if you really want to stick it to Google you have to go for Firefox or something derived from it. Chromium gives Google a ton of leverage to push features to all of their downstreams. not sure what engine these are using, but i also prefer to use Firefox because it’s open source. if these were open source you could easily see which engine they’re using.

it’s so much worse than the normal search. i would search for “dog” or “pasta” or “house” and get a pretty good result, but this conversational shit is just plain worse. and the “conversational” aspect is useless

pretty common misconception about how “AI” works. models aren’t constantly learning. their weights are frozen before deployment. they can infer from context quite a bit, but they won’t meaningfully change without human intervention (for now)

is there such a thing as “legitimate criticism” against an entire race of people? this writer is bonkers, and you can tell from the intro. seems like the actual content of the post was buried beneath the first paragraph where a rare few would find it. maybe it was wrong or illegal to fire this guy for being a racist asshole (being a state funded org or something?), but couching it in this narrative of “cancel culture” and “a violation of the first amendment” has fashy vibes to me. institutions should be allowed to control the narrative set by their employees. i understand that as part of my company my words reflect on them, and it’s up to their discretion whether they want to continue to associate with me based on the things i say. you have every right to say racist shit on your favorite fascist-owned platform, but everyone else has the right to tell you to fuck off.

bruh i know people in their 40s making 6 figures that couldn’t read an error message if it would save ten generations of their family.

i worked in Android development for years. i used Kotlin and Jetpack Compose in alpha. their docs weren’t perfect but they existed. in my experience good documentation evolves with the project and isn’t tacked on as an afterthought.

otherwise i get you point and really don’t know enough about the drama to pick a side. i just want my software to work 🤷‍♂️

ETA: honestly documentation as a first class primitive in Nix is a selling point. if only that consistency could be applied to high level docs

The Nix project has long intended to release version 3.0 when flakes 4 are stable. With Determinate Nix 3.0, we’ve fulfilled that promise

i noticed this language recently as well. i’m glad Nix upstream is defending themselves, but honestly, the place where Nix “3rd party” tooling shines is in documentation. i swear to god the #1 things holding back Nix adoption is piss poor documentation. and i love the idea of Nix to be clear, but if the official docs are years out of date for installing popular user space software like CUDA and the Rust toolchain, for which the docs are either far out of date or using solutions that are not standard or otherwise clunky, then it’s silly to recommend for my work. and also to be clear, i could pull string and make this happen at my company—we’ve done it for Rust—, but i will not stick my neck out for this kind of tribalism.

on one hand tho, Determinate Systems provided clear install instructions for flakes (which is an important feature, for a lot of maintainers for sure) and did make it clear what the differences were (some of which were clearly better defaults), even if the verbiage is a bit aggressive. i honestly don’t know what it will take. i’m slowly but surely becoming competent in the ecosystem, but i get the vibe from forum posts (which i’m forced to read in lieu of docs) that there’s this “why don’t you already get this” from the already established community. and maintainers act like there’s no reason for these “soft forks” to exist. Nix is not straightforward, and, no, the language isn’t simple enough to learn in an hour. adoption requires good docs

member when all the big cool web 2.0 companies had public facing APIs?

the reactionary opinions are almost hilarious. they’re like “ha this AI is so dumb it can’t even do complex systems analysis! what a waste of time” when 5 years ago text generation was laughably unusable and AI generated images were all dog noses and birds.

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

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.

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…

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.

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.

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