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.

this is one of those facts i have to struggle to keep to myself to avoid coming off as an insufferable nerd

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it’s interesting that they’re using pretty modest hardware (i assume they mean 24 cores not CPUs) and fairly outdated dependencies.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.

the semantics of C make that virtually impossible. the compiler would have to make some semantics of the language invalid, invalidating patterns that are more than likely highly utilized in existing code, thus we have Rust, which built its semantics around those safety concepts from the beginning. there’s just no way for the compiler to know the lifetime of some variables without some semantic indication

i was mostly making a joke about how this absolutely is not a common problem on any platform, not to this degree. and at least when my Arch and Nix systems go down i don’t have anyone to blame but myself. sure, systems have update issues, but a kernel level meltdown that requires a safe mode rescue? that’s literally never happened to me unless it was my fault

damn i haven’t used Windows in over a decade. are y’all ok?

i’ve used Chezmoi for years now pretty successfully. works on my Mac and Linux machines. it probably could be made to work on Windows. i am transitioning to NixOS, but i’ll probably keep using it anyway, since i still have Macs for work (and because they’re great laptops don’t @ me). the only real downside is that it only works for the home folder, so i have to manually control stuff for /etc, but i generally prefer user configuration for most tools anyway.

i had messed around with Ansible for this in the past, but i didn’t really like it for this use case. it’s been a while tho so it’s hard to say why.

not to pile on, but you might also look at GNU Stow. i decided against it, but it’s there.

obligatory i s’pose: https://github.com/covercash2/dotfiles

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”

most Zionists i’ve met are white Protestants, and most Jews i’ve met aren’t Zionists…

this comment traveled in time from 2001 lol

simply not true. they’re no angels or open source champions, but come on.

even though those are rules as written, i like to honor the crits, with a bit of nuance. if you’re super stealthy and roll a 1, maybe it makes a small noise but doesn’t cause an alarm. if you’re dumping strength on your wet noodle wizard, maybe you’re able to move that heavy thing an inch on a 20. it’s always situational though. people get excited to see a crit, and i think it makes it more fun.

i mean, i’ve worked in neural networks for embedded systems, and it’s definitely possible. i share you skepticism about overhead, but i’ll eat my shoes if it isn’t opt in

there are language models that are quite feasible to run locally for easier tasks like this. “local” rules out both ChatGPT and Co-pilot since those models are enormous. AI generally means machine learned neural networks these days, even if a pile of if-else used to pass in the past.

not sure how they’re going to handle low-resource machines, but as far as AI integrations go this one is rather tame

it’s an analogy that applies to me. tldr worrying about having my identity stolen via physical access to my phone isn’t part of my threat model. i live in a safe city, and i don’t have anything the police could find to incriminate me. everyone is going to have a different threat model. some people need to brick up their windows

it’s not a password; it’s closer to a username.

but realistically it’s not in my personal threat model to be ready to get tied down and forced to unlock my phone. everyone with windows on their house should know that security is mostly about how far an adversary is willing to go to try to steal from you.

personally, i like the natural daylight, and i’m not paranoid enough to brick up my windows just because it’s a potential ingress.