There are heretical forks that add it in though

Escalate to management as quickly as possible so you’re not just annoying some poor front desk worker that had nothing to do with it.

They’re probably referring to quantum entanglement, which affects the entangled particles instantly.

By the time we invent any sort of lightspeed travel, we’ll have long conquered quantum entanglement. If you have a signal transferred over a properly quantum entangled technology, the signal would transfer instantaneously.

It’s already beatable right now, there are services in third world countries where people get paid fractions of a penny to solve captchas for machines.

Fools are easily parted with their money, and I typically view a lot of misinformation as ways to seek out those exact fools. Not all of it, but a lot.

Take a bunch of crazy people that polite society doesn’t agree with, make them feel seen, and they throw money at you.

Or I can pay nothing and get a plain video file that I can do anything I want with, and play on any device without needing a player. And as long as I keep that file backed up somewhere, I’ll always have a copy of it.

The TV business is struggling to learn the lesson the music industry learned a long time ago.

At the time I decided on it, I used Sakura as a terminal emulator, plus it’s on the home row. I use a different term emulator now, but the muscle memory remains.

Super + S for a terminal, Super + F for Firefox.

Interesting to find a RyanF9 video here and not in a motorcycle community. But yeah, probably most people here don’t have much interest in Gore-Tex unless they ride or do other outdoorsy things.

I would say of the services to give money to, Discord is on the lesser evil side.

Sure, they lock a bunch of stuff behind Nitro, but they’re at least only giving people ads for their own stuff and not scams or dong pills. Because if nobody paid for anything, that money would have to come from somewhere.

The only thing more eco-friendly than buying an eco-friendly printer, is to not buy a new printer at all.

Both of my local libraries offer printing at $0.25 a page. For photos, I just go to the photo lab at the store and print them there.

Both are cheaper than owning a printer unless you’re doing a ton of it, and in the former case, I get to support a library just a little bit.

Doing a quick skim on my phone, your microphone quality is fine. I would probably lower the game audio in post a bit to make the sound more distinct, but it’s only noticeable when the game does loud stuff.

Speaking for LLMs, given that they operate on a next-token basis, there will be some statistical likelihood of spitting out original training data that can’t be avoided. The normal counter-argument being that in theory, the odds of a particular piece of training data coming back out intact for more than a handful of words should be extremely low.

Of course, in this case, Google’s researchers took advantage of the repeat discouragement mechanism to make that unlikelihood occur reliably, showing that there are indeed flaws to make it happen.

Accumulated knowledge in our society really is frail. Take a computer mouse, tons of people are involved in making them, they’re considered extremely simple tools. Yet not one person on the planet could go out into nature, get the natural resources required, and without help turn those resources into a working computer mouse.

I’m not an expert, but I would say that it is going to be less likely for a diffusion model to spit out training data in a completely intact way. The way that LLMs versus diffusion models work are very different.

LLMs work by predicting the next statistically likely token, they take all of the previous text, then predict what the next token will be based on that. So, if you can trick it into a state where the next subsequent tokens are something verbatim from training data, then that’s what you get.

Diffusion models work by taking a randomly generated latent, combining it with the CLIP interpretation of the user’s prompt, then trying to turn the randomly generated information into a new latent which the VAE will then decode into something a human can see, because the latents the model is dealing with are meaningless numbers to humans.

In other words, there’s a lot more randomness to deal with in a diffusion model. You could probably get a specific source image back if you specially crafted a latent and a prompt, which one guy did do by basically running img2img on a specific image that was in the training set and giving it a prompt to spit the same image out again. But that required having the original image in the first place, so it’s not really a weakness in the same way this was for GPT.

I asked ChatGPT to generate a utopic looking city but make the buildings curvy. It got pretty close.

I consider it occasionally, then remember I’m paying a ton more to save like, 15 minutes. Then I just go get it.

The issue, I think, was because most of what I use it for is anime. So some shows wanted the Japanese title, others wanted the English title, some couldn’t be found at all. My US TV shows and movies never had that problem.

The title matching is what made me go to Plex. Some shows were impossible to get sorted right on Jellyfin. Plus there’s a lot more ecosystem around Plex