Comment on Might not be efficient, but at least it... Uhhh, wait, what good does it provide again?

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fonix232@fedia.io ⁨17⁩ ⁨hours⁩ ago

"See, no matter how much I'm trying to force this sewing machine to be a racecar, it just can't do it, it's a piece of shit"

Just because there are similarities, if you misuse LLMs, they won't perform well. You have to treat it as a tool, with a specific purpose. In case of LLMs that purpose is to take a bunch of input tokens, analyse them, and output the most likely output tokens that is statistically the "best response". The intelligence is putting that together, not "understanding tic tac toe". Mind you, you can tie in other ML frameworks for specific tasks that are better suited for those -e.g. you can hook up a chess engine (or tic tac toe engine), and that will beat you every single time.

Or an even better example... Instead of asking the LLM to play tic-tac-toe with you, ask it to write a Bash/Python/JavaScript tic-tac-toe game, and try playing against that. You'll be surprised.

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