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Joined 2 years ago
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Cake day: July 1st, 2024

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  • Most of the games I liked back in the day don’t really hold up, though. Like, I loved the NES version of Strider back then; it had a strange and intriguing plot, I loved the trappings of the dragon-shaped space station and the plasma sword and the magnetic boots that let you walk on the ceiling, and I was a total sucker for any hint of metroidvania-type progression. But when I try to play it now, I notice the controls are awful, the collision detection is janky, the level designs are dull, and the “triangle jump” is nearly unusable.

    Whereas I tried Clash at Demonhead, and that’s surprisingly decent. A little simplistic, sure, and it bogs down as you get further into it, but it’s pretty amusing. Same with Guardian Legend: I can’t get through it, but I can definitely see the appeal. Edit: it may have been clear from context, but these are ones I didn’t play back in my youth.

    I do love indie stuff, though. I just played through Alwa’s Legacy, and that one is quite good. (Some people might find it too easy, but I tend to appreciate that at this point. I don’t have the reflexes I once did.)

    So I tend to agree that there’s still great stuff around. I just don’t think nostalgia is the whole story with retro games. I think a lot of it has to do with the retro games being the work of a big studio, even though the mechanics today would be relegated to an indie studio. There are subtle ways that affects the experience, that I think people are unconsciously aware of.

    But yeah, there are great games out there today, especially on PC.




  • My favorite is Lakoff’s category theory. He notes that philosophy largely pretends that categories are defined based on rules of inclusion and exclusion, but in fact human thinking is mostly based around categories defined by a prototypical example and various relations to that example. So, e.g., a penguin and a sparrow are both birds, but a sparrow is more like the prototypical bird, since it’s smaller and it flies.

    Many of the great philosophical questions over the years have been about trying to decide on precise rules of inclusion and exclusion for categories, even though the closer people look, the less it seems like there’s a meaningful boundary (e.g. “life” vs “non-living,” where you start to see things like viruses, prions, etc. that don’t fit cleanly into either category).




  • Fundamentally he doesn’t mean that he doesn’t know what happened. He means that he hasn’t received his talking points about this yet.

    He knows that there will be some way that the right-wing media ecosystem is going to frame this, and it’s very important that his framing match theirs exactly. Somewhere a think-tank is working on the perfect wording that will turn an innocent victim into, somehow, an evil communist traitor who was a threat to an ICE officer and also somehow to the very concept of America. And if he tried to give his own spin on it before that, it would only make their job more complicated.

    So yeah, he may know that an innocent man was shot, again. But he doesn’t yet know the important stuff: how to make him the villain. He just hasn’t had the time to get caught up on that! Give him a break.




  • Sounds like it could be really useful for VR displays. The best optics for VR right now use polarizers to fold the optical path repeatedly through the same lens, but a side effect is that only a small portion of the light makes it to the users’ eyes.

    Could also extend the battery life of phones; the display is the main power draw for most phones, and the higher brightness make that worse.

    I understand why people are reacting badly to these being described as “brighter,” but it does also mean “more energy efficient for the same brightness.”



  • Are they just clueless about how anything works?

    That’s certainly how I read it. They seem to think that if you feed the LLM a new prompt that tells it it made a mistake, it’ll update itself to avoid that mistake in the future. Which isn’t how any of this works, but I can understand why they wouldn’t know that given the marketing from the big AI companies.

    A couple of years from now everyone will know this was really dumb, but right now only the engineers know that, and management won’t listen to them. I guess they’ll learn the hard way.




  • No, my understanding is that they’re bringing in revenue on token generation, but it’s exceeded by the costs of token generation (running data centers, so, electricity and cooling). They definitely want to make a profit on token generation, but they’re afraid that raising costs that high too quickly would drive customers to switch to other providers. So they’ve reduced the amount they’re subsidizing token costs, but not switched over to making a profit.

    I can’t find a good citation for this, though, so it’s possible I’m mistaken. They also have huge costs associated with buying new GPUs and building new datacenters, so they’re operating at a massive loss either way, and it’s a little hard to find articles which tease apart the two aspects.

    In any case, operating at a massive loss for the first few years is practically standard operating procedure in silicon valley at this point, and sometimes it eventually leads to a profitable, even wildly profitable, business (e.g. Amazon). But it does require a steady stream of investors and a steadily increasing market valuation. That’s…we’ll have to see what happens on that front.