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Cake day: August 18th, 2025

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  • My understanding (not a fan at all but listened to my friend’s dad’s drug fueled ramble after his surgery during the only game I “watched”) is they both dropped the ball. Red Sox were just not great overall again and white sox got knocked out by the Detroit tigers who had a surprisingly strong year thanks to some guy.












  • I’ll just focus down on one example, but I’ve seen this amount of success with non llm AI elsewhere. Protein folding. I’m not directly in the space but my understanding from friends who are researchers is this has solved massive problems for them. Cutting years of time down to potentially days (it’s still in early adoption so not fully proven. But I trust their judgement).

    Alphafold uses an attention network to determine potential fold patterns and fairly well. This let’s them cut research time and materials as they can very quickly actually predict likely outcomes without having to directly test things in an experimental environment. (Where they go from there I get lost, I’ve helped friends write script to sort data so my knowledge of this area ends at the computer phase)

    Yes you can argue we technically could do it manually before AI, but ultimately you could say that about most any tool. The cotton gin didn’t do anything we couldn’t do before, but it sure changed things with it’s speed and utility.


  • AI has actually made plenty of things easier and is not evil. The problem is AI is so many different things but most people don’t talk about those as different. LLMs are the problem and a type of AI, but they aren’t the only AI.

    AI (in the non llm form) is used in medical research, material science, chemistry, and more and has been for several years (though has exploded recently alongside LLMs).

    AI as a whole is very much on the level of usefulness of an engine or the Internet. LLMs can go die in a fire.