@Kayana - eviltoast
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Joined 8 months ago
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Cake day: January 6th, 2024

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  • Huh, TIL.

    Regarding your edit, that amount wasn’t the cumulated cost of whatever Limewire were distributing, that would be idiotic indeed; rather the RIAA tried to call for a ruling that somehow those guys were causing $150,000 in damages - per instance. Now the article unfortunately doesn’t state how they possibly tried to justify that number, and I can’t be bothered to research that myself. Another thing that would interest me is how the plaintiff expected them to pay with almost every dollar on Earth.

    So while I don’t think this had anything to do with “lost sales”, I do agree with the possible fines and damage calculations not being fit for any sort of realistic purpose at all.





  • That could work too, but for many people, being able to dodge/avoid hits is exclusively the DEX bonus to AC, and they believe it doesn’t have to do anything with hit points.

    I’m on two minds about that: On the one hand, it’s true that you’re far better at dodging in lighter (or no) armor. OTOH, I agree with you that experience teaches you to decide where you’re going to get hit if at all. So it might be something like “raise your arm so the strike doesn’t hit your belly”.



  • Hmm, you’d probably have to have access to something like DndBeyond’s data to compile such a chart (or use one they compiled). Problem is, there doesn’t seem to be anything like that. The only published data visualisations are about races, classes and names.

    So I don’t think you can just search for it, the only other option I see is gathering that data (if from a smaller sample) yourself, by creating a poll asking for their ability spreads if they used point buy. You could try and advertise it in appropriate communities, and once you feel like your sample size is big enough, you can calculate the percentages.

    I wish there was an easier way (and maybe there is and I just didn’t look far enough), but from my chair, that’s the only option.



  • Well, one way to easily replicate point buy’s range per stat (if not its distribution limit over all stats) would be 7 + 1d8. You could also do: Start every stat from 12, and if you want to increase one, you can do so by rolling a d4 as a bonus (rerolling on a 4). However, to do that you’ll have to decrease a different one by another / the same d4. So you’ll still have the same range, but like with point buy there’s an element of control and choice to it.

    Regarding bigger ranges, one way could be using that same method, only with bigger dice (and possibly other starting points). E.g. you could start from 11 and roll a d8, rerolling an 8 if you’re adding it as a bonus. That example would give you values anywhere from 3 to 18, and it’s much more swingy than 4d6dl. Of course, if the high variance is an issue, you can experiment with dropping highest or lowest on 2d8.

    For example, if you’re dropping lowest on bonus rolls and penalty rolls, you’ll get characters with high highs and low lows, or if you’re doing it the other way around, you’ll get characters where each stat is fairly equal, without much variance to speak of.

    There isn’t much more I can say without knowing how much variance and player choice you want to include.








  • But you just completely ignored everything I said in that comment.

    Mathematically, that is precisely how O notation works, only (as I’ve mentioned) we don’t use it like that to get meaningful results. Plus, when looking at time, we can actually use O notation like normal, since computers can indeed calculate something for infinity.

    Still, you’re wrong saying that isn’t how it works in general, which is really easy to see if you look at the actual definition of O(g(n)).

    Oh, and your computer crashing is a thing that could happen, sure, but that actually isn’t taken into account for runtime analysis, because it only happens with a certain chance. If it would happen after precisely three days every time, then you’d be correct and all algorithms would indeed have an upper bound for time too. However it doesn’t, so we can’t define that upper bound as there will always be calculations breaking it.



  • That’s a point I didn’t actually think about, touché. Let’s go through this then:

    Before Covid (in my country at least), there was this massive push for more homes, because the interest rates were so low. Everyone was building a house, because it was so very cheap (in interest at least, not necessarily in costs). At that point, wise developers might have decided to not take on any big new projects, focusing on finishing their current ones instead of trying to ride out this bubble.

    Then Covid hit and the supply chains broke down. That was sudden and couldn’t be expected, I’ll give you that. But now, four years later, the main reason (in my opinion) for the low occupancy is the newfound interest for WFH, also resulting from Covid. Who needs an expensive condo in a crowded city if you can have a cheap flat in a small town instead?

    So in this case, I’ll (partially) retract my prior opinion and instead state that while a crash could’ve been seen somewhere on the horizon, Covid with all its consequences certainly couldn’t have been foreseen.

    I’m not familiar with the housing prices in Toronto compared to smaller cities in Canada, but perhaps those developers need to bite the bullet and lower their asking prices, because I’d imagine selling for less is still better than holding onto dead weight, praying for demand to go up again.