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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.

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.

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?

as you might have guessed i haven’t really tried it, but i have been reading about it. that said i have used “drop in replacement” tools like this (we use pnpm at work), and a drop in replacement is not without quirks. they wouldn’t have made a different tool altogether if it was really a 1:1 replacement. just because the commands are the same doesn’t mean it behaves the same. i.e. i doubt one person on the team could be using uv while everyone else sticks to pip

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

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…

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

sure it does. it won’t tell you how to build a bomb or demonstrate explicit biases that have been fine tuned out of it. the problem is McDonald’s isn’t an AI company and probably is just using ChatGPT on the backend, and GPT doesn’t give a shit about bacon ice cream out of the box.

not sure what you mean by expensive. i run language models on my laptop that are pretty good at this type of task. and, yes, these models are infinitely easier and cheaper ultimately than trying to change the human proclivity for attention seeking behavior.

you’ve not seen the type of email chains i get at work. personally i think it should be illegal to respond-all to an email chain with hundreds of people with “Great job team!!! 🎉”. but it would be great to have a LM to read it near instantaneously for me to be like “oh yeah there was a product release and here’s a few relevant metrics”. doesn’t matter if it’s 100% in on every subtle detail, and a decent summary could tell me where or if i even should dig into details.

a lot of things are unknown.

i’d be very surprised if it doesn’t have an opt out.

a point i was trying to make is that a lot of this info already exists on their servers, and your trust in the privacy of that is what it is. if you don’t trust them that it’s run on per user virtualized compute, that it’s e2e encrypted, or that they’re using local models i don’t know what to tell you. the model isn’t hoovering up your messages and sending them back to Apple unencrypted. it doesn’t need to for these features.

all that said, this is just what they’ve told us, and there aren’t many people who know exactly what the implementation details are.

the privacy issue with Recall, as i said, is that it collects a ton of data passively, without explicit consent. if i open my KeePass database on a Recall enabled machine, i have little assurance that this bot doesn’t know my Gmail password. this bot uses existing data, in controlled systems. that’s the difference. sure maybe people see Apple as more trustworthy, but maybe sociology has something to do with your reaction to it as well.

i mean, you’re right. i’m just saying it’s a little silly to ship a Python interpreter when there are easier, better supported ways to do the same thing.

looks like tesseract provides C bindings which are probably being utilized in those apps.

people generally probably hate the iOS integration just because it’s another AI product, but they’re fundamentally different. the problem with Recall isn’t the AI, it’s the trove of extra data that gets collected that you normally wouldn’t save to disk whereas the iOS features are only accessing existing data that you give it access to.

from my perspective this is a pretty good use case for “AI” and about as good as you can do privacy wise, if their claims pan out. most features use existing data that is user controlled and local models, and it’s pretty explicit about when it’s reaching out to the cloud.

this data is already accessible by services on your phone or exists in iCloud. if you don’t trust that infrastructure already then of course you don’t want this feature. you know how you can search for pictures of people in Photos? that’s the terrifying cLoUD Ai looking through your pictures and classifying them. this feature actually moves a lot of that semantic search on device, which is inherently more private.

of course it does make access to that data easier, so if someone could unlock your device they could potentially get access to sensitive data with simple prompts like “nudes plz”, but you should have layers of security on more sensitive stuff like bank or social accounts that would keep Siri from reading it. likely Siri won’t be able to get access to app data unless it’s specified via their API.

no need for Python. there’s a Google SDK, ML Kit, that will do the heavy lifting on this. if that’s not acceptable, TensorFlow, PyTorch, and ONNX support Android, albeit not as nicely integrated.

your image processing pipeline will be imageSource -> RGB encoding -> OCR -> profit. your OCR just needs an RGB encoded image. doesn’t matter if that’s a JPEG or YUV video feed at the source.

as for if there’s an app that fits OP’s exact use case, dunno.

same as with crypto. the software community started using GPUs for deep learning, and they were just meeting that demand

tbh this research has been ongoing for a while. this guy has been working on this problem for years in his homelab. it’s also known that this could be a step toward better efficiency.

this definitely doesn’t spell the end of digital electronics. at the end of the day, we’re still going to want light switches, and it’s not practical to have a butter spreading robot that can experience an existential crisis. neural networks, both organic and artificial, perform more or less the same function: given some input, predict an output and attempt to learn from that outcome. the neat part is when you pile on a trillion of them, you get a being that can adapt to scenarios it’s not familiar with efficiently.

you’ll notice they’re not advertising any experimental results with regard to prediction benchmarks. that’s because 1) this actually isn’t large scale enough to compete with state of the art ANNs, 2) the relatively low resolution (16 bit) means inputs and outputs will be simple, and 3) this is more of a SaaS product than an introduction to organic computing as a concept.

it looks like a neat API if you want to start messing with these concepts without having to build a lab.