RHEL moved away from KDE and landed purely as a GNOME only enterprise shop. Possibly KDE might invest more on the Kubuntu desktop or something

I faced this problem numerous times. This is mostly a problem with parsing the text.

Most often the solution is to translate the text into how other people think through a problem and replicate their mental model of the world in your head. This sounds tough but when you read, you should pay attention at various levels: Phrases -> Sentences -> Paragraph. Construct the mental model as numerous Cause->Effect relations. Identify other types of relations that the author wants to convey.

Often also change the way you parse the text. What I described above is Bottom-Up approach where you construct the big picture from smaller pieces. The other way would be to start Top-Down, construct a big picture view by reading only the start and end of a specific paragraph/technique, understand the transformation that is happening to code/data/control flow, then move on to the next big chunk. Eventually you will get the general idea. Then you can drill down on specific paragraphs/ideas and focus on the details.

Regarding Python itself, The sentence "reinstall it and use the pure python version, it's the only way you'll learn it properly" is assuming that one would easily figure out one among the the numerous ways of installing a python environment. It's not an easy thing to do given that you as a beginner won't understand all the nuances of what each method achieves.

Easiest way to proceed is to pick one way to setup your python environment and be done with it.

Tl; Dr

I am on Linux, so my personal stack is:

1. pyenv -> a. curl -fsSL https://pyenv.run/ | bash b. ~/.pyenv/bin/pyenv init --install

2. Install a python version -> a. pyenv install 3.14

3. Create my workspace and set the python version to the one I installed: a. mkdir workspace b. cd workspace c. pyenv local 3.14

4. Create a venv so I have a local python virtual environment: a. python3 -m venv .venv b. source .venv/bin/activate

5. Start working on my project/code.

(Note for others: I don't recommend uv here as I don't use it myself and haven't tried to properly use it for an extended duration of time)

On Windows, use python install manager + Venv.

This is a good tutorial:

https://www.youtube.com/watch?v=6e3WLhPLiZQ

Edit: I forgot to mention that the python docs are an excellent starting point for learning python in general: https://docs.python.org/3/

First of all, the premise that this "open-source" project is innovative or there's something other than Archlinux + Dotfiles + Bundled software in the ISO is flawed. There's hardly anything inside the OS that other projects cannot achieve or haven't. If you want to call something innovative, Nix and NixOS fit the bill perfectly, CachyOS also fits the description. Omarchy doesn't. It's like saying Endeavour OS is doing something innovative.

When someone who is not intent on supporting bigots looks at EndeavourOS and Omarchy, They will choose EndeavourOS 100% of the time.

Success for you might be to get a million dollars and not question the origins of said money or the motives. Other people may have stricter standards for success than that.

This project has received money because it has drawn people to linux. I can still appreciate open-source code even if it’s written by an ass hat.

1. This project has received money because a bunch of rich CEOs are friends with DHH.
2. These people like what DHH holds as his ideology and promote similar rightwing xenophobic ideas.
3. The project itself isn't innovating in the space. The likes of NixOS come to mind when you think what's new in the field.
4. Omarchy is a bundle of software and AI. The image is 6 GB because it's Arch + dotfiles + software.

I feel like using ai is like using drugs, they’re bad, but regulating them is muuuuuuuch better than just prohibiting, people will use it anyways.

crime won't happen because it is illegal?

I misread the title as "Fedora Officials" and was wondering what new hell Redhat had gotten itself into.

At this point we need a bootcamp to setup our editors first before we have the bootcamp/course for the actual language/work we want to accomplish.

Well, congratulations on neovim graduating the university of complexity! Emacs was the chief guest.

Does the new user "userB" belong to the same set of groups as the old user "userA"?

More details here: https://serverfault.com/questions/947301/nginx-displaying-failed-13-permission-denied-when-trying-to-access-new-site

Basically:

1. Check user permissions on the public directory -> You said userA and userB both have access.
2. Check that the new userB belongs to a group with rx access to the public directory for the website.

That means they want people to actually look at the code generated by LLMs and understand how their stuff works so when it breaks they can take responsibility and quickly provide a fix. If your project breaks at 2 am Saturday and the dev that coded is not able to respond to questions on the PR because they don't understand what they wrote it's annoying to deal with.

Pretending that it's the same is disingenuous. What people are arguing for is some level of polish and care.

What's the font? The curly f and the stub m suggests 0xProto.. but that's just my guess.

Edit: and gosh I just wish I had some time between work and hobbies.. but I am happy with Endeavour KDE. Although I did try to get into Niri twice already.

But if you are even a little bit educated as to what the textbooks say about LLMs, they are exactly next-token predictors. There's no ifs-or-buts about it.

Your chess analogy is misinformed too. Your description of neural networks trained on past master games reads like the machine is given pictures of the board and simply learns from that. It also assumes that is how modern day chess engines are written which is not completely true. It is called the concept of "Book" which records past moves in certain positions as a cache. And people don't call that "Next Token Prediction". It's not as sophisticated.. it's simply a lookup table or a cache.

Actually good Chess Engines are search engines walking over a tree or graph of possible next moves. One can argue this is actually a prediction engine... but "Prediction Engine" as a description feels like the machine is rolling the dice far more than a search.. which is the part that's misleading.

Here is my claim: LLMs as they stand today are fundamentally less sophisticated than a Chess Engine since the search part is absent from LLMs. This is not because LLMs are less powerful, no. Their domain is simply different from search engines. Token prediction IS the job description of an LLM. Which is why LLMs are not very good at chess.