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This past year or so I've had ChatGPT open a lot. It's been super useful as a explore a "new" [1] field in a lot more depth.

Interestingly though I don't get it to write code. It's no good at the language I write in, so useless there.

As a "tutor" though it's been really useful. I'm asking a lot of (probably simple) questions, and the answers are "right enough". Occasionally I'm not sure why something is failing, and it's usually helpful there too.

So, less brain-rot and more "helpful senior who helps me along".

[1] the work I'm doing is related to SQL, which I've used here and there before, but not to the depth or degree I am now. I don't need it to write SQL, but rather to answer more general questions and to compare SQL databases, discuss effeciency and so on.



But that there is the difference. You are using it to put knowledge in your brain, whereas what I'm seeing most people do is entirely skip that part, and just being a sort of clipboard-like vessel that takes information from ChatGPT and puts it into a code editor, with no intermediate thought or analysis - meaning that the brain also stores none of it, because it never actually really thought about any of it.

And if there is any thinking involved, it's not in trying to figure out the code, but trying to figure out why the AI can't figure out the code. It's a subtle difference, but results in a huge change in quality.


I use them as "research assistants" when google or the documentation fails me. But I always treat it as a less reliable stackoverflow - there's no guarantee what I'm reading is correct and rarely includes caveats like "doesn't work before version X" or "must have Y flag enabled."

I've particularly enjoyed converting terse documentation into a .md file and feeding into the LLMs context window, then using the LLM to "query" the underlying document.

Where it's always fallen short for me is code generation, and frankly that feature just doesn't interest me.




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