I couldn’t agree more with this, projects like artix are undermined by all the hard dependencies on systemd and Bash.
Void attracted me because of the support for posix, runit and musl (plus good zfs support). It’s unfortunate that Arch doesn’t have that greater portability.
This sounds a lot like Luet which is used in Moccacino (formerly Sabayon).
The manual is OK, much of it’s out dated and often outright wrong. It is still a great document.
Edits to the wiki are often knocked back if they weren’t made by the inner circle, discussions on the back page are often closed and frankly the TUs are mostly wankers. The forum policy on necro-bumping leaves half answers everywhere but the notion of “put it in the wiki” is undermined by the toxic community among inner party members.
Arch is a great middle ground between Fedora and Gentoo, but I had to walk away because the community was so toxic and childish.
I’m using void and Gentoo now and I’m pretty happy, anything that doesn’t run works in a container anyway.
TL;DR: community behaviour is much more important to me than technical use.
I agree with revealJS, I recommend trying it with org-mode in eMacs with the plugin (plus you also get banner for free)
Alternatively, it also works with Jupyter.
This is what I use for every presentation I need to give.
You want H2OGPT or just use Langchain with CLI
Just use flatpak and podman, in a punch you can proot into a different system / zfs data set / btrfs sublime
I just use and old laptop
Re your update.
My framework has been great, I’ve had no issues with it and I’m quite happy. Make sure to go with the matte screen though.
In saying that, I think I was happier with my thinkpad, but I have no good scientific reason for that, I suspect the nipple and keyboard are a big part of it.
Framework and ThinkPad have both been a really positive experience.
Also, if it’s just the DE, install sway / i3 and try that for a week. If you liked that it’s on literally every Linux distribution, even the BSDs.
Go with EndeavourOS. It won’t “just work”, but it will be the best compromise between confusing abstraction and low level frustrations.
Fedora is good but it abstracts a little too much away, this is great when you understand how software works, but it’s very confusing when you’re new to Linux and programming.
Arch is good, but you won’t be able to hid the ground running, you’d have to sacrifice a weekend to learn.
Go:
Tinkering with those in that order, after about 6 months, you’ll start to feel at home.
If and only if the trained model is accessible without licence.
E.g. I don’t want Amazon rolling out a Ilm for $100 a month based on freely accessible tutorials written by small developers.
But yeah duck copyright
Worth mentioning that one can use bubblewrap directly over chroot to get similar behaviour as well.
It’s often simpler to use distrobox but being able to rsync chroot a between devices can be very convenient.
I wish there was more variety.
You basically have BSD and Linux and in the Linux space {glibc/musl systemd/openrc/runit PKGBUILD,ebuild,deb,rpm} which seems like a lot but it’s the really niche stuff that’s fun to pull apart and play with.
Well to clarify the two big differences here are that the exe is pre compiled and maybe dynamic libraries.
Heavy tech stacks do suck though
These comments often indicate a lack of understanding about ai.
Ml algorithms have been in use for nearly 50 years. They certainly become much more common since about 2012, particularly with the development of CUDA, It’s not just some new trend or buzz word.
Rather, what we starting to see are the fruits of our labour. There are so many really hard problems that just cannot be solved with deductive reasoning.
The mistral-7b is a good compromise of speed and intelligence. Grab it in a GPTQ 4bit.
If you can find a copy yeah. GNU sed isn’t written for windows but I’m sure you can find another version of sed that targets windows.
Oh no you need a 3060 at least :(
Requires cuda. They’re essentially large mathematical equations that solve the probability of the next word.
The equations are derived by trying different combinations of values until one works well. (This is the learning in machine learning). The trick is changing the numbers in a way that gets better each time (see e.g. gradient descent)
Bind tun0 in the settings but what I do is run BitTorrent in a docker container with WireGuard so the vpn doesn’t effect my day to day browsing