Or if you have good hardware that doesn’t need the transcoding. If I was loading up h265 video on my server, I’d need to convert it to h264 or something else compatible if I wanted to use it with my iPad, since it’s old enough it doesn’t support doing anything but software decoding of that codec, and it doesn’t have the strongest processor.
Hadn’t bitcoin not been viable on GPUs for over a decade? Most of the cryptocurrency hype was mining other coins on GPUs, or using them to do blockchain calculations for NFTs and things.
At the same time, it is trivially easy to strip a + alias, so I’d not trust it to do anything much at all.
At the same time, it seems to be overstepping a bit to be classifying it as equal in severity as CSAM and terroristic content. People presumably aren’t being choked to death in the video.
To a lesser extent, so did the Lorax’s Aloysius O’Hare.
In theory, they do, but the US not only doesn’t recognise the authority of the ICC, they have provisions for a military invasion of the Hague/Netherlands if a member of the US armed forces is tried in the ICC.
The anti-vegans, who are strong proponents of an all-meat diet.
They’ve also had decades of experience to back them up. They’re not just a newly spun-up agency who’s been given a multi-billion dollar budget.
That would make sense, if they were doing something like tracking how often and what categories trigger their moderation filter.
Just in case an errant update or something causes the statistic to suddenly change.
I think that’s their point. You wouldn’t have a choice again if Intel goes out of business.
Or chicken breasts. That’s how you end up a greentext.
It’s been that for a while. Tumblr’s had it a while, between the earlier days of the user base, and the site not being the most well-coded thing compared to Twitter or Reddit.
Linking to the evergreen College Dropout GoFundMe CEO skit.
“I run a website that hosts popularity contests, and if you lose, you die!”
Maybe it’s cascade effects? Something depends on something else, which depends on a third thing that depends on AWS for something?
It would be interesting what kind of effects this might have on the mice, since they would be used to mouse estrous cycles, rather than human ones.
What do you like better here than on Reddit?
The interfaces are better, and being able to integrate with mastodon is interesting.
What do you miss from Reddit?
The size of the communities, really. On Reddit, a lot of the subs have grown big enough that they can maintain themselves, whereas here, they’re pretty much dead without input. A few of the more interesting counterpart communities that I would frequent a tonne on Reddit are dead now, and if you’re just one user, it does feel like spam to try and contribute to it constantly.
It’s really only a limit subset of communities that seem very active at all, and they are generally news or politics based.
Do you feel the culture here is genuinely different, or does it eventually drift the same way?
Bit of both. The culture in the larger communities would drift the way of Reddit just by volume, but the smaller ones are a bit more unique, and not always in a good way. Because Lemmy is a bit more tech-focused, I find a lot of the main medium-sized communities tend to have similar abrasiveness you see a bit in tech, though it can depend on both community and server.
They do call it a Hellsite for many reasons. Not being profitable is one of them.
At the same time, it has calmed down a bit as the user base has aged up, and the more volatile elements have left for bluer skies.
Oddly enough, other than the mess of the NSFW filter, it’s been fairly controversy free. You can still use their API without paying globs of money, for example. Twitter and Facebook have all thrown that out, and Reddit has made other bad decisions, in addition to imploding their third party app ecosystem.
IMO the appeal isn’t the actual breakfast in bed, it’s having your partner care for you enough to make you breakfast and bring it to you.
It’s something of the law of averages. At their core, an LLM is a sophisticated text prediction algorithm, that boils down the entire corpus of human language into numeric tokens, that it averages out, and creates entire sentences by determining the next most likely word to fill the space.
Given enough data, and you need a tremendous amount of it for an LLM, patterns start to come about, and many of those end up the ones that we see in LLMs.