggedon: "We are currently unable to make as many consoles as players want to buy, and we need a new business model and partnerships for hardware as we remain committed to Helix." (Helix, in this case, is Project Helix, the codename for Xbox's new console.)

Then there's the kicker, a renewed admission that Xbox still can't support the many studios it acquired in the late 2010s in an effort to grow its first-party game ambitions. "We have found ourselves over extended as we executed on changing strategies in a landscape of more readily available content," the pair said, noting elsewhere that with so many good games, not to mention the plethora of other forms of entertainment available, "Going forward, our competition is attention."

The University of California at Berkeley discovered the percentage of failing grades in multiple CS classes this spring "is significantly higher than past semesters," reports the campus's student newspaper.

"Instructors point to students' increased reliance on AI, lack of mathematical preparedness and understaffing as potential contributing factors." According to [coursework platform] Berkeleytime, 35.3% of CS 10 students and 10.6% of CS 61A students received F's in spring 2026. In spring 2025 and spring 2024, the percentage of F's did not exceed 10% for either class. The electrical engineering and computer sciences department's grading guidelines state that 7% of students in lower division courses, including CS 10 and CS 61A, should receive D's and F's...

[UC Berkeley teaching professor Dan Garcia, who taught both classes] believes the "primary driver" of these abnormally high failing rates is due to a "vast increase in academic dishonesty" due to students' usage of large language models, such as Claude, ChatGPT and Google Gemini. "Some of the numbers that you saw from the number of students who receive failing grades were because we caught them (cheating) and prosecuted them and are sending their cases to the Center for Student Conduct," Garcia said. "But in other cases, it's students who are leaning a little too hard on LLMs to do their work for them, and then at exam time just really aren't ready." According to Garcia, nearly 30 students in CS 10 were "caught cheating on take-home exams" in spring 2026...

In addition to overreliance on AI, Garcia also pointed out that many students are underprepared mathematically, a concern echoed by campus associate teaching professor Gireeja Ranade. Ranade noticed a similar lack of prerequisite mathematical skills in her spring 2026 EECS 127 class, "Optimization Models in Engineering," which she described as "differently challenging" to teach this semester. The class saw a 16.8% F rate, far higher than the 5% of D's and F's that the EECS department describes as "typical" for an upper division course...

Both Garcia and Ranade have joined more than 1,300 UC faculty in signing a petition calling for the reinstatement of ACT and SAT standardized testing scores for STEM admissions in the UC system.
Thanks to long-time Slashdot reader theodp for sharing the article.

A new Leiden Declaration, endorsed by the International Mathematical Union and published on June 2, 2026, warns that AI could undermine mathematics by flooding the field with plausible but flawed proofs, weakening attribution, shifting incentives, and giving tech companies too much influence over research priorities. "Mathematicians should find it quite striking that tech companies are suddenly interested in their work," said Kevin Buzzard, a mathematician at Imperial College London, in a statement. "The Leiden Declaration is a well-thought-through response to what is currently happening, as AI continues to disrupt this space." Ars Technica reports: The Leiden Declaration, which has already drawn hundreds of signatories, warns that recent AI developments are threatening "characteristic values" of mathematical research, "often in ways that disproportionately affect students and early-career mathematicians, and hence the long term future of the discipline."

First, it points out how AI models can "produce plausible but unreliable (or even incorrect) arguments which are difficult to distinguish from correct mathematical proofs." Such developments put reviewers under increasing pressure and are "jeopardizing our ability to implement traditional standards for the correctness, transparency, and independent verifiability of proof," the declaration warns. "Inaccurate AI-generated drafts are cheap to produce, and there is a risk of cluttering the literature with claimed results that are simply wrong," said Leslie Ann Goldberg, head of computer science at the University of Oxford, in a statement. "Once that happens, the errors are likely to propagate as new results are built on faulty foundations."

Second, the declaration highlights how "models trained on published works frequently return outputs that do not properly cite the human works they synthesize," while also pointing out that many current AI models were trained on data obtained through "exploiting licenses and access arrangements" or "simply violating copyright protections."

Third, the declaration describes how the use of AI "may become incentivized for its own sake, disrupting our mechanisms for hiring, funding and recognition" while leaving out researchers who lack access or are "unwilling to use technologies controlled by organizations whose values they do not share."

Fourth, the declaration warns against mathematics research "communicated through informal channels such as press releases or blog posts, often without any research paper or other disclosure of information necessary for scientific evaluation." Such communication strategies can lead to "oversimplification" in media reporting that overemphasizes AI tools' significance at the expense of prior human contributions, and "misleadingly uses specific mathematical tasks as metrics for the general reasoning capacities of commercial products."

Fifth, the declaration describes "increasing involvement of technology companies in mathematical research" as threatening the "autonomy of mathematics," especially as university budgets are under pressure and researchers may feel greater professional incentive to collaborate with technology companies on "asymmetric terms." This also raises the risk that mathematics research questions amenable to AI-driven techniques may be prioritized. What can mathematicians do about this? The Leiden Declaration urges them to treat AI as a tool, not a substitute for human responsibility. Individual mathematicians should disclose AI use, remain accountable for the correctness of their work, continue crediting human authors, and use AI tools only when they align with the declaration's values.

It also warns that mathematics can be applied to "warfare, oppression, mass surveillance, and the undermining of democracy," so mathematicians should weigh the ethics of tech-industry partnerships carefully. Professional organizations are encouraged to develop AI-use guidelines for publication and review, protect researchers from having their work used as training data without consent, support peer-reviewed publishing, and "actively prepare to become involved if major mathematical results are claimed using unconventional means."

For policymakers, the recommendations are blunt: "protect the rights of authors," "regulate the artificial intelligence industry," and "invest in public computational infrastructure." The declaration also urges people to "don't believe the hype," warning that tech companies have "a strong commercial incentive... to overstate the capabilities of their products."

ETH Zurich researchers say they have generated certified "perfect randomness" for the first time by using a quantum Bell-test setup with two entangled superconducting chips connected by a 30-meter cooled link. "In the long term, this work could play a similar role in digital security as atomic clocks do for timekeeping: a physically certified source of randomness that other systems can rely on," reports Phys.org. "Possible applications range from the encryption of sensitive communications and digital identities to public randomness services for lotteries and blockchain applications." From the report: They call their method randomness amplification. "This was made possible by an improved so-called Bell-Test with simultaneously high quality and high data rate," says [Renato Renner and Andreas Wallraff]. He and his coworkers use a complex setup that consists of two superconducting chips, which they cool down to very low temperatures close to absolute zero. Each chip represents a quantum bit or qubit, which can take on the states "0" or "1" or any arbitrary superposition of these states. A 30-meter-long tube, which is also cooled down, connects the two chips.

Microwave photons can fly back and forth between them, thus creating quantum mechanical entanglement. This means that a quantum measurement on one qubit, which randomly yields the values "0" or "1," influences automatically and at a distance whether "0" or "1" is measured on the second qubit. The separation of 30 meters ensures that, during the measurement, even at the speed of light, no information can be exchanged between the qubits. This would disturb the perfect randomness.

Wallraff and his team made the choice of the exact type of measurement (or "measurement basis" in technical jargon) on the two qubits depending on an imperfect random number generator. Renner's coworkers could then amplify the randomness of the measurement results further using a special algorithm. "The resulting sequence of zeros and ones is now really perfectly random, and we can even certify that," says Renner. He likens this result to crossing a ridge: "The technical improvements allowed us, for the first time, to create random numbers that will remain perfectly random for all eternityâ"no matter what analytical methods are used to assess their randomness." The findings have been published in the journal Nature.

An anonymous reader quotes a report from TechCrunch: OpenAI claims its new reasoning model has produced an original mathematical proof disproving a famous unsolved conjecture in geometry, which was first posed by Paul Erdos in 1946. If this sounds familiar to you, it's because this isn't the first time OpenAI has made such a bold claim. Seven months ago, the AI giant's former VP Kevin Weil posted on X: "GPT-5 found solutions to 10 (!) previously unsolved Erds problems and made progress on 11 others."

It turns out, GPT-5 didn't actually solve those problems; it just found solutions that already existed in the literature. Taunts from rivals like Yann LeCun and Google DeepMind CEO Demis Hassabis followed, and Weil promptly took down his premature post. Today, at least, it seems OpenAI didn't make the same mistake twice. Alongside the announcement, the company published companion remarks (PDF) in support of the disproof from mathematicians like Noga Alon, Melanie Wood, and Thomas Bloom, who maintains the Erdos Problems website, and previously called Weil's post "a dramatic misrepresentation."

[...] The proof, per OpenAI, came from a new general-purpose reasoning model, not a system specifically designed to solve math problems or even this problem in particular. OpenAI says this is significant because it means AI systems are now more capable of holding together long, difficult chains of reasoning and connecting ideas across fields in ways researchers may not have previously explored. That has implications for biology, physics, engineering, and medicine.

Test scores "are lower than they were a decade ago in school districts across the U.S.," reports Times magazine, citing new data released Wednesday by Stanford researchers. "Reading scores were down roughly 0.6 grades in 2025 compared to 2015, and math scores were down about 0.4 grades. This means that students were 60% of one school year behind where their peers were in reading a decade earlier and 40% of one school year behind in math."

But Stanford's announcement notes that America's schools "were in a 'learning recession' for seven years before the COVID-19 pandemic, with student test scores in math and reading on a steady decline since 2013." This reversal ended two decades of progress, according to Sean Reardon, the Professor of Poverty and Inequality at Stanford Graduate School of Education, whose data forms the backbone of the new research... The study reframes the narrative of pandemic-era learning loss, arguing that the crisis of the last few years was an acceleration of a problem that was already underway. "The pandemic was the mudslide that followed seven years of erosion in student achievement," said Professor Tom Kane, faculty director of the Center for Education Policy Research at Harvard University, and a lead author of the report...

The study found that the slowdown in learning coincided with two major shifts in American childhood and education policy: the widespread dismantling of test-based accountability systems that defined the No Child Left Behind era and the rise of social media use among young people. Reading scores, in particular, suffered consistently, with the average annual loss in the years just before the pandemic being just as large as the loss during it... Today, 8th-grade reading scores on national assessments are at their lowest point since 1990.

Compounding the problem, chronic student absenteeism remains a major obstacle to improving learning. Though down from its pandemic peak, 23 percent of students were chronically absent in the 2024-25 school year, far above the pre-pandemic rate of 15 percent.
More context from Time magazine: Reading scores were down roughly 0.6 grades in 2025 compared to 2015, and math scores were down about 0.4 grades. This means that students were 60% of one school year behind where their peers were in reading a decade earlier and 40% of one school year behind in math...

"The decline started around the time that social media's use among teens was exploding, and this was also occurring in a number of other countries," says Thomas Kane, one of the authors of the Educational Scorecard report and a professor at Harvard University... [H]e maintains that it is at the core of the decline in reading achievement. He points out that social media use was shown to be heaviest among the lowest achieving students.
"Some states and school districts are making progress," notes the Associated Press, "largely by shifting toward phonics-based instruction and providing extra support for struggling readers."

And "The picture is also brighter in math. Almost every state in the analysis saw improvements in math test scores from 2022 to 2025."

Are statistical programmers coalescing around a handful of popular languages? That's the question asked by the CEO of software assessment site TIOBE, which every month estimates the popularity of programming languages based on their frequency in search results: This month, the programming language R matched its all-time high by reaching position #8 in the TIOBE index once again. This is not a coincidence. The statistical programming language market is clearly undergoing a major consolidation. The biggest winners are Python and R, while many long-established alternatives continue to lose momentum. The era in which the statistical computing landscape was fragmented across many niche languages and platforms appears to be coming to an end.

Several established players are steadily declining:

— MATLAB is close to dropping out of the TIOBE top 20.

— SAS is about to leave the top 30 for the first time since the TIOBE index began.

— Wolfram/Mathematica remains well below its historical peak and is losing further ground.

— SPSS dropped out of the top 100 last month....


Elsewhere in the index, Java and C++ swapped positions this month. Java gained momentum following the successful release of Java 26. Another notable riser is Zig, which is approaching the TIOBE top 30 for the first time. Zig's growing popularity appears to be driven by its rare combination of low-level performance, straightforward tooling, and relative ease of use compared to traditional systems programming languages.
Their estimate for the most popular programming languages in May:

1. Python
2. C
3. Java
4. C++
5. C#
6. JavaScript
7. Visual Basic
8. R
9. SQL
10. Delphi/Object Pascal

The five next most popular languages on their rankings are Fortran, Scratch, Perl, PHP, and then Rust at #15. Rust is up for positions from May of 2025 — while Go has dropped to #16, seven ranks lower than its May 2025 position of #7.

Former Microsoft programmer Keith Curtis "wrote and self-published After the Software Wars to explain the caliber of free and open source software," according to his entry on Wikipedia, "and why he believes Linux is technically superior to any proprietary OS."

He's also KeithCu (long-time Slashdot reader #925,649), and has written a blog post on "How I added an LLM-based grammar checking + TeX math import to LibreOffice." : At Microsoft, I spent five years working on the text components RichEdit and Quill, and came to understand the "physics" of word processing: the file formats, data structures, and algorithms that provided fast access to text and properties, independent of the length of the file. Selecting one million characters to make them bold took about the same time as changing one character, because of the clever data structures (piece tables) and algorithms in these engines...

When I decided to add a real-time AI grammar checker to [LibreOffice plugin] WriterAgent, I knew what I was getting into, but I underestimated the trickery of LibreOffice's UNO.
His site shares the surprises he encountered, one by one. (Starting with "the office suite throws a bunch of initialization variables at your constructor. If your Python __init__ method doesn't handle them, the code fails to map the call, the stack misaligns, and the program dies.") There's sentence casing issues, duplicate words, and foreign-language syntax — all culminating in new features for "a LibreOffice extension (Python + UNO) that adds generative AI editing to Writer, Calc, and Draw..."

"If you want to try it out, the repo is here... Let's make LibreOffice and the free desktop AI-native!"

In Polymarket's prediction market, "most people end up losing money," reports the Washington Post — typically a few bucks.

"Since Polymarket launched in 2022, a few thousand people have lost the bulk of the money... and an even smaller group — .05 percent of users — has gone home with most of the overall profits, according to a new analysis from finance researcher Pat Akey and colleagues." A lot of users aren't that good at predicting the future. They're losing money at roughly the same rate as online gamblers betting on sports and other real-life events at traditional sportsbooks, according to the U.K. gambling regulator's analysis of 2024 data. On Polymarket, the odds of making a profit are slightly higher on weather and tech markets — and a little lower on sports...

On Polymarket, just 1,200 people took more than half the profits — $591 million, or more than $100,000 each. ["The top 1% of users capture 76.5% of all trading gains," the researchers write.] When you dabble in prediction markets, you're competing against these sophisticated players who consistently win. Most of those 1,200 big winners didn't place just a few smart bets. They appear to be pros making thousands of trades, mostly in the past year and a half, that were probably automated. One user made $3 million since January on more than a million trades about the Oscars, according to TRM Labs...

The most profitable participants are also just good at picking what to bet on, Akey found, winning so often it was statistically unlikely to be dumb luck. They had some sort of edge — expertise, deep research or, perhaps, inside knowledge.
"Our results suggest that the informational benefits of prediction markets come at a cost to unsophisticated participants," the researchers conclude.

Slashdot reader joshuark writes: Scientific American reports that a ChatGPT AI has proved a conjecture with a method no human had developed. A 23-year-old student Liam Price just cracked a 60-year-old problem that world-class mathematicians have tried and failed to solve.

The new solution that Price got in response to a single prompt to GPT-5.4 Pro was posted on www.erdosproblems.com, a website devoted to the Erds problems. The question Price solved — or prompted ChatGPT to solve—concerns special sets of whole numbers, where no number in the set can be evenly divided by any other...

Price sent it to his occasional collaborator Kevin Barreto, a second-year undergraduate in mathematics at the University of Cambridge. The duo had jump-started the AI-for-Erds craze late last year by prompting a free version of ChatGPT with open problems chosen at random from the Erds problems website. Reviewing Price's message, Barreto realized what they had was special, and experts whom he notified quickly took notice.

Tony Isaac shares a report from NPR: Federal survey data shows that the amount of math homework assigned to fourth and eighth grade students, in particular, has been steadily declining for the past decade. Some educators and parents say this is a good thing -- students shouldn't spend six or more hours a day at school and still have additional schoolwork to complete at home. But the research on homework is complicated. Some studies show that students who spend more time on homework perform better than their peers. For example, a longitudinal study released in 2021 of more than 6,000 students in Germany, Uruguay and the Netherlands found that lower-performing students who increased the amount of time they spent on math homework performed better in math, even one year later.

Other studies, however, suggest homework has minimal outcomes on academic performance: A 1998 study of more than 700 U.S. students led by a researcher at Duke University found that more homework assigned in elementary grades had no significant effect on standardized test scores. The researchers did find small positive gains on class grades when they looked at both test scores and the proportion of homework students completed. More homework was also associated with negative attitudes about school for younger children in the study. "The best educators figured out a long time ago that we can control what we can control," and that's what happens during the school day, Superintendent Garrett said, not homework. "There has been a shift away from it naturally anyway, and I felt like this made it equitable across our entire school system." "The best argument for homework is that mathematical procedures require practice, and you don't want to waste classroom time on practice, so you send that home," said Tom Loveless, a researcher and former teacher who has studied homework.

Ariel Taylor Smith, senior director of the Center for Policy and Action at the National Parents Union, said: "The thing they point to is that it's an equity issue, and not all parents have the same availability and ability to support their students. I would make the argument that if a kid is really far behind in school, that's an equity issue. They need the additional time to practice." Kids, she said, "need more practice ... Sometimes, you do have to practice the boring stuff, like math."

"The interesting issue for folks to consider is not should there be more homework, but should there be better homework," said Joyce Epstein, who has studied homework and is the co-director of the Center on School, Family, and Community Partnerships at the Johns Hopkins University School of Education. "Better homework in math might be knowing the fact that kids don't have to be practicing for hours, 10 to 20 examples," when they could establish mastery in less time.

An anonymous reader quotes a report from UploadVR: A group of independent researchers built a device that can artificially induce smell using ultrasound, with no consumable cartridges required. [...] The team of four are Lev Chizhov, Albert Yan-Huang, Thomas Ribeiro, Aayush Gupta. Chizhov is a neurotech entrepreneur with a background in math and physics, Yan-Huang is a researcher at Caltech with a background in computation and neural systems, and Ribeiro and Gupta are co-researchers on the project with software engineering and AI expertise.

Instead of targeting your nose at all, the device directly targets the olfactory bulb in your brain with "focused ultrasound through the skull." The researchers say that as far as they're aware, no one has ever done this before, even in animals. A challenge in targeting the olfactory bulb is that it's buried behind the top of your nose, and your nose doesn't provide a flat surface for an emitter. Ultrasound also doesn't travel well through air. The solution the researchers came up with was to place the emitter on your forehead instead, with a "solid, jello-like pad for stability and general comfort," and the ultrasound directed downward towards the olfactory bulb.

To determine the best placement, they say they used an MRI of one of their skulls to "roughly determine where the transducer would point and how the focal region (where ultrasound waves actually concentrate) aligned with the olfactory bulb (the target for stimulation)". [...] According to the researchers, they were able to induce the sensation of fresh air "with a lot of oxygen", the smell of garbage "like few-day-old fruit peels," an ozone-like sensation "like you're next to an air ionizer," and a campfire smell of burning wood. While technically head-mounted, the current device does require being held up with two hands. But as with all such prototypes, it likely could be significantly miniaturized.