This is no longer true thanks to a ruling by the European Data Protection Board. Hang on, I was misreading. I believe there’s been a recent ruling, but this one ain’t it.

EDIT: See pages 39 and 40. Here, it seems as though no “equivalent alternative” is provided under these criteria. It seems to me like consent-or-pay is heading toward an eventual ban, but Heise makes it clear on their website you can consent, pay, or leave – i.e. not an “equivalent alternative” to my mind.

EDIT 2: Okay, upon reading these criteria further, it seems like this isn’t a violation of EU law but that it’s reaaaally close and that the EDPB really hates consent-or-pay as a loophole and wants it to die as soon as possible. If not breaking the law, it’s still an ethical nightmare, so the first line of my comment stands: “Heise Group, you greedy cocks.”

Heise Group, you greedy cocks.

Here’s a version of that article that doesn’t deliberately break skirt as far as legally possible EU privacy law: https://archive.ph/ZTt3K

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.

For a municipal wireless network, I’m not too bothered with how OP describes it if it’s accessed through Wi-Fi.

>Be me

>Placed by aliens in a tiny cell with no hiding spot

>Suddenly there’s a wolf spider with a 20-meter legspan outside staring me in the face

>My mind goes blank as I realize I would be torn limb from limb but for the flimsy bars of my cell walls

>“Quit assuming the human is afraid. The spider is probably friendly. You’re just projecting, bro.”

Not only would the cat stress the hamster, but more importantly long-term is that these “Critter Trail”-style enclosures are not big enough for hamsters. At all. Full stop. Maybe for a weekend if the hamster is over and you can’t bring the full thing, but especially for what looks like a Syrian(?), this is a solitary confinement cell, not a living space.

For background before I get into specifics: hamsters’ entire lot in life is that they love to run long distances, explore, and burrow. When they spend hours running on their wheel at night, it isn’t because they’re bored; it’s because that’s naturally what they do, but in a vast, open wilderness.

It’s widely accepted among the hamster care community that the barest bare minimum floor space is 450 square inches. And this is often below what veterinary organizations recommend. It seems pushy and elitist, but in reality – similar to goldfish in aquariums – the pet industry are greedy fucks who want to do everything they can to lower the barrier of entry for hamster ownership, so they market hamsters as Tommy’s first pet that he can keep on his night table. 450 is arguably a compromise just so it isn’t as daunting to hamster owners who didn’t know before and want to do the right thing. Moreover, the recommendation for Syrians specifically is 600. (The 450 figure applies to dwarf hamsters as well; they’re just as ridiculously hyperactive.)

For context, these “Critter Trail”-style enclosures are often maybe 150 sq in, or about 1/3 of that (1/4 for Syrians, which I think this one is). Hamsters have to have room to run around and explore. It isn’t a nice-to-have; for them not to is, without any hyperbole, animal abuse. Additionally, they need to be able to burrow. It seems like that isn’t possible in this enclosure, but hamsters really need that to feel secure and not constantly stressed. By “burrow”, I mean several inches of bedding (ideally throughout, but if you’re in a pinch, one corner can be the burrow mound). The combination of needed floorspace and the fact bedding needs to be stacked high for burrowing pushes a lot of hamster owners to get a glass aquarium on a good sale and use that as the enclosure (it works super well). Some also use plastic tubs, but this has a DIY aspect to make sure your hamster has enough air.

If this hamster has to live in the enclosure pictured for the rest of their life, they’ll be nothing but chronically stressed – quite probably even fated to die early from it. This isn’t meant to be preachy; it’s just a reality that hamster cage companies are lying to you to make you feel better.

EDIT: Wheel is also – unfortunately – comically small for a Syrian. Syrian wheels are much larger, and wheels this small can permanently injure them. If anyone reading this specific part doubts this, I want you to look back at the picture and, in your mind’s eye, try to put that hamster on that wheel without bending it into an elbow macaroni.

I don’t disbelieve you, but I think a huge part of the mis/disinformation problem right now is that we can just say “I read something not that long ago that said [something that sounds true and confirms 90% of readers’ pre-existing bias]” and it’ll be uncritically accepted.

If we don’t know where it’s published, who published it, who wrote it, when it was written, what degree of correlation was established, the methodology to establish correlation, how it defines corruption, what kind and how many politicians over what time period and from where, or even if this comment accurately recalls what you read, then it’s about the same as pulling a Senator Armstrong even if it means well. And if anyone does step in to disagree, an absence of sources invites them to counterargue based on vibes and citing random anecdotes instead of empirical data.

What can I immediately find? An anti-term limits opinion piece from Anthony Fowler of the University of Chicago which does do a good job citing its sources but doesn’t seem to say anything about this specific claim. Likewise, this analysis in the European Journal of Political Economy which posits that term limits increase corruption but in return decrease the magnitude of the corruption because of an inability to develop connections.

Internet comments aren’t a thesis defense. But I think for anything to get better, we need to challenge ourselves to create a healthy information ecosystem where we still can.

blackbox/BlackBox

I just don’t watch American football, even the Super Bowl, so I know the team exists but have to think about it maybe once a year.

Just don’t let Google kill JPEG XL.

Any stray pixel in a (EDIT: exported) LaTeX document is a confirmed skill issue.

Text rotated 90° clockwise and only occupies the left 1/3 of the page in an MS Word document whose pages are all numbered ‘2’? Default assumption is “not your fault.”

This is an ad for a proofreading service, so nominally it’s meant for you to use in formal writing. In that context, only a small proportion of these words are “fancy”.

That said, a thesaurus is best used for remembering words you already know, i.e. not like shown here. Careful use of a thesaurus to find new words provided you research them first – e.g. look them up on Wiktionary (bang !wt on DuckDuckGo) to see example sentences, etymologies, pronunciations, possible other meanings, usage context (e.g. slang, archaic, jargon), etc. – can work, but if you’re already writing something, just stick to what you know unless it’s dire. You should make an effort to learn words over time as they come up in appropriate contexts rather than memorizing them as replacements for other words; this infographic offers a shortcut that’s probably harder and less accurate than actually learning.

A one-night stand with a word you found in the thesaurus is going to alienate people who don’t know what it means and probably make you look like a jackass to those who do.

Just do what Josh Nichols does.

Yeah, I retract my comment. The SVG cat with the Peter Griffin ballsack chin told me it’s fine.

1. Test the cable first if you have a spare.
2. Test the AC adapter if you have a spare.
3. If both fail, inspect the charging port with a flashlight. 4a) If it looks dirty, try cleaning it out with a toothpick (if you have a dedicated plastic tool for mobile repair, use that). 4b) If it doesn’t look dirty, refer to 4a. What often happens is that lint from your pocket compacts over time as it gets in there and then gets pressed in by the charger.
4. If this doesn’t work and you have a good, locally owned mobile repair shop nearby, they might look at the port for free just to see if there’s anything you missed.

Only after all of this would I start to strongly consider the phone itself as the culprit.

don’t say “very accurate”; say “exact”

First line of this infographic is already deeply misleading. It’s the equivalent of:

don’t say “very good”; say “perfect”

It’s overly superlative compared to what it’s trying to replace. “Exact” is inherently “very accurate”, but “very accurate” is not inherently “exact”.

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.

Large X models lack a crucial component of “open-source”. Freely redistributable and modifiable for any purpose, sure, but there’s no chance in hell of auditing one, let alone if the training data is kept a secret. It’s literally impossible; human beings cannot look at a trillion weights and biases representing a single highly chaotic, unfathomably complex nonlinear function whose input and output space are the totality of human language/images/etc. and say “yup, looks good to me.” Deep learning models – contrasted with traditional machine learning models – learn their own features which almost 100% of the time would be nonsense to a human. You just have a blob of shareware when you run DeepSeek.

(They also just outright steal from billions of copyright-protected sources to create it, so calling it “open-source” is pretty funny.)