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it’s interesting that they’re using pretty modest hardware (i assume they mean 24 cores not CPUs) and fairly outdated dependencies.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

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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

definitely not the real reason for a project like this to exist. Python package management can be nightmarish at times depending on what you’re doing. between barebones requirements.txt, Poetry, and the different condas there’s a ton of fragmentation, and none of them do everything you’d want in an ideal way. above and beyond speed, i think uv is another attempt at it. but it could just be another classic xkcd moment where now there’s just another standard to deal with

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.