Anthropic is urging leading AI labs to consider slowing development, warning that frontier models are advancing fast enough that they may soon be able to improve themselves without direct human intervention. The company says a global ability to pause or slow AI development would "likely be a good thing," citing internal data about accelerating model capabilities. From a blog post: Using public benchmarks and previously unreported data from within Anthropic, The Anthropic Institute is showing that AI is already accelerating the development of AI systems. To take just one example: today, Anthropic engineers on average ship 8x as much code per quarter as they did from 2021-2025. The technical trends discussed in this piece suggest that AI systems are going to become much more capable in coming years. These trends have huge implications. AI that can build itself would be a major development in the history of technology -- one that could bring enormous good for the world in science, healthcare, and beyond. But full recursive self-improvement also might increase the risks of humans losing control over AI systems. If systems are capable of fully building their own successors, the ways we secure them, monitor them, and shape their behavior all grow much more important. [...] If it were possible to effectively slow the development of this technology to give ourselves more time to deal with its immense implications, we think that would likely be a good thing. But if a slowdown simply lets the least cautious actors catch up technologically, it could leave everyone less safe. Without a global coordination mechanism, companies and governments will have to make difficult decisions about safety while under competitive and geopolitical pressures. We believe it would be good for the world to have the option to slow or temporarily pause frontier AI development to enable societal structures and alignment research to keep up with the advance of the technology. The Anthropic Institute will conduct research -- in collaboration with many others -- and take actions to help build the systems that a credible slowdown or pause would require. These systems would enable frontier AI developers to verify that others globally have actually stopped or slowed, and that a bad actor could not use the auspices of a coordinated slowdown to jump ahead in secret. If such systems existed, we expect that we would slow down or temporarily pause, if other developers at or near the frontier also did so in a verifiable manner...

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Meta has reportedly delayed the developer release of its Muse Spark AI model API multiple times, and as of Tuesday, had no scheduled launch date, according to the Wall Street Journal (paywalled). Reuters reports: A Meta spokesperson told Reuters on Wednesday that the company is already testing the Application Programming Interface (API) with some early partners and is looking forward to releasing it this month. "The muse spark API will be coming soon," Meta AI Chief Alexandr Wang announced in a post on X in April. Meta unveiled Muse Spark in April as the first model built to close the gap with rivals. Muse Spark is the first in a new series of models created by the company's Superintelligence Labs. Earlier on Wednesday, Meta unveiled an AI agent aimed at helping businesses carry out day-to-day operations, hinting at the company's ambitions to compete with rivals such as OpenAI, Anthropic and Alphabet's Google.

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Meta is scaling back parts of its employee tracking initiative after staff objected to software that collected mouse movements, clicks, keystrokes, and other actions for AI training data. According to Reuters, the company will now let workers pause collection for up to 30 minutes and request exemptions. Reuters reports: [Stephane Kasriel, a vice president in Meta's AI model-building Superintelligence Labs unit] said the team behind the software had also introduced "several optimizations" to reduce its impact on computer battery life, after employees complained it was consuming so much data it was causing their home internet usage to spike. "While we remain confident in the privacy protections we put in place at launch, which went through several layers of risk review, we have heard your concerns about personal data on work devices, battery life, and wanting more control over when capturing happens," Kasriel said in the memo.

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An anonymous reader quotes a report from GeekWire: A team inside Microsoft has been quietly building a platform for devices that run AI agents instead of apps, based on Android instead of Windows, with two working hardware designs so far, and an initial set of big-name companies lined up to run pilots. The platform, dubbed "Project Solara," is Microsoft's bet that AI will open up entirely new scenarios for computing -- using agents to avoid the constraints of traditional software, and off-the-shelf components to develop new devices quickly and inexpensively. [...] The company unveiled Solara on Tuesday at its Build conference in San Francisco, describing it as a new platform that spans from chip to cloud. GeekWire got a behind-the-scenes look at the project during a briefing last week in Redmond, including demos of the first two concept devices based on the platform: - A desktop hub that sits beside a PC and responds to voice commands, signs users in using facial recognition, and surfaces the day's most pressing items. With a monitor attached, it becomes a full Windows machine running in the cloud. - A wearable badge that reimagines the standard employee ID card. A fingerprint button wakes an agent in one press; a single tap records and transcribes a conversation; and a built-in camera lets the agent act on what the user sees. Microsoft says it won't ship these devices itself. Instead, it envisions hardware makers and other industry partners turning the reference designs into implementations of their own, each intended for a specific industry, company, or scenario. For example, in one demo shown by the company, the high-tech badge ran on agents designed for use by a health-care worker, including the ability to scan a patient's QR code, record and transcribe the visit, log vitals, and start a prescription. In another application of the same badge, the built-in camera scanned a brainstorm board with ideas for an office revamp, and made a suggestion: add some plants. The two devices are a starting point. The bigger opportunity, the company says, is all the tasks and workflows where a PC or phone gets in the way or isn't practical to use. [...] In the coming months, companies including AccuWeather, Best Buy, CVS Health, Levi's, and Target are expected to begin pilots of devices based on the reference designs. The operating system is the Microsoft Device Ecosystem Platform, or MDEP, an enterprise version of Android that Microsoft developed for devices including Teams meeting-room hardware. The company says it chose MDEP over Windows deliberately, to run on smaller, lower-power devices while keeping the management and security features IT departments expect: patch and over-the-air updates, device integrity, Microsoft Defender, Intune, and Entra ID sign-in. While the project is still in the early stages, Microsoft CEO Satya Nadella encouraged the team to show it at Build sooner than the company would normally show its work in public. "That underscores just how competitive and fast-moving the AI world is right now, but it also illustrates the pace that the new technologies are enabling," reports GeekWire. The report notes that the business model for the platform still needs to be worked out. The devices run on Microsoft's Azure cloud, but beyond that, "the economics are still taking shape." Qualcomm and MediaTek have been chosen as the first chip partners. "The badge runs on a new Qualcomm wearable chip; the desk hub runs on MediaTek IoT silicon," reports GeekWire. "Both are off-the-shelf, not custom, which is central to how Microsoft plans to keep devices cheap and fast to build."

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Longtime Slashdot reader Matt_Bennett shares a blog post from Adafruit: Adafruit received at 10:38 p.m. ET on May 22, 2026 a letter from former FBI chief of staff, Jonathan F. Lenzner, and partner at Fenwick & West LLP, counsel for Flux, demanding, among other things, that Adafruit refrain from publishing an article addressing what the letter characterizes as false and potentially defamatory claims about Flux, including statements about Flux's intellectual property, commercial traction and user base. The letter further asserts claims under the Computer Fraud and Abuse Act. Adafruit accessed only information that Flux's own systems made publicly available through a server misconfiguration. Adafruit's reporting concerns a matter of public security interest and was conducted in the ordinary course of responsible disclosure. Although Adafruit vigorously rejects the assertions made in Flux's May 22, 2026 demand letter, we have temporarily stopped publishing on the Adafruit blog while we consider our response and next steps. We will update the community as appropriate. For context, Adafruit is a major open-source hardware company and electronics retailer known for its maker-focused boards, components, tutorials, and community publishing. Flux.ai is relevant because it is building an AI-assisted circuit-board design platform aimed at changing how engineers create and collaborate on PCB designs. "Adafruit probably did a review of AI PCB tools," writes HN user karmicthreat. "I've used Flux.ai before; it was a pretty bad experience. After about 50-100$ in tokens a couple of times, I couldn't get more than a couple of simple components on the schematic. And not in sensible positions..." Redditor AlexTaradox adds: "Nothing was published as far as I know. I assume they did review of AI tools and likely contacted flux with some preliminary results, but flux saw where it is going and decided to block them from publishing any results. Flux is garbage and they obviously know it, but they need to hold for some time until some other scam acquires them. Doing anything with them is just asking to be screwed..." Further discussions are taking place on Reddit and Hacker News.

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An anonymous reader quotes a report from Ars Technica: In April, GitHub announced that it was moving subscribers from request-based billing to a usage-based model for its AI-powered Copilot service. As that new pricing model goes into effect today, many GitHub Copilot users are reporting some extreme sticker shock as they realize just how quickly their previous "normal" usage is burning through their newly limited monthly allotment of AI credits. Across social media and forums, many Copilot users are sharing personal statistics showing how just a few hours of AI usage can now account for a large chunk of their new monthly subscription caps. For some users, it reportedly took less than a day to use up a month's usage quota. That's a big change from previous months, when GitHub Copilot subscribers were allocated a certain number of "requests" and "premium requests" based on their payment tier. GitHub said that the old system meant that "a quick chat question and a multi-hour autonomous coding session [could] cost the user the same amount," forcing Copilot itself to "absorb much of the escalating inference cost behind that usage." [...] Indeed, some Copilot users have been sharing estimates from GitHub's own tool showing that their previous monthly usage would rack up bills in the thousands of dollars under the new pricing plan. Under GitHub's new usage-based pricing system, paid Copilot subscriptions instead grant users a certain number of AI "credits" each month, with one credit corresponding to $0.01 of usage. Subscribers also get bonus credits depending on their subscription level: the $10/month Pro plan includes 1,500 credits ($15 worth); the $39 Pro+ plan includes 7,000 credits ($70 worth); and the $100/month Copilot Max plan includes 20,000 credits ($200 worth). The precise number of Copilot credits used by a given prompt is determined by the number of input and output tokens used and the rates charged by the underlying large language model. That means pricing is highly dependent not just on the type of request but on the specific model that a user chooses. One million output tokens from OpenAI's GPT-5.4 nano would run just $1.25 on GitHub Copilot, but that same level of output would run $30 on the frontier GPT-5.5 model (Copilot users who rely on "Auto" mode to pick the most appropriate available model for any request should be extremely careful, as some users report it can switch to expensive models for extremely simple queries).

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An anonymous reader quotes a report from NPR: The buzz on college campuses is that AI is disrupting the job market for young college graduates. But new research from the Federal Reserve Bank of New York finds that the culprit may be something else: remote work. An analysis of federal employment data, paired with a deep dive into the flexible work arrangements at one unnamed Fortune 500 tech company, reveals that companies are less likely to hire recent college grads into occupations that can be done remotely. Researchers speculate that employers are reluctant to put such workers in a setting where it's harder to absorb lessons from coworkers. The researchers found the unemployment rate among younger college grads -- those under the age of 29 -- rose 20% after the pandemic, while unemployment among older college grads fell slightly. The study compares unemployment rates pre-pandemic, from 2017 to 2019, with unemployment rates after the pandemic, from 2022 to 2024. Unemployment rose as remote work grew fourfold, the researchers write. "Our analysis suggests that these trends are related, with remote work making it more difficult for managers to train and mentor new employees." Regardless of the cause, the New York Fed report warns that a high unemployment rate among young college grads is concerning. "Early-career experiences can have lasting consequences," the researchers write. "Research finds that individuals who began looking for jobs in slacker labor markets tend to have lower earnings and slower career progression relative to comparable peers who began their job search in better market conditions." Further reading: Why Is the US Job Market So Tough, Especially for Recent College Grads?

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An anonymous reader quotes a report from Politico: Anthropic has extended an invitation to the European Commission granting the EU's cyber agency access to its powerful AI hacking tool Mythos, according to a Commission official familiar with the process. The AI firm made the formal invitation after a meeting with the Commission in San Francisco last Thursday, the official said, adding the EU now has to put in place a mechanism to access the model with proper security safeguards. European Commission spokesperson Thomas Regnier said in a statement the Commission has had "several productive meetings with Anthropic" and "welcome[d] the latest developments on potential future access." [...] "This latest development is of utmost importance to get a clear picture on the potential risks," Regnier said, adding: "Let's not forget that Mythos is not one off, a new wave of powerful models are coming to the market." An ENISA official said the agency does not have active access now but is working to implement it. The Commission is working on a formal action plan to respond to powerful AI hacking tools. It has indicated it wants to release it before the summer break, according to an industry official. Anthropic's Mythos was unveiled in early April and triggered fears that it could enable large-scale attacks with its ability to find and exploit vulnerabilities. "European authorities for weeks were shut off from accessing the cutting-edge cybersecurity AI tech, leading to urgent calls by European politicians and government officials to gain access," notes Politico. "Cyber officials also called for Europe to build its own version."

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A historian-turned-software engineer warns that "so little is ever written down" by professional programmers in a new article for Fast Company: Perhaps there's an early design doc, but then it turns out that everything was substantially revised before work began. Maybe there are a few wiki pages explaining known issues, some of which were solved a long time ago and others that have been left to molder in the codebase. Somebody might have left a comment in the code itself, but typically it's a warning not to change something or else something else will break... Software engineering has an ambivalent relationship with documentation. Everyone agrees documentation matters in theory, but in practice it's inconsistent, outdated, or missing entirely. Part of that is simple inertia. Writing documentation is usually less interesting than writing the code itself. But it's also ideological. The Agile movement emerged in part as a reaction against the heavily documented Waterfall methodology, and one of Agile's core values explicitly prioritizes "working software over comprehensive documentation." In escaping bureaucratic overdocumentation, the industry also normalized underdocumentation. High turnover at software jobs always brings "a constant drain of domain knowledge." And he's he's skeptical that generative AI will be able to fill in those gaps: [H]aving it generate documentation on the codebase itself might sound like a solution to the absence of other written information. LLMs can certainly summarize code back to you. But hold up with that idea. Beyond hallucinations, there's a deeper problem: Writing documentation is itself part of the thinking process. Whether I'm writing history or software, putting an approach into words helps refine it before I sink hours into implementation. Documentation also captures intent. An LLM may be able to summarize what a codebase does, but it cannot reliably explain why a developer chose one approach over another, or what trade-offs shaped that decision... An LLM can read code that I've written. It might even scan a large codebase and accurately summarize what it's doing. But it can't assess authorial intent. Thanks to long-time Slashdot reader smooth wombat for sharing the article.

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"In the future, AI agents will be able to find one another using the Domain Name System (DNS), instead of crawling about and probing ports or checking configured resources," writes The Register. InfoWorld writes that "numerous proprietary agent registries are on the market, but the Linux Foundation suggests we simply extend the distributed, open Domain Name System (DNS) infrastructure we already have." The foundation is now inviting contributions to the DNS-AID project, a standard way for AI agents to discover, verify, and communicate with one another over DNS that requires no new infrastructure. It enables agents and Model Context Protocol (MCP) servers to use DNS as a global, vendor-neutral directory. While many details remain to be worked out, the proposal suggests domain owners create a new well-known address that can provide a starting point for agents looking for one another: _index._agents.{domain}. This approach ensures that agent discovery remains scalable, secure, and compatible with the protocols that underly the internet, the Linux Foundation said. The Linux Foundation descrbes DNS-AID as enabling a standard way for AI agents to discover and communicate with one another. "By leveraging the internet's existing Domain Name System (DNS) infrastructure, DNS-AID provides a robust, decentralized alternative to the centralized registries and hardcoded URLs currently limiting AI interoperability." The standard was originally developed by Infoblox, their announcement notes, but "Because the protocol is implementation-agnostic, it functions across any DNS provider, ensuring that organizations maintain control over their agent infrastructure without relying on proprietary, centralized services."

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The state of Ohio — one of America's hot regions for data center construction — "is suspending a tax break that has been critical to its competition with other states," reports the Associated Press. The move "comes as tax breaks for energy-hungry AI data centers are increasingly playing a role in state budgets," the article points out. But they also note the expanding data center industry "is under pressure to pay the full costs" The size of Ohio's tax break skyrocketed, dwarfing previous projections, as opposition to data centers is sweeping through cities, suburbs and towns there and prompting lawmakers to form a committee to study the impact. In the meantime, residents are trying to bypass the GOP-controlled Legislature and get a referendum on November's midterm election ballot that's designed to permanently ban hyperscale data centers, likely the strictest such statewide ban under consideration in the U.S... The state, in 2024, had used previous history in projecting that the exemption would total $136 million in fiscal 2025 and $142 million in fiscal 2026. It was $554 million in 2024 and nearly $1.6 billion in 2025, the state reported... State tax breaks for the massive data center industry are facing growing criticism by governors and lawmakers... Thirty-eight states have some form of a sales tax break for data centers, according to the National Conference of State Legislatures... [Though many were passed before 2022, when data centers were smaller.] Ohio's exemption is fairly broad, applying not only to construction materials, but to the expensive equipment — such as server racks and cooling systems — used in data centers. Operators might buy new server racks every couple of years as the technology improves.

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The Zig programming language wants to be a modern alternative to C (including better memory safety features). It's maintained by as an open-source project by a 501(c)(3) nonprofit and a network of contributors. But Business Insider notes that Zig bans the submission of AI-assisted code: On the JetBrains podcast, Zig President Andrew Kelley called AI-assisted contributions "invariably garbage." "People are sending us contributions that have no value whatsoever," Kelley said. "They have negative value, because they take review time away from the team...." There are more pull requests than reviewers. At the time of the recording, Kelley said that Zig had 200 open pull requests. Those AI-generated "slop contributions" slow the whole team down even more, Kelley said. "We've wasted everybody's time...." Big Tech companies have projected lofty goals for the percentage of code that should be — and already is — written with AI. Zig doesn't have a mandate to be maximally efficient like these public companies. Instead, "mentorship" is part of its core mission, Kelley said, making AI contributions counterproductive. "We're all trying to get better at programming," Kelley said. "People who are sending AI pull requests, those people are not helping this goal."

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After Google announced AI-emphasizing changes to its search results, many web surfers began defecting to DuckDuckGo, reports TechCrunch. (They describe DuckDuckGo as "a privacy-focused alternative" that accounts for around 2% of the U.S. search market...) DuckDuckGo said U.S. app installs went up 18.1% week-over-week on average during the May 20 to May 25 period, compared to May 13 to May 18. The company said that growth was sustained for six consecutive days and peaked at 30.5% on May 25. On iOS, the rate of install is even higher, with week-over-week growth hitting a 33% average, peaking at 69.9%... DuckDuckGo said the trend is stronger in the U.S, and that DuckDuckGo continued to gain users over the Memorial Day weekend, when it usually sees a dip in traffic. Some of that data is backed up by third parties. App analytics company Apptopia found a 29% increase in average daily downloads in the U.S. and a 12% increase globally over the same period. DuckDuckGo also said visits to its AI-free search page, noai.duckduckgo.com averaged 22.7% week-over-week growth, peaking at 27.7% on May 24, according to the article. ("DuckDuckGo also offers an AI Image Filter that filters out AI-created images from search results.") TechCrunch delves into the reason why: I overheard a woman on the phone saying she was switching to DuckDuckGo because you can "opt out of using AI... Google just isn't Google anymore," she said. It seems that others had the same idea... Some have argued it will kill the open web, while others shared concerns that AI overviews surface inaccurate responses and take away control from users who might not want to use AI. It also overcomplicates simple things. A Google spokesperson pointed out that AI Mode isn't the default in their search results. (And CNET notes Google include an AI-free "Web" choice in its results if you just want a page of ftraditional blue links.) TechCrunch adds that DuckDuckGo also offers a separate free tool called Duck.ai offering access to models including Claude, Meta's Llama and OpenAI's GPT-5 mini. "All chats are private because DuckDuckGo strips the user's IP address before requests reach model providers, deletes conversations within 30 days, and prevents chats from being used for training."

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Security company Okta shot up 30% Friday, reported CNBC, while data platform provider Snowflake jumped 50% this week. They see it as part of a larger trend where software stocks "soared this week," signaling "some companies are navigating their way through AI disruption better than Wall Street expected" and that investors "may have been too quick to declare the end of software with the emergence of AI. Even as AI displaces certain tools and job functions, many software companies continue to show growth, assisted by their own AI products..." The "SaaSpocalypse" may not be over. But for now at least, fears of software's demise have cooled... The iShares Expanded Tech-Software exchange-traded fund rose 8% this week and closed May up 21%, the best monthly performance for the ETF since October 2001. Back then it was a brief rebound during the dot-com bust, while the current rally comes as concerns about the impact of AI ripple across the sector. Software names have been hit particularly hard over the past year due to the boom in so-called vibe coding, with users able to now build apps and websites in minutes thanks to offerings from Anthropic, OpenAI and others... Elsewhere in the software space, Atlassian climbed 26% for the week and ServiceNow surged over 20%, while Shopify, Workday and Asana each gained at least 14%.

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Anthropic has released Claude Opus 4.8 with stronger performance and better handling of uncertain or flawed data, including a greater tendency to flag issues rather than make unsupported claims. The update also introduces a "Dynamic Workflows" research preview for coordinating complex tasks across many subagents. TechCrunch reports: Opus 4.8 comes with the expected best-in-class benchmark results, but there's also particular attention to how the model manages bad or uncertain data. In the launch post, Anthropic's early testers found that the new model is "more likely to flag uncertainties about its work and less likely to make unsupported claims." Echoing this point, a testimonial from Bridgewater associates said the biggest difference in the upgrade was "Opus 4.8's tendency to proactively flag issues with the inputs and outputs of an analysis, something other models routinely missed and left to the users to catch." Together with the new model, Anthropic launched a feature called Dynamic Workflows, which will be available in research preview. The system is designed to help larger models like Opus manage complex tasks across hundreds of parallel subagents. "Claude Code alongside Opus 4.8 can now carry out codebase-scale migrations across hundreds of thousands of lines of code from kickoff to merge, with the existing test suite as its bar," the post explains. As for Mythos, Anthropic's most advanced model, the company hinted it could be made publicly available in the not too distant future. "We're making swift progress on developing these safeguards and expect to be able to bring Mythos-class models to all our customers in the coming weeks," the company wrote.

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Robinhood is launching beta support for a new feature that will let AI agents make payments and trade stocks on users' behalf. The company is also rolling out a virtual credit card for AI agents, with spending limits and approval controls. TechCrunch reports: Robinhood said users on its platform can now create a separate account for their AI agents and connect them to a dedicated wallet. While these agents would be able to read and analyze users' portfolios to come up with trading strategies and suggest investments, they'll only be able to access the pre-loaded balance in the dedicated wallet to place orders. Users will get notifications of all trades their AI agent makes and will be able to monitor their activities within the Robinhood app. For some trades, agents will show a preview that users may have to approve before the order is executed. The company said it has also built in fraud detection protection, in which a team from Robinhood would review suspicious trades and help users resolve disputes. Robinhood says users can connect their AI agents to its Model Context Protocol (MCP) service to do things like analyze concentration risk and sector exposure, execute trades, or look through analyst notes to identify new investment opportunities across various sectors. The agentic trading feature is launching in beta and only allows stock trading right now. The company says it plans to add support for options, crypto, event contracts, futures, and prediction markets soon.

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Linux stable kernel maintainer Greg Kroah-Hartman says Rust can help Linux deal with a flood of AI-discovered security bugs (namely Dirty Frag, Copy Fail, and Fragnesia) by preventing common C mistakes around memory, locking, error handling, and untrusted data at build time rather than during human review. It's "not a silver bullet" and does not mean rewriting the whole kernel, but he said new drivers and subsystems will increasingly use Rust as Linux evolves forward. ZDNet reports: Kroah-Hartman illustrated those pitfalls with real C bugs in the kernel, including a 15-year-old Bluetooth bug that dereferenced a pointer without checking it and a Xen bug where "we forgot to unlock" in an error path. "The majority of the bugs in the kernel are this tiny, minor stuff," he explained. "Error conditions aren't checked, locks aren't forgotten, unreleased memories leak, and vulnerabilities add up over time. They crash the kernel. This is what we live with in C. This is why we don't like it." Kroah-Hartman argued that the "best beauty of Rust" is catching those mistakes at build time rather than in review. For example, when it comes to locking, he highlighted Rust's locking abstractions in the kernel: "The only way you can get access to inner pointers of structures is by grabbing that lock, and releasing the lock automatically. The compiler does it, it's guarded, the lock happens, everything's happy. You just can't write code to access these values...without grabbing the lock. The compiler will not let you." Those properties, he argued, directly remove a huge fraction of the bugs he sees: "This is going to save us those two things. First, 60% of the bugs in the kernel right there, they're gone. Thank you." The payoff is earlier, more automated enforcement: "If this happens at build time, not review time, don't make me a maintainer who has to read your code [and] say, 'Oh, then you properly check that error value. Oh, did you properly grab the locks in the right spot?' Rust gives us that for free. This is the best thing ever." Even if Rust vanished tomorrow, Kroah-Hartman argued, it has already forced the kernel to clean up C code and interfaces. He credited Rust's influence outright: "We stole this from Rust. Thank you. It's a good idea, so if Rust disappeared tomorrow, we have cleaned up the C code in the kernel so much and taken in the ideas. We thank you, you've made Linux better with it just by existing." [...] What ultimately sold a number of core maintainers, including him, on Rust was how it "makes reviewing code easier." With CI [Continuous Integration] bots enforcing builds and Rust's type system enforcing key invariants, maintainers can "focus on the logic" rather than resource bookkeeping: "I can care about that one function. I don't have to worry about the rest of this stuff, because I assume that it works properly, because it was built properly." Internally, he said, the top maintainers have already made their call on Rust's status: "The Linux kernel maintainers, we get together every year and talk about what the processes are doing. Last year, we said the Rust experiment is over. It's not an experiment. This is for real." The rationale: "The people behind it are real. We trust them. We know what they're doing. They've shown and put in the work to make Rust a viable language in the kernel, and we're going to make this stick. Let's go full speed ahead. And, as always," he said wryly, "world domination proceeds." "If you never remember anything else in my talk, just remember these four words. It came from Microsoft Security many, many years ago," Kroah-Hartman told attendees. "They realized all input is evil. You have to validate all input."

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An anonymous reader quotes a report from The Verge: How newsrooms should use AI -- or if they should at all -- has been a recurrent debate within the media industry over the last several years. Increasingly, these rules are being hammered out at the bargaining table between unions and publishers. Right now, employees at The New York Times are gearing up for a fight. Unionized staff with the Tech Guild say Times management has refused to provide the union with information related to how the company has used AI, its plans for AI use in the future, and how it will affect employees' jobs and workflow. (The union filed an unfair labor practice charge earlier this month.) The Tech Guild, a NewsGuild of New York unit of around 700 software engineers, designers, product and project managers, and data analysts, also filed grievances saying Times management violated their collective bargaining agreement when it started using two internal AI tools that track and evaluate employee performance and activity. [...] Both the Tech Guild and the Times Guild (which represents 1,500 editorial, ad sales, and support staff at the Times) filed unfair labor practice charges against the Times, saying that company violated labor law by refusing to respond to their requests for information around AI use at the outlet. The Times did not respond to specific questions about how it uses DX and Glean, but spokesperson Danielle Rhoades Ha said in an email that the company disagrees with the characterizations made in grievances and that it would respond as part of its "normal contractual process." "Likewise, we will respond to this Request for Information (RFI) in due course as we've done with 80+ other RFIs from the Guild in recent years," Rhoades Ha said. The Times Guild is currently bargaining a new contract, pushing for robust protections against AI, like requirements that a human is behind any AI tool being used, that any journalism utilizing AI is transparently labeled, and that staff are compensated for AI model training deals the company might make. The Times deploys artificial intelligence tools for some reporting, like using it to parse millions of documents related to Jeffrey Epstein or scan satellite images of Gaza to try to find where Israel had dropped a specific kind of bomb. [...] [Ben Harnett, a software engineer at the Times and chair of the unit's generative AI committee] emphasizes that the unit's position is not that AI shouldn't ever be used, but that workers should have a say in how it's deployed. Metrics like how many tokens an employee uses or how often they're using AI to do their jobs create pressure to do more and incentives that don't align with doing quality work. "It's going to distract [you] from actually doing a good job, which is what we think the company should want," he says. Two of the contentious AI tools mentioned in the report are DX and Glean. DX is an engineering productivity tool that tracks a developer's output, generative AI use, efficiency, and other related metrics. Meanwhile, Glean is an internal knowledge-search tool that indexes materials like wikis, GitHub documents, Google Docs, and emails so employees can query company information. The concern, according to Times Tech Guild members, is that data meant to measure broader developer experience is now being applied to individuals and cited in performance or disciplinary contexts. There's also worry that it could be used to monitor individual contributions and produce false or misleading results.

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An anonymous reader quotes a report from TechCrunch: There is a certain wildness in the tech industry these days that both mimics previous eras of large changes, like cloud computing (runaway costs in the early days), and is like nothing we've ever seen before (record revenues accompanied by mass layoffs). One possible explanation: tech executives, especially CEOs, are collectively suffering from delusions of AI grandeur. And at least one tech CEO has said as much out loud: Box founder Aaron Levie. "CEOs are uniquely prone to AI psychosis because they're sufficiently distant from the last mile of work that still has to happen to generate most value with AI," Levie wrote on X. CEOs "play with AI," develop a prototype, or generate a contract, to use Levie's examples, and then make the leap to believing agents can do the work. But these top-level executives aren't the people who have to review code, discover bugs, and identify calls to hallucinated libraries before software is deployed. They aren't responsible for training AI models on a company's idiosyncratic contract terms, nor do they have to spend days combing through contracts to find sneaky terms, as Levie indicates. In other words, Levie's theory posits, CEOs don't really understand processes well enough to know what really can and can't be automated. But that lack of knowledge doesn't stop them from acting on their beliefs. [...] So what are CEOs to do instead? Levie advises CEOs to use AI "a ton" to really see what it can and can't do, "and come out the other side with an appreciation for both the upside and the real work."

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joshuark writes: BusPatrol, a company that has installed AI-powered cameras in tens of thousands of school buses around the U.S., now plans to turn those cameras into automatic license plate readers (ALPRs), capturing the location of every vehicle the buses drive past, and give that data to law enforcement, 404 Media has learned. BusPatrol has already taken steps to share the collected data with law enforcement contracting giant Axon, according to leaked BusPatrol documents and a source with knowledge of the plans. BusPatrol has acknowledged how controversial its plan to collect and share this data is, pointing specifically to concerns about ICE using license plate data, but emphasizes the likely success of selling the angle of protecting children. "Who would have thought that school buses would be turned into the mass surveillance state?," Michael Soyfer, an attorney from the Institute for Justice, which has various ongoing ALPR-related lawsuits The Institute for Justice argues that warrantless use of ALPR systems is unconstitutional, describing similar systems as a "dragnet." Kate Spree, senior manager of brand communications at BusPatrol, said in an email "This inquiry is based on a false premise and inaccurate information. BusPatrol does not pool or sell data across communities; student safety program data is used only to support the BusPatrol program in the community where that data was created." When 404 Media asked clarifying questions and said that the reporting is based on leaked BusPatrol material, Spree stopped replying to text messages and emails. This plan gives new meaning to the animated cartoon series "The Magic School Bus"... Further reading: FBI Wants to Buy Nationwide Access to License Plate Readers

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