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Joined 9 months ago
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Cake day: March 3rd, 2024

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  • you have to do a lot of squinting to accept this take.

    so his wins were copying competitors, and even those products didn’t see success until they were completely revolutionized (Bing in 2024 is a Ballmer success? .NET becoming widespread is his doing?). one thing Nadela did was embrace the competitive landscape and open source with key acquisitions like GitHub and open sourcing .NET, and i honestly don’t have the time to fully rebuff this hot take. but i don’t think the Ballmer haters are totally off base here. even if some of the products started under Ballmer are now successful, it feels disingenuous to attribute their success to him. it’s like an alcoholic dad taking credit for his kid becoming an actor. Microsoft is successful despite him


  • these days Hyprland but previously i3.

    i basically live in the terminal unless i’m playing games or in the browser. these days i use most apps full screen and switch between desktops, and i launch apps using wofi/rofi. this has all become very specialized over the past decade, and it almost has a “security by obscurity” effect where it’s not obvious how to do anything on my machines unless you have my muscle memory.

    not that i necessarily recommend this approach generally, but i find value in mostly using a keyboard to control my machines and minimizing visual clutter. i don’t even have desktop icons or a wallpaper.



  • All programs were developed in Python language (3.7.6). In addition, freely available Python libraries of NumPy (1.18.1) and Pandas (1.0.1) were used to manipulate data, cv2 (4.4.0) and matplotlib (3.1.3) were used to visualize, and scikit-learn (0.24.2) was used to implement RF. SqueezeNet and Grad-CAM were realized using the neural network library PyTorch (1.7.0). The DL network was trained and tested using a DL server mounted with an NVIDIA GeForce RTX 3090 GPU, 24 Intel Xeon CPUs, and 24 GB main memory

    it’s interesting that they’re using pretty modest hardware (i assume they mean 24 cores not CPUs) and fairly outdated dependencies. also having their dependencies listed out like this is pretty adorable. it has academic-out-of-touch-not-a-software-dev vibes. makes you wonder how much further a project like this could go with decent technical support. like, all these talented engineers are using 10k times the power to work on generalist models like GPT that struggle at these kinds of tasks, while promising that it would work someday and trivializing them as “downstream tasks”. i think there’s definitely still room in machine learning for expert models; sucks they struggle for proper support.




  • i feel like if you’re not sat stationary at a workstation (who is these days) what you want is a laptop that’s good at being a laptop. 99% of the software developers i work with (not a small number) use Macbook Pros. they are well built, have good components, have best in class battery life (we’ll see how things shake out with Qualcomm), and are BSD based and therefore Unix compatible. my servers and gaming/CUDA PC? Linux all day. my laptop? Macbook. i’m not ideological enough to have range anxiety every time i step away from my desk. plus any decent sized org is going to have to administrate these machines, from scientists to administrators, and catering to .4% of your users is not a good ROI if your software vendors struggled for 8 years to get their Windows 98 based specialty sensor software to run on Mac.

    that .4% is likely not 0 because they are nerds.

    seriously tho if Qualcomm chips can make a Linux book that lasts all day i would happily make the switch


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    3 months ago

    i’m not really here to advocate for Rust in the kernel. i will say that i work on Rust professionally at a Fortune 100 company that is in the process of adopting it, which may skew my perception of it as mainstream, just to get the bias out of the way.

    it is part of the project though, no? drivers still need to be interfaced with. so the people working on driver interfaces should be comfortable with it, again at least to preserve basic builds and do basic code review. this is specifically in reference to the issue that this thread is ostensibly started from: a kernel dev was getting worked up about “having to learn Rust”. so no, i don’t think it’s a strawman to point out the real people denying or frustrating patches just because they don’t understand the language. overly harsh maybe but not a total mischaracterization.


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    3 months ago

    i can definitely see it as a “hostile takeover” of sorts, but this is something the project has decided on, for better or worse. i can understand not wanting to learn a new language that you may not like or agree with, but that means you will have to divest yourself from a project that adopts that language to a certain extent. Rust is—again for better or worse—something Linus thinks is good for the project, and thus learning Rust at least enough to not break the builds is a requirement for the project. i can’t imagine working on a software team where a chunk of people refuse to take part in a major portion of it simply because they’re not immediately familiar with it. that does sound like old crotchety behavior. on the other hand it’s tragic that so many people with all this experience are being forced into a design decision that arguably may have been made hastily and that they had little say in.

    that makes this definitely an old guard vs new issue. and maybe it is an olive branch for the old guard to say “let’s just take our time with this.” but we have crossed a threshold where seeing a new project in C is the oddity while new projects in Rust are commonplace. Rust is mainstream now, and “i don’t want to learn this” is a dogshit technical justification.






  • language is intrinsically tied to culture, history, and group identity, so any concept that is expressed through a certain linguistic system is inseparable from its cultural roots

    i feel like this is a big part of it. it reminds me of the Sapir Whorf Hypothesis. search results and neural networks are susceptible to bias just like a human is; “garbage in garbage out” as they say.

    the quote directly after mentions that newer or more precise searches produce more coherent results across languages. that reminds me of the time i got curious and looked up Marxism on Conservapedia. as you might expect, the high level descriptions of Marxism are highly critical and include a lot of bias, but interestingly once you dig down to concepts like historical materialism etc it gets harder to spin, since popular media narratives largely ignore those details and any “spin” would likely be blatant falsehood.

    the author of the article seems to really want there to be a malicious conspiratorial effort to suppress information, and, while that may be true in some cases, it just doesn’t seem feasible at scale. this is good to call out, but i don’t think these people who concern their lives with the research and advancement of language concepts are sleeping on the fact that bias exists.


  • it’s super weird that people think LLMs are so fundamentally different from neural networks, the underlying technology. neural network architectures are constantly improving, and LLMs are just a product of a ton of research and an emergence after the discovery of the transformer architecture. what LLMs have shown us is that we’re definitely on the right track using neural networks to solve a wide range of problems classified as “AI”