Post #1494430

@KatS look @miki don't get me wrong but any time i've tried using LLMs for my work, which isn't just some fun side project but actual production-running code, LLMs have been way too unreliable. It also resulted in me knowing jack shit about my own code, which is poison for long term maintainability. Since these models are just statistically determining the next most likely token based on training data and fine tuning, without any actual understanding or thought behind it, I seriously can't see this tech being reliable enough one day. (reliable compared to humans, i don't seek 100% reliable in this case, natural language is too imprecise for that anyways. i would expect "good enough" as "as good as a professional in the given field") The other part of the equation is the amount of compute and electrical energy necessary to train and operate such a level, and on that level, there's no way in hell that shit is ever gonna be worth it, financially and environmentally. i'm not expecting the "make job for phone operators easier", i expect the "when i dial a number, it should be at least as reliable and efficient at routing it correctly as a phone operator would be". you can call me whatever you want, even llm denier if you need to, but autocorrect on steroids isn't worth exploiting other people's work or boiling our oceans.