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InitialsDiceBearhttps://github.com/dicebear/dicebearhttps://creativecommons.org/publicdomain/zero/1.0/„Initials” (https://github.com/dicebear/dicebear) by „DiceBear”, licensed under „CC0 1.0” (https://creativecommons.org/publicdomain/zero/1.0/)V
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3 yr. ago

  • It's not actually a bad metric. Basically the demand for cocaine is going to be more or less constant over something like a 6 month window, but the supply is going to fluctuate, so the dealers are going to cut what they have until it's enough to meet demand. That means you can basically use the purity as a good proxy for available supply; when it goes down, the dealers are feeling the squeeze and having to stretch their limited supply, when it goes up the dealers aren't having a hard time getting the goods so they can afford to adulterate it less.

  • More circular financing. Definitely a sign that everything is going great.

  • Absolutely there's a difference. LLMs, when it comes to this specific task, are better. That's why they're being used here. It's a job they are uniquely well suited to. They do indeed come with high hardware requirements, which is why you're not forced to use them, and why they provide the option to offload the work to a cloud service.

    Personally I would absolutely not want to ever feed my documents into an off-device model, but the point of self-hosted software is that it does what you tell it to and they absolutely should include letting you make bad decisions.

  • I think you're under the impression that the difference between those things is far greater than it actually is. Large Language Models work by developing statistical maps of associations. That's discrimination. They're a direct evolution of categorization models. The ability to associate a hash of a JPEG with "cat" is the same as the ability to associate "How are you?" with "Great, how about you?" It's all associative mapping. LLMs are just the current leading edge of that technology. If you want to, for example, generate a list of tags that describe a document, an LLM is the best tool we currently have for doing that.

    To put it another way, what you term "discriminative AI" is "generative AI." It's generating a category or list of categories in response to an input. That's not functionally different than generating a sentence in response to a sentence, it's just an order of magnitude less complex. You can argue terminology but the technology exists on an evolutionary curve, with no real hard boundaries.

  • Paperless and Papermerge have always done a lot more than just OCR. If that's all they were, most people can do that already on the software that comes with their scanner. The core selling point of these applications is automatic categorization, sorting and tagging, and those have always relied on machine learning tools. Literally the first thing you do after setting Paperless up is start training the AI.

  • what's the best alternative?

    You're asking for suggestions for an automatic document categorization tool that doesn't use any automatic document categorization?

  • It reads as especially hysterical in this context, because Paperless is an automatic document categorization system, and I'm really sure what they think the automatic part of that is if it's not "AI" of some broad description. Paperless, Papermerge et al are basically wrappers for machine learning tools and have been for as long as they've existed. LLMs are a natural and obvious fit for the kind of work these applications exist to do.

    This just feels like someone reading "Improved AI pathfinding" in the patch notes for a video game and screaming "OH MY GOD IS NOWHERE SAFE?!"

  • This article is seriously underselling the problem.

    Yes, all of this is accurate, and its good data for people fighting back against data centres. They produce shockingly few jobs, and the most highly paid of those will likely be experts moved in from elsewhere.

    But there's a far bigger problem; AI data centres have no path to profitability. The cost of GPUs is so astronomically high and the depreciation so insanely fast that even if 3,000 - 4,000 data centres worth of AI compute demand materializes (it won't; companies like Meta are already selling their own unused compute because demand is so weak), they still won't be profitable, ever.

    The article talks about how the permanent employment offered - on paper - is in the tens to low hundreds and that's true, but the real employment is mostly going to be zero. In five years time 99% of these projects will be abandoned before completion, or turned into skate parks.

  • Banks was ahead of his time in so many ways. What an absolutely brilliant writer.

  • Jesus dude, you didn't need to sweeten the deal, I was already for it!

  • So now he's in favour of HRT?

  • Hard disagree on that one. I fucking love Dylan.

  • Running Up That Hill covered by Placebo.

    The original is bland eighties pop. The cover is deeply unsettling in all the ways Placebo are so fucking good at.

  • This is the real trap of the Iran war. Trump either has to commit to a deeply unpopular invasion, or he has to be the first US president to openly defy AIPAC.

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  • Sure. And if Christians actually studied the Bible they'd know that. But they don't.

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  • Wouldn't it be "Gosh dang it"? Hank is a good Christian boy, he doesn't take the Lord's name in vain.

  • Reminder that it's now public knowledge that the US had to warn Iran - through back channels - that Israel was going to attempt to assassinate their negotiators in a deliberate attempt to sabotage the peace process. It wasn't a subtle attempt either; Israel sent jets into Iranian airspace to shoot down their plane.

    Israel is openly working against the US' interests. They're basically an enemy nation at this point. And y'all want to hand them more resources and access to all your secrets. OK, sure, good luck with that.

  • Yeah, I'm trying to get better at that. More exercise, more stretching, more vegetables in my diet. It's a process, but I'm starting to feel the difference.

  • Take a walk. Even if it's just on a treadmill. Even if it's just up and down the stairs a few times. There are empirical studies on this; the physical act of walking energizes your brain. Doesn't have to be outside, doesn't have to be going anywhere. Just walk. Five to ten minutes, put a podcast on or something. Do that every hour or two.