excellent! i can now remove my eternal TODO to write this myself :D

well if it makes you feel better you just made me man history xD

i just cat grep .bash_history lol

but this does sound more convenient xD

but wait, it gets even more cyberpunk: the security cam footage is on streamable! :o https://streamable.com/2e8p4v

welp, looks like you don’t use python virtualenvs… well i guess jokes on you all your shit is probably broken now (and as a bonus, that’s probably a big part of the donwload size as well) :p

nice… so we need a github marketing campaign to fund the software that makes the world run? nice…

a bachelor of latte arts? xD

electron has entered the room

tbf it probably is still significantly less that windows, i was being a bit facetious…but it’s still at like 1-1,5gb idling on a fresh boot (this is the whole DE, not just cinnamon)…

i did a fresh install with mate on an old machine though and it was a lot less (the usual 500mb or something) can’t see anything suspicious running though - and yes I did check without any stuff running in the background like steam which is also stupidly intensive with their webkit nonsense

does it still use the same amount of ram as windows? :/

the level of “cutest boy in town” i believe

i was going to mention some books too but then i saw the pink floyd answer and realised that would be far more likely to not end up sitting on a shelf :(

wololooooo

huh? it’s got a dock… just open your browser of choice and start streaming?

but thank fuck specifically he has cos now it’s a brilliant piece of software xD

also iirc gitlab does offer something like this as a feature now with “merge trains” (though i’ve never really used it, usualy just go for the feature branch out of habit x) )

you forgot: “who don’t pay taxes in the country they live in” :picard:

yeh that’ll probably be it tbf… the cuda drivers are specifically for scientific computing and are pretty rubbish for anything else unfortunately… even amd ones are like that :(

however a way i found around it is to just push my gpu compute envs to docker and voila (also avoids the pain of installing the drivers cos nvidia actually provides a cuda docker image) :D