I kinda agree with the author — as a person with more than enough coding experience I don't get much value (and, certainly, much enjoyment) from using AI to write code for me. However it's invaluable when you're operating in even a slightly unfamiliar environment — essentially, by providing (usually incorrect or incomplete) examples of the code that can be used to solve the problem it allows to overcome the main "energy barrier" for me — helping to navigate e.g. the vast standard library of a new programming language, or provide idiomatic examples of how to do things. I usually know _what_ I want to do, but I don't know exactly the syntax to express it in a certain framework or language
Yeah, I don't leverage LLMs much, but I have used it to look up APIs for writing vscode extensions. The code wasn't usable as-is, but it gave me an example that I could turn into working code - without looking up all of the individual api calls.
I've also used it in the past to look up windows api, since I haven't coded for windows in decades. (For the equivalent of pipe, fork, exec.) The generated code had a resource leak, which I recognized, but it was enough to get me going. I suspect stack overflow also had the answer to that one.
And for fun, I've had copilot generate a monad implementation for a parser type in my own made-up language (similar to Idris/Agda), and it got fairly close.
There's a product called Context7 which among other things provides succinct examples of how to use an API in practice (example of what it does: https://context7.com/tailwindlabs/tailwindcss.com )
It's supposed to be consumed by LLMs to help prepare them to provide better examples - maybe a newer version of a library than is in the model's training data for example.
I've often thought rather than an MCP server of this that my LLM agent can query, maybe i just want to query this high signal to noise resource myself rather than trawl the documentation.
What additional value does an LLM provide when a good documentation resource exists?
