The catarrhine who invented a perpetual motion machine, by dreaming at night and devouring its own dreams through the day.

  • 3 Posts
  • 667 Comments
Joined 11 months ago
cake
Cake day: January 12th, 2024

help-circle







  • I used to moderate a forum some years ago, with incremental bans. It was warning, warning, 1d, 3d, 7d, 15d, 1m, permaban.

    It does not work well. For good users the system is irrelevant, they drop the behaviour after a single warning; shitty users keep the same behaviour even after the short bans are over, and then evade the larger bans, so you’re basically taking multiple mod actions for what could be handled with a single one.

    Eventually the forum shifted into a “three warnings and you’re permabanned” system, but by then I wasn’t a mod there any more so I don’t know how well it worked.


  • No. But I think that it’s often poorly used.

    Most users are reasonable and should be treated as such by default; a simple warning goes a long way. Sometimes an overall good user is being really shitty so you ban them for, like, a week? Just to let them chill their head.

    Permaban is for the exceptions. It’s for users who cannot be reasoned with, will likely behave in a shitty way in the future, and have a negative impact on the community.




  • I agree that Reddit will become irrelevant to internet power users. However, I disagree that it takes a massive fuckup to lose the critical mass of users.

    A simple way to explain this is to imagine that everyone has an individual “I’m pissed and I leave” threshold; if a platform displeases a user more than that threshold, they leave.

    For power users, this threshold is really low, so they ditch platforms like Reddit faster. However, that does not mean that the others aren’t getting displeased - they do; it might not be enough to convince them to leave, but it quickly piles up with other things displeasing them.

    As such, even a large platform can lose that critical mass of users over time, even without a massive fuckup. It’s just about small things piling up.

    Another thing to consider is that power users are more important to a platform than the rest of the userbase, because the power users interact with the platform more. And they’re typically the ones doing janny crap, or finding and sharing content, or that actually have anything meaningful to add instead of “lol lmao”. So once the power users leave, the platform becomes less desirable for the others too, and that’s recursive - as the power users leave, the almost-power users leave too, then the ones after them, so goes on. And there the critical mass goes down the drain.






  • I know you are, but the argument that an LLM doesn’t understand context is incorrect

    Emphasis mine. I am talking about the textual output. I am not talking about context.

    It’s not human level understanding

    Additionally, your obnoxiously insistent comparison between LLMs and human beings boils down to a red herring.

    Not wasting my time further with you.

    [For others who might be reading this: sorry for the blatantly rude tone but I got little to no patience towards people who distort what others say, like the one above.]




  • It doesn’t need to be filtered into human / AI content. It needs to be filtered into good (true) / bad (false) content. Or a “truth score” for each.

    That isn’t enough because the model isn’t able to reason.

    I’ll give you an example. Suppose that you feed the model with both sentences:

    1. Cats have fur.
    2. Birds have feathers.

    Both sentences are true. And based on vocabulary of both, the model can output the following sentences:

    1. Cats have feathers.
    2. Birds have fur.

    Both are false but the model doesn’t “know” it. All that it knows is that “have” is allowed to go after both “cats” and “birds”, and that both “feathers” and “fur” are allowed to go after “have”.