[–] 4 points 2 weeks ago

You don't need to ignore it. She should understand your stance on AI. You don't need to come off strong with pure hatred, but she should understand your dislike of AI. On the topic of her homework, keep it light. You don't need to fixate on it, just say it looks good (if it does). I don't know if you can help her reduce her usage. Maybe someone else here can figure that out.

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  • [–] 3 points 2 weeks ago

    As a Debian user and programmer, I also suggest Fedora. As said above, Fedora has everything that Ubuntu does, does not force snap packs on its users, and finally, it offers updated software libraries compared to more stable distros such as Debian.

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  • [–] 1 point 2 weeks ago

    Hello! First of all, I wrote this myself without AI (but with a stackover post here for the word counter (wc) part and here for the -exec of find part).

    How it works

    We select a directory called A. If A has a sub-directory at somepoint with the matching string "red" in it, we then ignore it. Otherwise, it is the deepest directory with "red" in its name. That makes sense. So, how do we do this?

    First, I wrote a find.sh script

    if [[ $(find "$1" -mindepth 1 -type d -name '*red*' | wc -c) -eq 0 ]]; then
      echo "$1"
    fi
    

    It will output the name of the directory if it is the deepest match, or nothing if not. Then I do

    find . -type d -name '*red*' -exec bash find.sh {} \;

    Finally

    find . -type d -name '*red*' -exec $SHELL -c 'if [[ $(find "$1" -mindepth 1 -type d -name "*red*" | wc -c) -eq 0 ]]; then echo $1; fi' sh {} \;

    Hope this helps 👍

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  • [–] 3 points 3 weeks ago

    I believe so. They do not mention a change in model architecture which reduce the compute for these byte-level models. So it would be expected that compute costs of token generation increase proportionally with the average increase of tokens with respect to current subword tokenization. I have just read the paper here and they seem to combat the increase compute cost with their efficent self-attention method, I do not see a mention of this here, so I only assume that the internal model architecture remains the same, and thus it is expected that the larger context length and time to generate a token will be the main issues.

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  • [–] 5 points 3 months ago

    It was better when they at least made money with the personal data instead of giving it out for free lol. On a real note, hope tiktok dies. Terrible platform and im so happy I've never had to use it.

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  • [–] 11 points 3 months ago (1 child)

    Glad that we can take their work for free to. Hopefully the community can produce open weight models that run locally so we don't need to pay these fools.

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