AmericanEconomicThinkTank

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  • 39 Comments
Joined 8 days ago
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Cake day: September 22nd, 2025

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  • 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.









  • I could give you some indepth economic analysis, and behavior philosophy behind business spending, but the easiest?

    Just unionize. Now. Volunteer time to talk outside work hours. Speak to local professionals like accountants and lawyers to help pro-bono with getting setup.

    Talk to political offices locally, run if you can.

    Talk about these issues with strangers. Print out your own corny pamphlet newspaper to educate someone willing to read.



  • To a good point yea, it’s experience based, it’s why the top schools are already exposing the topic of disinformation and media literacy to younger generations. Trust but verify is an excellent mantra, take time to properly think through and challenge new information you encounter, keep a change of pace to stay mentally fresh, destressing yourself when possible all work together to keep a healthy learning mindset.

    It’s essentially a holistic approach to learning and processing.

    Unfortunately, geopolitical interests, personal ideology, and everything in-between will make true online objectivity nearly impossible, so learning to best navigate it is pretty much the only approach for now. Besides keeping offline as much as possible.




  • 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.