fidzu : nerd

A Georgia data center developed by QTS used nearly 30 million gallons of water through two unaccounted-for connections before residents complained about low water pressure and the county utility discovered the issue. "All told, the developer, Quality Technology Services, owed nearly $150,000 for using more than 29 million gallons of unaccounted-for water," reports Politico. "That is equivalent to 44 Olympic-size swimming pools and far exceeds the peak limit agreed to during the data center planning process." From the report: The details were revealed in a May 15, 2025 letter from the Fayette County water system to Quality Technology Services, which outlined the retroactive charge of $147,474. The letter did not specify how many months the unpaid bill covered, but when asked about it Wednesday, Vanessa Tigert, the Fayette County water system director, said it was likely about four months. A QTS spokesperson said the timeframe was 9-15 months. Once the data center was notified, it paid all retroactive charges, a QTS spokesperson said in an email, noting the unmetered water consumption occurred while the county converted its system to smart meters. The Fayette County water system confirmed the data center's meters are now fully integrated and tracked. Tigert, the water system director, blamed the issue on a procedural mix-up. "Fayette County is a suburb, it's mostly residential, and we don't have much commercial meters in our system anyway," she said. "And so we didn't realize our connection point wasn't working." The incident became public last week when a county resident obtained the 2025 letter to QTS through a public records request and posted it on Facebook, prompting outrage from residents concerned about the data center's water consumption. [...] Tigert, who sent the 2025 letter to QTS, said the utility didn't know about the water hookups because the connection process "got mixed up" as the county transitioned to a cloud-based system while also trying to accommodate an industrial customer. Tigert also said her staff is small and at capacity. "Just like any water system, we don't have enough staff. We can't keep staff," she said. "I've got one person that's doing inspections and plan review, and so he's spread pretty thin." She said it's possible her staff did know about hookups but that she hadn't been able to locate the inspection report. "I may have hit 'send' too soon," she said about the 2025 letter to QTS. While the utility charged the data center a higher construction rate for the unapproved water consumption, Tigert confirmed the utility did not penalize or fine the data center. For what it's worth, the Blackstone-owned company says its data centers use a closed-loop cooling system that does not consume water for cooling. The reason for last year's high water use, according to QTS, was the temporary construction work such as concrete, dust control, and site preparation. Once the campus is fully operational, it should only use a small amount of water for things like bathrooms and kitchens. But that point could still be years away, as construction and expansion in Fayetteville may continue for another three to five years.

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12 May 2026 3:30am GMT

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12 May 2026 2:33am GMT

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12 May 2026 2:28am GMT

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12 May 2026 1:24am GMT

Digg is relaunching again, this time as an AI-focused news aggregator rather than the Reddit-style community site it recently abandoned. TechCrunch reports: On Friday evening, the founder previewed a link to the newly redesigned Digg, which now looks nothing like a Reddit clone and more like the news aggregator it once was. This time around, the site is focused on ranking news -- specifically, AI news to start. In an email to beta testers, the company said the site's goal is to "track the most influential voices in a space" and to surface the news that's actually worth "paying attention to." AI is the area it's testing this idea with, but if successful, Digg will expand to include other topics. The email warned that the site was still raw and "buggy," and was designed more to give users a first look than to serve as its public debut. On the current homepage, Digg showcases four main stories at the top: the most viewed story, a story seeing rising discussion, the fastest-climbing story, and one "In case you missed it" headline. Below that is a ranked list of top stories for the day, complete with engagement metrics like views, comments, likes, and saves. But the twist is that these metrics aren't the ones generated on Digg itself. Instead, Digg is ingesting content from X in real-time to determine what's being discussed, while also performing sentiment analysis, clustering, and signal detection to determine what matters most. [...] The site also ranks the top 1,000 people involved in AI, as well as the top companies and the top politicians focused on AI issues.

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11 May 2026 11:00pm GMT

Production-version patches are coming online and should be installed pronto.

11 May 2026 10:28pm GMT

Audi made sure to consult American tastes for its first full-size SUV.

11 May 2026 10:01pm GMT

Nvidia's real AI moat isn't "a piece of hardware," writes Wired's Sheon Han. It's CUDA: a mature, deeply optimized software ecosystem that keeps machine-learning workloads tied to Nvidia GPUs. An anonymous reader quotes a report from Wired: What sounds like a chemical compound banned by the FDA may be the one true moat in AI. CUDA technically stands for Compute Unified Device Architecture, but much like laser or scuba, no one bothers to expand the acronym; we just say "KOO-duh." So what is this all-important treasure good for? If forced to give a one-word answer: parallelization. Here's a simple example. Let's say we task a machine with filling out a 9x9 multiplication table. Using a computer with a single core, all 81 operations are executed dutifully one by one. But a GPU with nine cores can assign tasks so that each core takes a different column -- one from 1x1 to 1x9, another from 2x1 to 2x9, and so on -- for a ninefold speed gain. Modern GPUs can be even cleverer. For example, if programmed to recognize commutativity -- 7x9 = 9x7 -- they can avoid duplicate work, reducing 81 operations to 45, nearly halving the workload. When a single training run costs a hundred million dollars, every optimization counts. Nvidia's GPUs were originally built to render graphics for video games. In the early 2000s, a Stanford PhD student named Ian Buck, who first got into GPUs as a gamer, realized their architecture could be repurposed for general high-performance computing. He created a programming language called Brook, was hired by Nvidia, and, with John Nickolls, led the development of CUDA. If AI ushers in the age of a permanent white-collar underclass and autonomous weapons, just know that it would all be because someone somewhere playing Doom thought a demon's scrotum should jiggle at 60 frames per second. CUDA is not a programming language in itself but a "platform." I use that weasel word because, not unlike how The New York Times is a newspaper that's also a gaming company, CUDA has, over the years, become a nested bundle of software libraries for AI. Each function shaves nanoseconds off single mathematical operations -- added up, they make GPUs, in industry parlance, go brrr. A modern graphics card is not just a circuit board crammed with chips and memory and fans. It's an elaborate confection of cache hierarchies and specialized units called "tensor cores" and "streaming multiprocessors." In that sense, what chip companies sell is like a professional kitchen, and more cores are akin to more grilling stations. But even a kitchen with 30 grilling stations won't run any faster without a capable head chef deftly assigning tasks -- as CUDA does for GPU cores. To extend the metaphor, hand-tuned CUDA libraries optimized for one matrix operation are the equivalent of kitchen tools designed for a single job and nothing more -- a cherry pitter, a shrimp deveiner -- which are indulgences for home cooks but not if you have 10,000 shrimp guts to yank out. Which brings us back to DeepSeek. Its engineers went below this already deep layer of abstraction to work directly in PTX, a kind of assembly language for Nvidia GPUs. Let's say the task is peeling garlic. An unoptimized GPU would go: "Peel the skin with your fingernails." CUDA can instruct: "Smash the clove with the flat of a knife." PTX lets you dictate every sub-instruction: "Lift the blade 2.35 inches above the cutting board, make it parallel to the clove's equator, and strike downward with your palm at a force of 36.2 newtons." "You can begin to see why CUDA is so valuable to Nvidia -- and so hard for anyone else to touch," writes Han. "Tuning GPU performance is a gnarly problem. You can't just conscript some tender-footed undergrad on Market Street, hand them a Claude Max plan, and expect them to hack GPU kernels. Writing at this level is a grindsome enterprise -- unless you're a cracker-jack programmer at DeepSeek..." Han goes on to argue that rivals like AMD and Intel offer competitive specs on paper, but their software stacks have struggled with bugs, compatibility issues, and weak adoption. As a result, Nvidia has built an Apple-like moat around AI computing, leaving the industry dependent on its expensive hardware.

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11 May 2026 10:00pm GMT

FCC extends waiver allowing routers and drones to get patches for two more years.

11 May 2026 8:48pm GMT

Photoflare 1.7 returns after years without a release, adding Qt 6, G'MIC filters, a rewritten canvas engine, and many editing improvements.

11 May 2026 7:51pm GMT

Tails 7.7.3 fixes the Dirty Frag Linux kernel vulnerability with kernel 6.12.86 and updates Tor Browser, Tor, and Thunderbird.

11 May 2026 4:14pm GMT

SparkyLinux 8.3 ships refreshed Debian 13.4-based KDE Plasma, LXQt, MATE, Xfce, and Openbox editions.

11 May 2026 1:54pm GMT