Correct, as it has to. In addition to behavior, CheckUsers use IP addresses to help identify sockpuppets. If you could bypass the exemption by just saying “here’s a new account; pls exempt”, it would quickly become common knowledge among sockmasters that all they need is to quickly ask and be accepted days later.

At that point, the block on proxy editing near-completely fails at one of its main functions.

I don’t know what you mean by “identify yourself”. You need an account with a trustworthy history of editing, at which point you can request the exemption.

You can get an exception.

True. Facebook from its inception as Facemash was a way for creepy, misogynist slimeballs to ogle and “rank” women they went to school with against their consent by harvesting public databases. “It was just a prank bro.” —Mark ‘Real Human’ Zuckerberg

You are actually correct that Wheel11 had 10 other versions. It just happens that those 10 other versions were released before September 1987.

Not sure how this applies when:

• X11 was the only standard prior to Wayland.
• GNOME is dropping X11 in a short time.
• KDE’s telemetry even five months ago showed 80+% of (that portion of) their userbase uses Wayland, and they plan to drop X11 once they have a concrete set of problems worked out.
• Hyprland and Sway run Wayland exclusively.
• Cinnamon, MATE, and Xfce are working on Wayland sessions. Cinnamon’s is there but, I think, still experimental.
• Budgie is working to go Wayland-only.
• There’s no sign that Wayland will stop improving from a state that’s arguably already much better than X11.
• X11’s actual maintainers barely want anything to do with it beyond bug fixes, and the only person who wants to “innovate” it via a fork is a bigot and a fucking moron who doesn’t know things you learn in CS 101.
• X11’s maintainers are majorly involved in developing Wayland and have been since the start. This is their idea.

It seems like it went from “Situation: there is one standard” to “Situation: there are two standards developed by largely the same people with one set to replace the other”, and then soon: “Situation: there is one standard and one translation layer kept around for a decade or so for compatibility.”

Not every single time someone tries to make things better is this xkcd relevant; this had nothing to do with unifying standards and everything to do with superseding one.

Has anyone so far stopped you from using your outmoded tangle of garbage? Or do you just not like that major desktop environments are switching to more sensible defaults?

If you’re worried about GNOME 50 dropping X11 in the future… okay? Nobody’s obliged to maintain your shit. Linux is all about choice, and it’s their choice not to spend untold thousands of hours working to keep X11 usable, just like it’s your choice to change your Linux to something that does still use it. Switch to any one of the other desktop environments; see if the Wayland Illuminati or whatever gives a shit.

If “the wheel” is achieved by making literally every application you run a keylogger, I’m very cool with Wayland “reinventing the wheel”. X11’s handling of user input is a fucking embarrassment.

Besides just the Steam Deck and Proton, a big reason people are finally sticking around on Linux is because using X11 feels exactly like what it is: a cobbled-together piece of archaic shit that needs to be left behind.

Wayland by contrast feels fantastic to run, and on my GTX 1070 with proprietary drivers, the only current issue I have with it is how Firefox picture-in-picture popouts don’t stay on top by default.

I remember speculating as a (small) kid that the AI soldiers in Battlefront II’s local multiplayer might be real people employed by the developer. Not the brightest child was I.

Rob DenBleyker’s English teacher is en route to his house to beat him with a tire iron (“me and Clark Kent”).

The quote only came from searching for luxchat on DDG, and even then it led to a login-only page of lux.chat. So unfortunately, I can’t. And even then, I’m doubting luxchat and lux.chat are by the same people.

I know what community this is, Blaze, but it baffles me to recommend this over Element.

• The Matrix Foundation is a UK-based CIC.
• IIRC the for-profit company which develops the Element app which is commercial FOSS is UK-based – therefore it’s European.
• Luxchat, however, lacks support for Linux, the obvious choice for removing oneself from US-based software.
• Luxchat, however, isn’t made by the people behind the protocol like Element.
• Unlike Element’s company, which exists to make money to fund Matrix’s development, Luxchat is full-stop a for-profit company ostensibly supporting nothing. Edit: I was mistaken; a GIE is a company (they’re registered C8 with the Commercial and Companies Register in Luxembourg), but the type of company’s: “aim is not to make a profit, but it can do so simply as an accessory, as the profit resulting from the joint action must also go directly to its members.” That said, I can’t see anything about any money going back to the development of Matrix, which is still a major red flag to me compared to Element.
• The tiny-ass website doesn’t do a thing to estsblish what license Luxchat is under. Element, by contrast, is squarely FOSS.
• While desperately, fruitlessly searching for any sort of license, one of the few pages that turned up from their website just read “Create stunning AI chatbots with our 21st.dev-inspired Glassmorphic UI.” as one of their premium services. 🤮 This may(?) be something different. See below.
• I can find effectively fuck-all about this company that I’m supposed to be trusting with my privacy.

Edit: So to be clear, they say it’s a fork of Element, but that doesn’t tell me the license or give me the source code. Trying to find the source code for the messaging app just returns this crap about their an AI service which is just ChatGPT wrapper number 486 billion.

Edit 2: Okay, I think the confusion over this AI BS is that there’s lux.chat – ChatGPT wrapper garbage – and luxchat – an Element fork. I have no idea if these are related. If they are, this information is too hard to find. If they aren’t… I mean maybe if you had more information on your website, luxchat, I wouldn’t have to scour the Internet to find that information and run into lux.chat.

Edit 3: The FAQ clears literally none of my questions up. Cool.

Dude, I’m sorry, I just don’t know how else to tell you “you don’t know what you’re talking about”. I’d refer you to Chapter 20 of Goodfellow et al.'s 2016 book on Deep Learning, but 1) it tragically came out a year before transformer models, and 2) most of it will go over your head without a foundation from many previous chapters. What you’re describing – generative AI training on generative AI ad infinitum – is a death spiral. Literally the entire premise of adversarial training of generative AI is that for the classifier to get better, you need to keep funneling in real material alongside the fake material.

You keep anthropomorphizing with “AI can already understand X”, but that betrays a fundamental misunderstanding of what a deep learning model is: it doesn’t “understand” shit about fuck; it’s an unfathomably complex nonlinear algebraic function that transforms inputs to outputs. To summarize in a word why you’re so wrong: overfitting. This is one of the first things you’ll learn about in a ML class, and it’s what happens when you let a model train on the same data over and over again forever. It’s especially bad for a classifier to be overfitted when it’s pitted against a generator, because a sufficiently complex generator will learn how to outsmart the overfitted classifier and it will find a cozy little local minimum that in reality works like dogshit but outsmarts the classifier which is its only job.

You really, really, really just fundamentally do not understand how a machine learning model works, and that’s okay – it’s a complex tool being presented to people who have no business knowing what a Hessian matrix or a DCT is – but please understand when you’re talking about it that these are extremely advanced and complex statistical models that work on mathematics, not vibes.

Your analogy simply does not hold here. If you’re having an AI train itself to play chess, then you have adversarial reinforcement learning. The AI plays itself (or another model), and reward metrics tell it how well it’s doing. Chess has the following:

1. A very limited set of clearly defined, rigid rules.
2. One single end objective: put the other king in checkmate before yours is or, if you can’t, go for a draw.
3. Reasonable metrics for how you’re doing and an ability to reasonably predict how you’ll be doing later.

Here’s where generative AI is different: when you’re doing adversarial training with a generative deep learning model, you want one model to be a generator and the other to be a classifier. The classifier should be given some amount of human-made material and some amount of generator-made material and try to distinguish it. The classifier’s goal is to be correct, and the generator’s goal is for the classifier to pick completely randomly (i.e. it just picks on a coin flip). As you train, you gradually get both to be very, very good at their jobs. But you have to have human-made material to train the classifier, and if the classifier doesn’t improve, then the generator never does either.

Imagine teaching a 2nd grader the difference between a horse and a zebra having never shown them either before, and you hold up pictures asking if they contain a horse or a zebra. Except the entire time you just keep holding up pictures of zebras and expecting the child to learn what a horse looks like. That’s what you’re describing for the classifier.

Currently, Karton is actively developed by Derek Lin, a University of Waterloo student participating in Google Summer of Code 2025. His primary aim is to deliver a virtual machine manager that truly belongs in the KDE ecosystem.

Neat, but celebrating this as “finally getting” seems premature at best.

This is entirely correct, and it’s deeply troubling seeing the general public use LLMs for confirmation bias because they don’t understand anything about them. It’s not “accidentally confessing” like the other reply to your comment is suggesting. An LLM is just designed to process language, and by nature of the fact it’s trained on the largest datasets in history, practically there’s no way to know where this individual output came from if you can’t directly verify it yourself.

Information you prompt it with is tokenized, run through a transformer model whose hundreds of billions or even trillions of parameters were adjusted according to god only knows how many petabytes of text data (weighted and sanitized however the trainers decided), and then detokenized and printed to the screen. There’s no “thinking” involved here, but if we anthropomorphize it like that, then there could be any number of things: it “thinks” that’s what you want to hear; it “thinks” that based on the mountains of text data it’s been trained on calling Musk racist, etc. You’re talking to a faceless amalgam unslakably feeding on unfathomable quantities of information with minimal scrutiny and literally no possible way to enforce quality beyond bare-bones manual constraints.

There are ways to exploit LLMs to reveal sensitive information, yes, but you have to then confirm that sensitive information is true, because you’ve just sent data into a black box and gotten something out. You can get a GPT to solve the sudoku puzzle, but you can’t then parade that around before you’ve checked to make sure the puzzle is correct. You cannot ever, under literally any circumstance, trust anything a generative AI creates for factual accuracy; at best, you can use it as a shortcut to an answer which you can attempt to verify.

This user’s entire history (username included) is spent signal-boosting demonstrably false, bad-faith attacks against Wikipedia. The article’s premise is that the ADL of all organizations is a good arbiter of what is antisemitic when it comes to coverage of Israel’s genocide in Palestine. The article starts with “This past March, researchers from the Anti-Defamation League accused Wikipedia of biased coverage of the Israeli-Palestinian conflict.”

Newsflash: it isn’t. The ADL consistently treats anyone who dares to challenge Israel’s genocide as antisemitic. This user is a ridiculous troll and should be banned from communities for their transparent, bad-faith agenda. I’m sure if there’s a story worth posting, somebody other than “wikipediasuckscoop” can post it. It’s so transparent that in an age where the Internet is blanketed with far-right disinformation, one of the last remaining bastions of truth that refuses to compromise and bend to said disinformation will come under attack by bad-faith, far-right actors desperately flailing to discredit it.

I’d like to point out that when the article says “propagandists” (i.e. people opposed to Israel’s genocide) and arbitrarily delineates them from “editors”, what it’s failing to point out (likely because a) its author doesn’t understand shit about fuck or b) its author doesn’t care) is that any article related to a conflict between Israel and Arab countries is extended protected by default (on top of other heavy editing restrictions). This means that it can only be edited 1) on a registered account 2) which is at least 30 days old and 3) which has made at least 500 edits. This isn’t 2001:0db8:85a3:0000:0000:8a2e:0370:7334 typing “Izreel sux lololol” or even just some random sockpuppet account trying to insert anti-Israel bias. You have to be an experienced editor to make changes to these articles. Every single one of these even remotely controversial public changes is put under a microscope and discussed ad nauseum by other experienced editors on the corresponding talk page – not just to make sure that it’s covered without bias per NPOV but that its claims are suitably backed by reliable, independent sources.

And other reasons why “security through obscurity” is bullshit.

the covfefers*