honestly, where NixOS shines for me is in my homelab. i don’t always have time to fully document what i’m doing, but my NixOS config is code-as-documentation for when work burns all of my memories away and has a git log and conflict management so i can manage multiple systems that share common config.

and once you find out you can have services run on systemd with syntax like services.jellyfin.enabled = true you’ll never want to go back to containers, although it has ways to manage those as well.

it’s overall a great OS for tinkering and deploying small services across small networks. not sure how it scales, but for my use case it’s damn near perfect

modifier for window manager nav and general OS controls like wofi/rofi

man this brings back memories.

i was able to install Arch on my 2012 Macbook Pro, but the networking was a huge issue. not only did the driver cause terrible screen tearing for some inexplicable reason, but i had the same problem even getting the dang thing installed. luckily i’m an Android developer and was able to share wifi over USB with an Android device.

yeah i don’t know what the use case is for hiding or partially hiding windows as if they’re papers on a desk other than sheer skeuomorphism.

yeah i see that too. it seems like mostly a reactionary viewpoint. the reaction is understandable to a point since a lot of the “AI” features are half baked and forced on the user. to that point i don’t think GNOME etc should be scrambling to add copies of these features.

what i would love to see is more engagement around additional pieces of software that are supplemental. for example, i would love if i could install a daemon that indexes my notes and allows me to do semantic search. or something similar with my images.

the problems with AI features aren’t within the tech itself but in the surrounding politics. it’s become commonplace for “responsible” AI companies like OpenAI to not even produce papers around their tech (product announcement blogs that are vaguely scientific don’t count), much less source code, weights, and details on training data. and even when Meta releases their weights, they don’t specify their datasets. the rat race to see who can make a decent product with this amazing tech has made the whole industry a bunch of pearl clutching FOMO based tweakers. that sparks a comparison to blockchain, which is fair from the perspective of someone who hasn’t studied the tech or simply hasn’t seen a product that is relevant to them. but even those people will look at something fantastical like ChatGPT as if it’s pedestrian or unimpressive because when i asked it to write an implementation of the HTTP spec in the style of Fetty Wap it didn’t run perfectly the first time.