AmericanEconomicThinkTank

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Cake day: September 22nd, 2025

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  • If that’s what you’re facing, maybe try thinking of it like you’re trying to get to know them a little. If you start talking about say, your recent effort to make sambal oelek for use in say noodles, asking if they’re familiar with the name, talk about how it has some origin in French cooking from the colonial period, add some info about how you like to use it etc.

    Like if I’m ordering food, I’ll always go for the full pronunciation, then maybe follow up with the server or whoever asking if it’s the dish with this or that main focus.

    I also love having fun with themed dinner parties with friends and the such, give full presentation on the pronunciation, it’s history in different areas that make it slightly different, give everyone a copy of the recipe (minus a few key but subtle ingredients lol) in both the original language and English.

    All in all I say just try to make it a chance to be enjoyed one way or another and it doesn’t take long to get your confidence flowing lol. I totally empathize, I used to be absolutely mortified about the same thing, still do sometimes, just less so these days.








  • Oh the ag. collapse could be, at the very least, interesting.

    Bad enough that the lions share of the industry will need major handouts, or more likely that farm after farm will be bought out for land-lease to former owners, and then given handouts to offset purchase price. But, the midwest corporate cash crop farms have been fighting tooth and nail against soil conservation methods just to squeeze a few extra bucks out.

    I so hope we don’t end up getting into another dust bowl.

    I’d highly recommend folks look at keeping up a community garden or two if possible, or helping out at one if not.








  • Nope, language models by inherent nature, xannot be used to calculate. Sure theoretically you could have input parsed, with proper training, to find specific variables, input those to a database and have that data mathematically transformed back into language data.

    No LLMs do actual math, they only produce the most likely output to a given input based on trained data. If I input: What is 1 plus 1?

    Then given the model, most likely has trained repetition on an answer to follow that being 1 + 1 = 2, that will be the output. If it was trained on data that was 1 + 1 = 5, then that would be the output.



  • Might be focusing a bit too much into specific demographics here, work towards the message, impact, and readability first and foremost. That’s where the audience draw should be, it’s what will keep them reading. Sure, relatability with age can help draw some, but if you remove those small details then no impact should happen to the overall story.

    Have you tried going for more of an indirect approach, say focusing on mannerisms, behavioral traits and the such to demonstrate a difference in age?