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Joined 4 months ago
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Cake day: March 3rd, 2024

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  • 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.


  • used to be the Android team used Ubuntu, not sure if that’s still the case. Linux is pretty much the native environment for Android dev. i’d recommend at least 4GB of dedicated RAM if not 8. definitely at least 8 if you plan to use the emulator (which is itself a VM).

    Android Studio will get you 90% of the way there. it will help you install the SDK, emulators, etc, and provide UI front ends for the CLI tools, ie adb.

    there’s really not much to system level dependencies. if your distribution supports JDK 17 (probable) you’ll be fine with whatever.

    obligatory: i use Arch, btw



  • chrash0@lemmy.worldtoLinux@lemmy.mlLix - a new fork of Nix
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    2 months ago

    i really want to like Nix.

    gave it a shot a few years ago, but i felt like documentation and community support wasn’t really there yet. this was long before Nix surpassed Arch in terms of number of available packages. now people still complain about documentation, especially of the Nix language. i see a lot of package authors using it, and that kind of tempts me to start using at least the package manager. but a lot of packages don’t. the allure of GitOpsing my entire OS is very tempting, but then there’s been these rumors (now confirmed) of new forks, while Guix splintered off much earlier. for something that’s ostensibly supposed to be the most stable OS, that makes me nervous. it also seems to have some nontrivial overhead—building packages, retaining old packages, etc.

    the pitch for Nix is really appealing, but with so much uncertainty it’s hard to pull the trigger on migrating anything. heck, if i could pull off some PoCs, i think my enterprise job might consider adopting it, but it’s a hard recommend for me today as it was 5 years ago.



  • it’s not the only problem listed here, and they’re pretty explicit that pickle files are known to be insecure. however, Huggingface isn’t being negligent by allowing them. somewhat ironically, it’s tough to get ML engineers/researchers to try anything they didn’t learn first. Huggingface themselves makes safetensors which is a more secure open weights format, but there are also competing standards in this space and many stubborn and apathetic devs will stick with pickle cuz it’s easy. it’s a tough problem for HF, but i understand why they do it this way.

    in a previous job i asked that we not use pickle files either in trying new models or internally distributing models, and they didn’t see the point. this a wider cultural problem, and HF is just trying to capitalize on that market of dumb dumb ML researchers



  • i’ve been daily driving nushell for about 6 months and it’s been great for the most part. the downsides are 90% regular breaking changes (big breaking changes just dropped today that i’ll have to migrate) and 10% translating scripts or commands from bash.

    it can really make you feel like a wizard the first time you bang out a pipeline to change some data in a JSON file.

    the only thing i might mildly disagree with is the sentiment that we need community buy-in. sure it would be nice if the project had more eyes on it, but i’m not trying to convince my company to adopt nushell. unlike TypeScript or Rust i don’t have to inconvenience anyone by introducing nushell to my workflow. you can just start using it. and i’d recommend it to basically anyone who isn’t brand new to shells. but it doesn’t hurt my feelings one bit if my coworkers don’t see the appeal