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To be fair, if you turn on thinking mode on Llms and see the output their is some thinking/reasoning.

A simple example:

Prompt:Make it yellow

Think: the user wants something yellow but hasn't said what it is. Previously the user talked about creating a Button, so it must be the button but I should clarify by asking

Response: is it the button that you want yellow?

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I guess it becomes philosophical, what is thinking? What I’ve notice it does all that but misses the absolute most obvious things or gets incredibly basic things wrong.

this part of LLMs is the pre-programmed human logic. The LLMs arent actually thinking, they're just going through a defined IF THEN loop based on whatever weights, if there is some ambiguity in a prompt the LLM is just programmed to prompt for more info. Its not actually thinking it needs to ask anything, its just coming back with low precision probability.

All of the recent improvements in the LLM's "thinking" have just been layers of programming on top of its statistical models. Which is why its becoming clear LLMs are really not advancing that much.

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