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InitialsDiceBearhttps://github.com/dicebear/dicebearhttps://creativecommons.org/publicdomain/zero/1.0/„Initials” (https://github.com/dicebear/dicebear) by „DiceBear”, licensed under „CC0 1.0” (https://creativecommons.org/publicdomain/zero/1.0/)RX
Posts
42
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838
Joined
2 yr. ago

  • As someone who played a lot of U2:XMP back in the day, I can confirm this is the case. Honestly I'm not sure why Epic killed their master servers, since it seems like something that can run on a toaster in a supply closet, but good that it was relatively easy for the community to reverse engineer and adopt.

  • MKVToolNix is the right answer, BUT if you plan on sharing your Linux ISOs with the wider community you may not want to edit the original file.

    Not sure what you're watching on, but Plex lets you set a preferred audio language per-user, while Jellyfin and Kodi support external audio tracks as long as they are properly identified, so you could extract/find the English track you want and just toss it in the same folder

  • It would be amazing if it could have a significant impact on spatial and temporal accuracy of things like rain. I feel like for me the existing weather report is good enough for "it will probably rain tomorrow" but it's really hit-or-miss when you get to hourly resolution. A good model may be able to go so far as to say "it will probably rain between 3-4pm on the east side of town tomorrow, and 2-3pm on the west side"

    That's the dream at least. With enough data and a sophisticated enough model it feels like it could be possible.

  • There's a difference between the real-ish-time weather data continuously fed in to output predictions, and the decades of weather data used to build the model. The continuous feed of data is more than likely part of what Google alleges is saving significant energy.

    Its the training on decades of information, and occasional updates to those trained models that take a significant amount of resources, but hopefully for relatively short bursts.

  • I feel this personally, I live in the hills outside of a valley metro. All weather data is forecasted off of valley sensors, but shit gets weird when you suddenly climb 2000+ ft.

    The best weather services in my area are those that can factor in peoples household meters into their forecasting, but those services still aren't perfect.

  • It's not just about cutting costs, but also improving accuracy. Physical simulations factor in a dozen or so weather conditions to predict outcomes. Machine learning can track thousands of conditions, drawing connections not realized in physical models, leading to much more accurate statistical models.

  • What they leave off is how much goes into training the model, but I imagine once they settle on a trained model it can carry on pretty efficiently for a long time, especially if they're baking in things like atmospheric CO2 levels to help keep forecasts in line with global warming.

  • Sunglasses are often coated with special filters to block more UV light than other parts of the spectrum, e.g. 90+% of UV, but only 75% of visible. These glasses would block all light in (very roughly) even amounts. To achieve similar protection you would barely be able to see.

  • You're not going to like this, but I have been encoding my own B-tier collection in 1080p at roughly BluRay quality with HDR & Uncompressed audio. A-Tier movies stay 4K Remux, but the B-squad gets the downgrade (but I'm not willing to give up HDR / Atmos)

  • SHAME.

    Jump
  • It was the bloodiest battle that the world ever saw

    With civilians looking on in total awe

    The fight raged on for a century

    Many lives were claimed but eventually

    The champion stood, the rest saw their better

    Mr. Rogers in a blood-stained sweater