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Cake day: July 5th, 2023

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  • In many parts of the US it’s typical to start driving several years earlier than that, and realistically there is no way to get anywhere other than by car. Until kids can drive, they might quite literally be unable to go anywhere or do anything without an adult to drive them. It’s sprawl to an absurd degree.

    Even where bikes could theoretically be used from a distance perspective, it would likely be way more dangerous and way less practical (no bike lanes and every road is full of cars, no bike parking, you’re never getting to a bike shop for repairs without a car, …)



  • Ah, I see. That makes sense, but to be fair I think that was expected. I suspect they also pull the same data from every page where adsense is embedded regardless of browser, e.g., and every other company out there is aggregating the same sort of data every possible place they can get it from (shared sign ins, etc etc)

    Edit: It’s definitely a particularly bad look when there are several things in there that representatives for Google have apparently lied about over the years.








  • This is a thing that is true of all LLMs, but it seems like you’re misunderstanding the core issue. It CAN give outputs like that sometimes. What we CAN’T do is force it to give outputs like that ALL the time.

    It will answer “I don’t know” if its predictive text model guesses that the most common response to this would be “I don’t know”. To do that, to simplify a little, you could imagine that it reads your question, compares that to all the text in its training data, and tries to find the conversation that looks most like the question you asked, then answers whatever the person in the training data answered. But your exact question wasn’t in its training data, so if you took that mental model, and instead had it compare to 1000 similar looking things in its training model and average them, then it would hopefully do a better job of coming up with something at least close to what you actually asked. Now take it to a million, or a billion.

    When we’re asking questions about the real world, we would prefer for it to answer based on knowledge about the real world. But what if it “matches” data from a work of fiction? Or just someone who doesn’t know what they’re talking about? Or true information, but about a different subject?

    It doesn’t know anything. It doesn’t understand anything you say. It just looks at patterns that it learned from the training data and tries to guess what words are most likely to be said in that case. In other words, “here’s one case where it didn’t hallucinate” and “it will never hallucinate” are not the same thing at all.

    Edit: To clarify, it doesn’t search its training data to answer your question, so asking “was this in the training data” is impossible. By the time you interact with it, the data is long gone. It was just used for training.




  • My point wasn’t that the status quo is good or right. There’s a fundamental problem if the person most motivated to improve the property - the tenant actually living there - isn’t the one who the system rewards for doing so.

    Pretending the system we have today is different than it is is just denying reality, and isn’t an effective way to realize change. The reality that we live in is that by improving your own home while renting, you’re a sucker who is being taken advantage of by the system.




  • That’s not a great argument at all. Assuming a rent agreement with say a 1-year term, there’s a huge difference between trying to change rent in the middle of the contract period (obviously violates the contract unless it has specific provisions for this, which is also unlikely in most places) and asking for higher rent to renew for another term (which Occam’s razor says presumably is happening here). A farmer renting farmland would never be leasing for less time than it will take their crops to grow, as that would obviously be an insane risk.

    The better point here is on improving the property. Some rental contracts I’ve seen have terms where if the tenant spends money improving the property they get some kickback (part of it can be reduced from rent, e.g.). If you’re improving property someone else owns for free and expect not to be taken advantage of, then I don’t know what to tell you except that you’re a sucker.

    If there are takeaways from this post, it’s either that 1) more jurisdictions should include stuff about this as part of their legal protections for tenants, or 2) don’t be a sucker and give your landlord money for free.

    Edit: if I wasn’t clear, my point was that imo there should be better policies around tenants improving the homes they live in to begin with (because obviously nothing here was illegal)



  • XP-based progression isn’t always padding. It definitely isn’t hard to find examples where it is, but it’s also a pretty good solution to a common problem: you want the game to present a hero’s journey, where you start out weak and eventually become powerful, but you want a generic way to handle the players’ progress.

    It’s really the same as the debate in TTRPGs like D&D, where the DM could either reward levels based on XP earned from killing monsters, or could forego that altogether and award levels at set points in the story. In a video game setting where you intend things to be really open ended / the player should have a lot of freedom about what tasks they do and in what order, it’s hard to handcraft exactly what each player’s adventure and progression should look like, so an XP system is a really simple way to generalize it for everyone.

    It’s only padding if it requires you to engage with a lot of content that you otherwise wouldn’t want to do, before you can progress the story you’re actually interested in. But that’s not the fault of the system itself, it’s in how the designers chose to use it.