Perhaps I'm just daft or slow, but I'm not 10x-ed by LLM usage. It's precisely because the details matter. (And yes, I read the article and it's lamenting the loss of low level machine-level details.) I read the LLM output, it slows me down. I challenge the LLM output assumptions, that slows me down. I stop to step away and think, it slows me down. I cannot run a bunch of simultaneous prompts and supply this same level of mental context to all of them, because my brain works best when I treat it as single-threaded. My experience with this approach to using an LLM has been, so far, positive.
What I have noticed as I've migrated to newer models is that the assumptions made are incorrect less often. But if I don't pay close attention wrong assumptions can still be made. Cross cutting concerns are lost, or written in such a way as to be useless.
So, the loss of details, to me, would be my own laziness in not performing due diligence.
What I have noticed as I've migrated to newer models is that the assumptions made are incorrect less often. But if I don't pay close attention wrong assumptions can still be made. Cross cutting concerns are lost, or written in such a way as to be useless.
So, the loss of details, to me, would be my own laziness in not performing due diligence.