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Joined 2 years ago
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Cake day: June 15th, 2023

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  • There are two potential show-stoppers.

    1. Field-specific apps that only run on windows. If you really need Adobe Creative Cloud or SolidWorks or something like that you might be out of luck. This is mostly true for apps that require GPU acceleration, which is difficult to rig up in a VM. You wouldn’t want to do that if it was a big part of your workload.

    2. Mandatory spyware and rootkit DRM to prevent cheating with remote tests. Hopefully if they do such a thing they provide loaner hardware too. I’ve seen a lot of bullshit in my time but my experience is outdated, so I don’t know what’s common nowadays.



  • It ranges from “automatic” to “infuriating”.

    If you have Secure Boot enabled, there are some hoops to jump through. Read the docs and follow the steps for DKMS.

    Depending on your distro and your requirements, you might want to install the drivers manually from Nvidia rather than using older drivers from your distro.

    If you need CUDA, god help you. Choose a distro that makes this easy and use containers to avoid dependency hell. Note that this is not any easier on Windows (at least not last I checked, which was a few years ago).





  • I’ve been using cryptpad.fr (the “flagship instance” of CryptPad) for years. It’s…fine. Really, it’s fine. I’m not thrilled with the experience, but it is functional and I’m not aware of any viable alternatives that are end-to-end encrypted.

    It’s based on OnlyOffice, which is basically a heavyweight web-first Microsoft Office clone. Set your expectations accordingly.

    No mobile apps, and the web UI is not optimized for mobile. I mean, it works, but does using the desktop MS Office UI on a smartphone sound like fun to you?

    Performance is tolerable but if you’re used to Google Sheets, it’s a big downgrade. Some of this is just the necessary overhead involved in an end-to-end encrypted cloud service. Some of it is because, again, this is a heavyweight desktop UI running in a web browser. It’s functional, but it’s not fast and it’s not pretty.


  • DNS over HTTPS. It allows encrypted DNS lookup with a URL, which allows for url-based customizations not possible with traditional DNS lookups (e.g. the server could have /ads or /trackers endpoints so you can choose what to block).

    DNS Over TLS (DoT) is similar, but it doesn’t use URLs, just IP addresses like generic DNS. Both are encrypted.



  • Honestly, that sounds great.

    My biggest problem with Flatpak is that Flathub has all sorts of weird crap, and depending on your UI it’s not always easy to tell what’s official and what’s just from some rando. I don’t want a repo full of “unverified” packages to be a first-class citizen in my distro.

    Distros can and should curate packages. That’s half the point of a distro.

    And yes, the idea of packaging dependencies in their own isolated container per-app comes with real downsides: I can’t simply patch a library once at the system level.

    I’m running a Fedora derivative and I wasn’t even aware of this option. I’m going to look into it now because it sounds better than Flathub.




  • In my experience, this is more a problem if you are fully running your own mail servers, not so much if you are using an established email service. My MX record reflects my email provider, and my outgoing mail goes through their servers. So I’m as trusted as they are, in general. Your mail provider should have instructions on how to set up DNS for verification.


  • If you’re willing to pay money for it, you can get your own domain for $2-$15 per year, then use it with pretty much any commercial email service. That way you can change email providers without changing your address.

    This is my plan going forward. I’m going to suffer the inconvenience of changing my address, but only one more time, not every time I want to change providers.



  • But any 50 watt chip will get absolutely destroyed by a 500 watt gpu

    If you are memory-bound (and since OP’s talking about 192GB, it’s pretty safe to assume they are), then it’s hard to make a direct comparison here.

    You’d need 8 high-end consumer GPUs to get 192GB. Not only is that insanely expensive to buy and run, but you won’t even be able to support it on a standard residential electrical circuit, or any consumer-level motherboard. Even 4 GPUs (which would be great for 70B models) would cost more than a Mac.

    The speed advantage you get from discrete GPUs rapidly disappears as your memory requirements exceed VRAM capacity. Partial offloading to GPU is better than nothing, but if we’re talking about standard PC hardware, it’s not going to be as fast as Apple Silicon for anything that requires a lot of memory.

    This might change in the near future as AMD and Intel catch up to Apple Silicon in terms of memory bandwidth and integrated NPU performance. Then you can sidestep the Apple tax, and perhaps you will be able to pair a discrete GPU and get a meaningful performance boost even with larger models.




  • vd (VisiData) is a wonderful TUI spreadsheet program. It can read lots of formats, like csv, sqlite, and even nested formats like json. It supports Python expressions and replayable commands.

    I find it most useful for large CSV files from various sources. Logs and reports from a lot of the tools I use can easily be tens of thousands of rows, and it can take many minutes just to open them in GUI apps like Excel or LibreOffice.

    I frequently need to re-export fresh data, so I find myself needing to re-process and re-arrange it every time, which visidata makes easy (well, easier) with its replayable command files. So e.g. I can write a script to open a raw csv, add a formula column, resize all columns to fit their content, set the column types as appropriate, and sort it the way I need it. So I can do direct from exporting the data to reading it with no preprocessing in between.


  • My experience might be a bit outdated, but I remember finding the default Mac OS X Terminal extremely slow. A few years back I ran an output-heavy command, and the speed difference between displaying the output in terminal vs outputting it to a file was orders of magnitude. The same thing on my Linux system was much, much faster. I’m not sure how much of that was due specifically to rendering, vs memory management or something else, though.

    I might see if I can still reproduce this in Sequoia and if Ghostty is faster on Mac.