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Cake day: June 20th, 2023

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  • RE: backups, I’d recommend altering your workflow. Instead of taking an image of a box, automate the creation of that box. Create a bash script that takes a base OS, and installs everything you use fresh. Then have it apply configuration files where appropriate, and lastly figure out which applications really need backup blobs to work properly (thunderbird, for example). Once you have that, your backups become just the data itself. Photos, documents, etc. Everything else is effectively ephemeral because it can be reproduced through automation.

    Takes a lot less space, is a lot more portable. And much better in scenarios where something in your OS is broken or you get a new computer and want to replicate your setup.





  • For people with “that one game” there is a middle ground. Mine is Destiny 2 and they use a version of easy anticheat that refuses to run on Linux. My solution was to buy a $150 used Dell on eBay, a $180 GPU to be able to output to my 4 high-res displays, and install Debian + moonlight on it. I moved my gaming PC downstairs and a combination of wake-on-lan + sunshine means that I can game at functionally native performance, streaming from the basement. In my setup, windows only exists to play games on.

    The added bonus here is now I can also stream games to my phone, or other ~thin clients~ in the house, saving me upgrade costs if I want to play something in the living room or upstairs. All you need is the bare minimum for native-framerate, native-res decoding, which you can find in just about anything made in the last 5-10 years.



  • “Open source” in ML is a really bad description for what it is. “Free binary with a bit of metadata” would be more accurate. The code used to create deepseek is not open source, nor is the training datasets. 99% of “open source” models are this way. The only interesting part of the open sourcing is the architecture used to run the models, as it lends a lot of insight into the training process, and allows for derivatives via post-training


  • It’s a little deeper than that, a lot of advertising works on engagement -based heuristics. Today, most people would call it “AI” but it’s fundamentally just a reinforcement learning network that trains itself constantly on user interactions. It’s difficult-to-impossible to determine why input X is associated with output Y, but we can measure in aggregate how subtle changes propagate across engagement metrics.

    It is absolutely truthful to say we don’t know how a modern reinforcement learning network got to the state it’s in today, because transactions on the network usually aren’t journaled, just periodically snapshot for A/B testing.

    To be clear, that’s not an excuse for undesirable heuristic behavior. Somebody somewhere made the choice to do this, and they should be liable for the output of their code.







  • Dran@lemmy.worldtoLinux@lemmy.mlBeing Forced to Say Goodbye
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    1 month ago

    Depends on where you work and what their policies are. My work does have many strict policies on following licenses, protecting sensitive data, etc

    My solution was to MIT license and open source everything I write. It follows all policies while still giving me the flexibility to fork/share the code with any other institutions that want to run something similar.

    It also had the added benefit of forcing me to properly manage secrets, gitignores, etc