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Joined 10 months ago
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Cake day: December 16th, 2023

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  • Active GuixSD user.

    Our application catalog is much smaller than many other distros simply because we don’t have the userbase large enough to surface the volunteers necessary to support it. So you will have to learn to write your own packages eventually

    That said, if you know your way around functional languages (in this case, scheme), it’s probably the easiest time I’ve ever had writing a package. Everything that goes into the script is known at the time the script is written, so weird extrinsic problems don’t really occur after you’ve written the package.

    Some stuff that you and the guix maintainers may not have the time to support will also get updated more slowly.

    Luckily flatpak exists, and is a godsend for the new wave of read-only (functional/ostree-based) OSs.

    Biggest appeal for me was having all my configuration in one place (and documented) so if I forget I did something in 6 months, it’s always staring at me in my home or system config file. You can accomplish the same thing by being diligent with say, script files, but it’s drop-dead easy to just maintain a system and home descriptor file and keep editing that.



  • Most data centers evaporative cooling from what I understand, and according to This

    Cooling towers use water evaporation to reject heat from the data center causing losses approximately equal to the latent heat of vaporization for water, along with some additional losses for drift and blowdown. In larger data centers this on site water consumption can be significant, with data centers that have 15 MW of IT capacity consuming between 80-130 million gallons annually. n this study, on-site water consumption is estimated at 1.8 liters (0.46 gallons) per kWh of total data center site energy use for all data centers except for closet and room data centers, which are assumed to use direct expansion (air-cooled chillers).

    And seeing as hyperscale data centers usually use between 20-50 megawatts per data center, and there’s three of them in Colon, that’s like at least 240 million gallons of water a year.

    Yikes.






  • Depends on the training and the output.

    Just like if you photographed the Mona Lisa in such a way as it recreated the piece as if it wasn’t a photograph, a model sufficiently trained that can reproduce the original training data, you have copyright issues.

    Problem is that many models can do this, but it’s a mathematically improbable occurrence.

    If I make a stamp that’s made of 1 billion exact copies of different copyrighted photos and cut it infinitesimally small, and mixed it up, the problem that it can produce the original work that it was made from still becomes a copyright issue.

    You’d have to prove the opposite, in fact. That it’s mathematically impossible for your model to reproduce the copyrighted content for it not to be an issue