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Buyers still struggling to differentiate data platforms in era of AI

Cloud data platform vendor Snowflake has made its set of PostgreSQL extensions open source in a bid to help developers and data engineers integrate the popular open source database with its lakehouse system.…

Retailer's tech systems aren’t down anymore, but the same can’t be said for its rocky financials

Marks & Spencer says its April cyberattack will cost around £136 million ($177.2 million) in total.…

Supply chains also unprepared for liquid cooling demands

A survey of datacenter professionals reveals that supply chain constraints and power availability are hampering the industry's efforts to scale datacenter capacity.…

A ‘three-letter person’ experiments with the new type-safe C, and is impressed

Famed mathematician, cryptographer and coder Daniel J. Bernstein has tried out the new type-safe C/C++ compiler, and he's given it a favorable report.…

President Trump has re-nominated tech billionaire and private astronaut Jared Isaacman to lead NASA, reversing his earlier withdrawal over concerns about Isaacman's political affiliations. CBS News reports: Mr. Trump nominated Isaacman to the Senate-confirmed post last year, but announced in late May he had decided to withdraw Isaacman after a "thorough review" of his "prior associations." Weeks after the withdrawal, the president went further in expressing his concerns about Isaacman's credentials. At the time, Mr. Trump acknowledged that he thought Isaacman "was very good," but had been "surprised to learn" that Isaacman was a "blue-blooded Democrat, who had never contributed to a Republican before." [...] Mr. Trump made no mention of his previous decision to nominate and then withdraw Isaacman in his Tuesday evening announcement of the re-nomination on his Truth Social platform. "This evening, I am pleased to nominate Jared Isaacman, an accomplished business leader, philanthropist, pilot, and astronaut, as Administrator of NASA," Trump posted. "Jared's passion for Space, astronaut experience, and dedication to pushing the boundaries of exploration, unlocking the mysteries of the universe, and advancing the new Space economy, make him ideally suited to lead NASA into a bold new Era."

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After a £312M upgrade to the retiring OS, Defra still has 24,000 devices to replace

The UK's Department for Environment, Food & Rural Affairs (Defra) has spent £312 million (c $407 million) modernizing its IT estate, including replacing tens of thousands of Windows 7 laptops with Windows 10 – which officially reached end of support last month.…

Longtime Slashdot reader hackingbear writes: South China Morning Post, citing Chinese state media, reported that an experimental reactor developed in the Gobi Desert by the Chinese Academy of Sciences' Shanghai Institute of Applied Physics has achieved thorium-to-uranium fuel conversion, paving the way for an almost endless supply of nuclear energy. It is the first time in the world that scientists have been able to acquire experimental data on thorium operations from inside a molten salt reactor according to a report by Science and Technology Daily. Thorium is much more abundant and accessible than uranium and has enormous energy potential. One mine tailings site in Inner Mongolia is estimated to hold enough of the element to power China entirely for more than 1,000 years. At the heart of the breakthrough is a process known as in-core thorium-to-uranium conversion that transforms naturally occurring thorium-232 into uranium-233 -- a fissile isotope capable of sustaining nuclear chain reactions within the reactor itself. Thorium (Th-232) is not itself fissile and so is not directly usable in a thermal neutron reactor. Thorium fuels therefore need a fissile material as a 'driver' so that a chain reaction (and thus supply of surplus neutrons) can be maintained. The only fissile driver options are U-233, U-235 or Pu-239. (None of these are easy to supply.) In the 1960s, the Oak Ridge National Laboratory (USA) designed and built a demonstration MSR using U-233, derived externally from thorium as the main fissile driver.

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Ruled him out just six months ago due to Musky connections

US president Donald Trump on Tuesday decided who he wants to lead NASA, despite having ruled out the same person six months ago.…

Can’t rule out more revenue wobbles given the complexity of big projects

Server-maker and designer Supermicro has promised to improve performance, after missing its guided revenue and revealing its margins aren’t strong.…

Net access cut on election eve, resumed after widely-loathed president was sworn in after disputed poll

The African nation of Tanzania has reconnected to the internet after a five day outage.…

An anonymous reader quotes a report from Ars Technica: Although Google DeepMind's Weather Lab only started releasing cyclone track forecasts in June, the company's AI forecasting service performed exceptionally well. By contrast, the Global Forecast System model, operated by the US National Weather Service and is based on traditional physics and runs on powerful supercomputers, performed abysmally. The official data comparing forecast model performance will not be published by the National Hurricane Center for a few months. However, Brian McNoldy, a senior researcher at the University of Miami, has already done some preliminary number crunching. The results are stunning: A little help in reading the graphic is in order. This chart sums up the track forecast accuracy for all 13 named storms in the Atlantic Basin this season, measuring the mean position error at various hours in the forecast, from 0 to 120 hours (five days). On this chart, the lower a line is, the better a model has performed. The dotted black line shows the average forecast error for official forecasts from the 2022 to 2024 seasons. What jumps out is that the United States' premier global model, the GFS (denoted here as AVNI), is by far the worst-performing model. Meanwhile, at the bottom of the chart, in maroon, is the Google DeepMind model (GDMI), performing the best at nearly all forecast hours. The difference in errors between the US GFS model and Google's DeepMind is remarkable. At five days, the Google forecast had an error of 165 nautical miles compared to 360 nautical miles for the GFS model, more than twice as bad. This is the kind of error that causes forecasters to completely disregard one model in favor of another. But there's more. Google's model was so good that it regularly beat the official forecast from the National Hurricane Center (OFCL), which is produced by human experts looking at a broad array of model data. The AI-based model also beat highly regarded "consensus models," including the TVCN and HCCA products. For more information on various models and their designations, see here.

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