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725
Joined
2 yr. ago

  • The main difference is that crypto was/is burning huge amounts of energy to run a distributed ponzi scheme. LLMs are at least using energy to create a useful tool (even if there is discussion over how useful they are).

  • yup,just even the word "sideloading" seems like its been custom created to sound shifty and sidewise when all it means is installing something. People would look at you weirdly if told them "sideloading" Photoshop on to your PC was dangerous, but somehow its accepted for phones.

  • The point you have to remember is that it is trained on bulk data out there in a very inefficient manner, it needs to see thousands of examples in order to start getting any sort of understanding of something. If you ask it "how do I do {common task} in {popular language}" you will generally get excellent results, but the further you stray from that the more likely to be error prone it is.

    Still it is often good to get you looking on the right track when you are unsure to start, and is fantastic for learning a new language. I've been using it extensively in learning C# where I know what I want to code but not exactly how to use existing features to do it.

  • Yup, as long as you are aware that it could be wrong and look at it critically LLMs at GPT scale are very useful tools. The best way I've heard it described is having a lightning fast intern who often gets things wrong but will always give it a go.

    So long as you're calibrated to "how might this be wrong" when looking at the results it is exceptionally useful.

  • Infrastructure needs central backing, that's not exactly controversial. Would you rather each individual company has to fund its own chargers and end up with a patchwork that only works for each brand individually (and probably change over time to not support older models). Thats how the railways initially developed in 19th century Britain and it was a horrible mess of privately owned incompatible gauges.