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Joined 3 years ago
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Cake day: June 6th, 2023

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  • What I’m noticing more, is that you can keep a consistent 11.4MB/s, this feels relatively close to what you’d usually pull through a 100mbit/s link (after accounting for overhead). If that’s the case, it shouldn’t matter how the NFS client decides to chunk the data, for how much throughput there is to the NAS. Which means you’re looking at a broken NFS server that can’t handle large single transmissions.

    If it’s not the case, and you’ve got a faster network link, it seems that the NAS just can’t keep up when given >2gb at once. That could be a hardware resource limitation, where this fix is probably the best you can do without upgrading hardware. If it’s not a resource limitation, then the NFS server is misbehaving when sent large chunks of data.

    Basically, if your network itself (like switches, cables) isn’t broken, you’re either dealing with a NAS that is severely underspecced for what it’s supposed to do, or a broken NFS server.

    Another possibility for network issues, is that your proxmox thinks it has gigabit (or higher), but some device or cable in between your server and NAS limits speed to 100mbit/s. I think it’d be likely to cause the specific issues you’re seeing, and something like mixed cable speeds would explain why the issue is so uncommon/hard to find. The smaller buffers more frequent acknowledgements would sidestep this.

    Do note I am also not an expert in NFS, I’m mostly going off experience with the “fuck around and find out” method.



  • Iirc, the XZ backdoor was specifically targeting RH and Debian, which for some reason link libsystemd into OpenSSH. Afaik even upstream Arch was unaffected, not just Artix. The exploit code, though non-functional, still made its way onto your system (assuming you updated when it was in a release version).

    I’m not defending systemd though, it’s clear that Poettering’s goals do not align with the rest of the Linux community. I’m saying that Artix not being affected by the XZ backdoor is not a good argument for why to use Artix or avoid systemd.

    It’s like saying “Linux doesn’t get malware” because most desktop malware targets the OS with the largest desktop userbase, Windows. This alone doesn’t suddenly make Linux “better”. That doesn’t mean there aren’t other reasons to avoid Windows.




  • If I had to pick one, Arch. I already use it a lot, so it’s familiar. I know my way around the package manager and how to create packages, so even when things aren’t available for Arch out of the box, I can make it work.

    It’d be kind of a hassle trying to keep anywhere close to 100% server uptime, but for my own personal stuff that shouldn’t be that big of an issue, as I can fix it when I have the time.

    For desktop, I basically can’t do stable release. I frequently mess with new projects requiring the latest versions of everything, which is a near impossible task on stable-release distros.



  • Matrix (Synapse with Element) can be self-hosted for free, though they have optional paid plans for enterprises. The main goal of Matrix is federation (connecting with other servers), though this can be turned off completely. This is probably the most “business” look/feel you can get fully FOSS, if that’s what you’re looking for.

    XMPP has more clients/servers, and is more for the technically oriented end user. I can’t really give recommendations here, as I haven’t extensively used XMPP.

    Spacebar (formerly Fosscord) is a Discord clone (API compatibility as a goal) that can be selfhosted.


  • It’s an old blog post, but this doesn’t look very good for System76. At the same time, GNOME (and GTK) is refusing to implement basic features. Stuff like server side window decorations, because they can’t “tolerate” SSD. The hard enforcing of Adwaita theming might make sense in GNOME, but on devices not 100% in the GNOME ecosystem, libadwaita apps have awful UX. I do not want shit like Zenity to take up 50% of my screen space for 3 words and 2 buttons, yet libadwaita enforces it.


  • Unless Arch’s lts kernel switches to a newer lts (in a year or two?), you can run nvidia 580 dkms modules and the lts kernel with basically no maintenance.

    After that, you can consider something like linux-lts66 from AUR, or switch to another distro if desired. The first option requires compiling the kernel (no maintenance, just processor time), and will keep your system security patched until the last LTS kernel supported by nvidia 580 modules stops being supported.

    Whatever kernel you choose, ensure you have the -headers, like linux-lts-headers. That way, the nvidia-580xx-dkms package can install properly.

    If you haven’t yet, look into an AUR helper like yay or paru. These significantly improve quality of life when using AUR packages.






  • The documentation you were looking at might’ve been the Matrix specification.

    There is documentation on how to host a Matrix server, I’d honestly recommend using containers (maybe docker compose) for this one. It can definitely be confusing setting up a service like a Matrix homeserver for the first time.

    As for other people finding it, you can (and should) make your homeserver invite-only. It’s also possible to disable federation, which makes the server self-contained. It will not accept incoming connections from other servers, nor make outgoing connections to other servers.

    This does mean everyone you want to talk with has to be on your homeserver. There are probably better options available if you want to avoid Matrix’ federation issues, like Spacebar.


  • Web push for notifications. Sure, there’s privacy implications, but it’s already near universal. There’s other options like ntfy.sh if you’re not limited to existing infrastructure. UnifiedPush also works well as a protocol for push notifications.

    Everything else can be handled in-app. Password reset will have to be done by an admin, though it’s completely doable for a small selfhosted service.

    Some of the downsides OP listed may or may not always apply, but there are always downsides. Either you have to set up your own email server (with extra maintenance burden), or your “selfhosted” app suddenly relies on third party infrastructure, like your email provider (or those of other users on your instance).


  • I’m going to assume you’re unable to see the embedded image. I didn’t add alt text, that’s my mistake.

    Below “Besides”, there is a screenshot of a tweet by user @haydendevs stating “this is who you’re arguing with online” and an attached image of a series of dots connected by lines. This is the (overused) visual representation of a “neural network” in machine learning. The meaning of the image in this context is to state you are arguing with bots or AI online. I used this twitter screenshot as an attempt to make a joke of the fact the OP reads like AI-generated text.

    I will edit the alt text in my comment above.


  • MPV is great, I use it all the time. It’s fully replaced VLC on my desktop.

    It is not an “alternative to Jellyfin”. It does not offer many “comfort features” like (synced ootb) watch tracking. It does not transcode at all, and it doesn’t even run on devices that need transcoding most, like smart TVs.

    These two applications fall into two different categories, and they will never replace each other. One is a media player, you throw mpv any video file, it puts it up on screen, great. The other is a media server, it allows you to sign in, browse your nicely organized library, and click play on the movie of your choice, very cool.

    Even the idea of opening SMB or NFS to the entire internet just so your most technical of friends can manually download and watch a movie is insane compared to setting up Jellyfin. Reminder, not everyone has the connection to stream a full 4k bluray rip, transcoding allows those users to watch at all.

    Besides,

    Screenshot of a tweet by user @haydendevs stating “this is who you’re arguing with online”, and an attached image of a series of dots connected by lines. This is the often used visual representation of a “neural network” in machine learning.