Also, there’s the cost and community aspect of games. For the price of a movie ticket and popcorn, I can buy a game that I can play with friends for easily dozens of hours instead of us silently sitting next to each other for an hour or two.

With the increasing death of third places and the increasing cost of existing outside, video games have become their own sort of third place for people to get together and just hang out.

On the one hand, yes, and Fandom is a blight on the internet.

On the other hand, AI like ChatGPT are wrong some 53% of the time. The fact that this is another “use nontoxic glue to keep your cheese from falling off of pizza” situation doesn’t mean that Google isn’t equally culpable for doing nothing to prevent these sorts of occurrences even when the sources are right (AI is as likely to make things up that aren’t even in its cited sources as it is to actually give you info from them).

There’s a train bridge like that in my hometown, but it’s directly over the base of a fairly steep hill. Pretty much anything bigger than a work van is likely to hit it, and I’ve seen a couple of box trucks with the top 6 inches or so of their roof peeled back like a half-open can of sardines.

Like a record, baby.

nice shoe laces

Any interest in anything CAD related? Generally it’s engineering/mechanical focused, but I imagine IT knowledge would be very useful (I know Gcode is useful for 3d printing, at least).

Another Millennial here, so take that how you will, but I agree. I think that Gen Z is very tech literate, but only in specific areas that may not translate to other areas of competency that are what we think of when we say “tech savvy” - especially when you start talking about job skills.

I think Boomers especially see anybody who can work a smartphone as some sort of computer wizard, while the truth is that Gen Z grew up with it and were immersed in the tech, so of course they’re good with it. What they didn’t grow up with was having to type on a physical keyboard and monkey around with the finer points of how a computer works just to get it to do the thing, so of course they’re not as skilled at it.

Me, making a donut: “Honey? Where’d you put my UV unwrapper?”

I found a couple of interesting graphs:

I dont really like how this first one divided their different groups (there’s probably a fairly major difference between the lower end of “middle class” and the top which brings up their percentage) and the way percentages work really skews the scale, but it does paint a similar picture. This second one is the more interesting, I think, because it shows that income inequality is pretty similar across many countries - before taxes. Taxes seem to be the big equalizer in other countries, with France going from slightly worse than the US before taxes to roughly two times closer to “perfect equality,” according to the metric they used, which puts it nearly on par with Sweden, who is miles ahead of every other country before taxes. This alone doesn’t say anything about wealth inequality due to how the wealthy actually live and use their wealth, but it does paint a picture of how closing tax loopholes can work.

Because we’re talking pattern recognition levels of learning. At best, they’re the equivalent of parrots mimicking human speech. They take inputs and output data based on the statistical averages from their training sets - collaging pieces of their training into what they think is the right answer. And I use the word think here loosely, as this is the exact same process that the Gaussian blur tool in Photoshop uses.

This matters in the context of the fact that these companies are trying to profit off of the output of these programs. If somebody with an eidetic memory is trying to sell pieces of works that they’ve consumed as their own - or even somebody copy-pasting bits from Clif Notes - then they should get in trouble; the same as these companies.

Given A and B, we can understand C. But an LLM will only be able to give you AB, A(b), and B(a). And they’ve even been just spitting out A and B wholesale, proving that they retain their training data and will regurgitate the entirety of copyrighted material.

Reminds me of when I read about a programmer getting turned down for a job because they didn’t have 5 years of experience with a language that they themselves had created 1 to 2 years prior.

The argument that these models learn in a way that’s similar to how humans do is absolutely false, and the idea that they discard their training data and produce new content is demonstrably incorrect. These models can and do regurgitate their training data, including copyrighted characters.

And these things don’t learn styles, techniques, or concepts. They effectively learn statistical averages and patterns and collage them together. I’ve gotten to the point where I can guess what model of image generator was used based on the same repeated mistakes that they make every time. Take a look at any generated image, and you won’t be able to identify where a light source is because the shadows come from all different directions. These things don’t understand the concept of a shadow or lighting, they just know that statistically lighter pixels are followed by darker pixels of the same hue and that some places have collections of lighter pixels. I recently heard about an ai that scientists had trained to identify pictures of wolves that was working with incredible accuracy. When they went in to figure out how it was identifying wolves from dogs like huskies so well, they found that it wasn’t even looking at the wolves at all. 100% of the images of wolves in its training data had snowy backgrounds, so it was simply searching for concentrations of white pixels (and therefore snow) in the image to determine whether or not a picture was of wolves or not.

I think this misses 2 possibilities. The first one being the unlikely scenario where a species’ space travel program outpaces the ecological collapse of their planet, necessitating a jump into an interplanetary civilization, and the second being the rarity of certain materials required for a technological civilization to continue to exist. The Rare Earth metals are so named because of their rarity on the planet, with most deposits being the result of meteorite impacts, and even things like iron only exist in finite quanities. There’s been talk for years now of capturing asteroids in orbit around the planet for mining purposes and atmospheric “scooping” to harvest gases from the gravity wells of other planets for gases such as hydrogen.

Unless a civilization achieves 100% efficiency in a closed cycle of material use, they will need to look to the stars by necessity eventually.

Yep, they literally cannot work any other way than as a ponzi scheme. Because the people “earning” want to take more money out of the system than they put in, and the company is taking money out as well just to keep the game running and the employees paid, as well as to make a profit. So you need substantially more suckers buying into the system than the money that is being paid out.

Eventually, somebody is gonna be left holding an empty bag.

If you wanna see more, this guy has a YouTube channel all about his work with lions, cheetahs, hyenas, jaguars, etc. It’s called The Lion Whisperer.

So the way Tumblr works is that your account is basically a blog, with your home page on the site being populated with posts from the accounts that you follow. You can reblog posts onto your own account and comment on them to create individual conversation threads like this one. At one point, there was a bug in the edit post system that let you edit the entirety of a post when you reblogged it, including what other people had said previously, and even the original post. This would only affect your specific reblog of it, of course, but you could edit a post to say something completely different from the original and create a completely unrelated comment chain.

The question is whether or not Tumblr users would want such a thing.

I feel like the same thing will happen like when WordPress introduced a (bad) TikTok/streaming clone called Tumblr Live. I think less than 10% of the userbase ever interacted with it, most of the community openly hated it, and the people who did use it largely didn’t use Tumblr themselves.

I could see Tumblr users actively finding a way to defederate their blogs from everything Fediverse related.

This smells to me like WordPress reducing their workload more than anything since they own Tumblr (unless maybe there’s some sort of financial incentive to increasing the number of WordPress blogs?).

But also, considering that at one point in Tumblr’s history, you could edit other people’s posts, maybe it is an improvement.

In its own way, I’d say. I saw a poll recently asking people when they made their account, and it seems that the majority of users have been on there since the 2010s. All the toxic users left for Twitter after the porn ban, and that seems to have really chilled out the site.

Modern Tumblr reminds me of Lemmy in a lot of ways. Less tech oriented, but would rather burn the site to the ground than see it become one of the modern corporate social media sites. “Become unmarketable” seems to have become the guiding motto there.

There’s a major overlap between the two groups anyway, so hatred of one is often an easy sign of hatred of the other.

That, and how many furries are in tech related fields says a lot about the health of an online system. If the furries don’t want anything to do with it, then it must have an issue of some kind.