There is also writefreely. It is fairly basic, but says it supports “publish[ing] to multiple blogs from one account”. Haven’t really used it, but it looks kinda cool imo

https://github.com/writefreely/writefreely

I’m not an expert on btrfs, but I assume the inconsistencies come from deduplication, metadata, and maybe compression. I think some of them just count raw block storage, and some include the cost of metadata.

Traditional du assumes that each file takes up it’s full space on disk which isn’t always the case on btrfs. When using btrfs backed oci images, storage can easily appear multiple times higher.

I use btrfs filesystem usage /. I’m not sure that it is the “correct” way, but it works fairly well.

Slint has fairly decent docs and has worked fairly well for my small projects

I’ve gotten tired of weird regex stuff in awk, sed, and grep, so I’ve moved to perl -E for all but the most basic of things.

1. Codeberg is fully open source(forgejo) while gitlab has an open source core+community edition but a source available propietary enterprize edition.

2. Codeberg is a nonprofit with no ulterior motives. Gitlab is a publicly traded for profit entity with a goal to make profit

3. This could just be me, but codeberg feels a lot more transparent. When they have outages, they explain why.

4. Super minor, but the codeberg team “self-hosts” their own servers so you only need to trust the one entity rather than additionally trusting the server provider.

Are you sure your screen refresh rate is correct?

Zellij - a better way for a cli application to communicate with the terminal

Warp - a terminal emulater that integrates LLM completion natively

Fish - a shell that generates completions automatically from a man-page

They could be refering to the V programming language

I may be missing something, but the only machine learning focused api I know of are AMD’s ROCM, Nvidia’s CUDA, and now Intel’s oneAPI. I haven’t looked into Apple’s machine learning frameworks and I consider vulkan more of a general purpose api than a machine learning one.

Now there are 3 competing standards Edit: 6ish accually

Turing Complete Configuration

• more extensible
• tend to be heavier
• harder to provide detailed error messages
• more difficult for new users

Data Based Configuration

• easier to use
• easier to provide documentation
• lighter to embed
• more limited usecases

To enable the use of flakes, you have to use the ‘extra-expiremental-features flakes’ flag.

Edit: Apparently they are called ‘extra-expiremental-features’ not ‘extra-unstable-features’. Regardless the nix docs explicitly describe them as unstable here

https://nixos.org/manual/nix/unstable/contributing/experimental-features.html

Nixos

Pros

• Delarative Config
• largest package repos

Cons

• poor documentation
• cli and package management is in limbo with unstable flakes/cli

In most cases you don’t want one. It can make forks confusing and lend malicious actors more credibility than they deserve.

Copyright controls the code. Trademarks are the recognisable names/icons that identify a project.

I like curl’s standard and trasparent release cycle. The consistent feature freeze before releases seems like a good idea to prevent bugs.

I’d consider dual-booting(windows+linux or linux+linux) or just installing a desktop environment. You can login to your wm and they can login to a full de

Congratulations and welcome. I use arch, btw

I use a shared boot partition all the time. I mount my EFI system partion on /efi. Then I bind mount /efi/$OSNAME to /boot in my fstab. Then I just manage my bootloader (typically systemd-boot or refind) manually. Any distros I install are installed in my encrypted btrfs partition within their respected subvolumes