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I find Karpathy's focus on tightening the feedback loop between LLMs and humans interesting, because I've found I am the happiest when I extend the loop instead.

When I have tried to "pair program" with an LLM, I have found it incredibly tedious, and not that useful. The insights it gives me are not that great if I'm optimising for response speed, and it just frustrates me rather than letting me go faster. Worse, often my brain just turns off while waiting for the LLM to respond.

OTOH, when I work in a more async fashion, it feels freeing to just pass a problem to the AI. Then, I can stop thinking about it and work on something else. Later, I can come back to find the AI results, and I can proceed to adjust the prompt and re-generate, to slightly modify what the LLM produced, or sometimes to just accept its changes verbatim. I really like this process.






I would venture that 'tightening the feedback loop' isn't necessarily 'increasing the number of back and forth prompts'- and what you're saying you want is ultimately his argument. i.e. if integral enough it can almost guess what you're going to say next...

I specifically do not want AI as an auto-correct, doing auto-predictions while I am typing. I find this interrupts my thinking process, and I've never been bottlenecked by typing speed anyway.

I want AI as a "co-worker" providing an alternative perspective or implementing my specific instructions, and potentially filling in gaps I didn't think about in my prompt.


Yeah I am currently enjoying giving the LLM relatively small chunks of code to write and then asking it to write accompanying tests. While I focus on testing the product myself. I then don't even bother to read the code it's written most of the time



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