> Claiming that it's "malpractice" to avoid it is a plainly inflammatory claim

Well, I'll certainly accept the critique on that part, lol

I think that the current frontier is the completion of tasks that require large chunks of a codebase as context/knowledge.

I think one major area where it's useful is that it "knows" how to use sufficiently common libraries in your language and is simply a superior form of documentation for a lot of stuff.

For example, I wanted to do stuff with PDFs in Python, both ingesting PDFs and modifying them. For the latter functionality, I asked GPT-4 what my options were for modifying PDFs in Python in xyz manner; it gave some options, I went with ReportLab; I asked it how specifically to do what I wanted in ReportLab, telling it to fill in some half-baked function I copied and pasted in. It filled it in, gave me the imports, etc. This all took about 5-10m. There is no way it would have been efficient for me to research the original question, and no way I could have derived the same functionality from ReportLab's documentation as quickly.

I use it a lot as a replacement for "documentation" in that sense; for language-level libs, for stuff like Docker (whose docs are pretty bad). It's just a waste of time, IMO, to read people's (usually) poorly written documentation or a SO thread when the LLM can just cogently distill the information for you AND answer your specific questions about the issue.

Similarly, it's been pretty invaluable for me in learning Haskell. Syntax and other errors in Haskell provided through VSCode Haskell extension via GHC are comically bad, IMO, often amounting to a small red squiggly that's not even where the actual error is, with the message "parse error". When I have put the code snippets into GPT-4, it has identified and corrected the issue 99% of the time, and I can ask follow up questions of my own and get explanations. It's like having a senior Haskell dev at my beck and call to badger, and I think that's the general vibe of my usage.

I consider myself a pretty competent mid-level dev who can ask good questions and has a reasonable estimation of what he doesn't know, and I think that's a good Goldilocks position to make good use of the tool.

Accordingly, I think if people who are working with the same stuff in the same codebase who have themselves become relatively expert in what they need to know won't find LLMs as useful here, but if you're in a situation where you have to get familiar with a lot of new stuff, they are pretty incomparable.

> If you identify the part that is broken (which most linters will do for you) and paste that into Google, you will get what you want roughly as often as ChatGPT furnishes a proper answer.

Well most of the time, I don't need to identify the part that is broken or devote any mental energy to combing through Google search results. I just copy and paste errors into GPT-4, often without any context, and it tells me what's wrong, and I take the fix and do it, only stopping to comprehend what actually went wrong if I judge that it's worthwhile.

The UX of traditional internet search, with its multiple results of unguaranteed quality, is, I think, kind of obsolete now in many contexts. I have questions to which I want answers. I have patience for reading actual long-form content, such as good dev blog posts, books on stuff like Docker, cloud providers, "how to build an interpreter", etc. What I do not have patience for is combing a bunch of random internet Q&A stuff that is about an error that might not even be the exact error I have.