The author starts out with an excellent observation:

  Lately, I've been playing around with LLMs to write code. I find
  that they're great at generating small self-contained snippets.
  Unfortunately, anything more than that requires a human...

https://aider.chat/docs/ctags.html

The problem I am trying to solve is that it’s difficult to use GPT-4 to modify or extend a large, complex pre-existing codebase. To modify such code, GPT needs to understand the dependencies and APIs which interconnect its subsystems. Somehow we need to provide this “code context” to GPT when we ask it to accomplish a coding task. Specifically, we need to:

1. Help GPT understand the overall codebase, so that it can decifer the meaning of code with complex dependencies and generate new code that respects and utilizes existing abstractions.

2. Convey all of this “code context” to GPT in an efficient manner that fits within the 8k-token context window.

To address these issues, I send GPT a concise map of the whole codebase. The map includes all declared variables and functions with call signatures. This "repo map" is built automatically using ctags and enables GPT to better comprehend, navigate and edit code in larger repos.

The writeup linked above goes into more detail, and provides some examples of the actual map that I send to GPT as well as examples of how well it can work.

I've been hearing executives and "tech leaders" recently saying that 80% of the new code is now written by chatgpt, and that it will "10x" a developer, but that sure mismatches with my experience. I suspect there will be a lot of managers with much higher expectations than is reasonable, which won't be good.

It's actually 1.2x productivity. Writing code does not take most of the day. If GPT can be just as good at debugging as it is at writing code, maybe the speedup would increase a bit. The ultimate AI speed == human reading + thinking speed, not AI generation speed.

but not all human thinking is worthwhile. I had it do a simple chrome extension to let me reply inline on HN, and it coughed out a manifest.json that worked first try. I didn't have to poke around the Internet to find a reference and then debug that via stack overflows. Easily saved me half an hour and gave me more mental bandwidth for the futzing with the DOM that I did need to do. (to your point tho, I didn't try feeding the html to it to see if it could do that part for me.)

so it's somewhere between 1.2x and 10x for me, depending on what I'm doing that day. Maybe 3x on a good day?

Also chances are great that the AI just spit out some code from that extension… (Of course without attribution. Which would make it a copyright volition.)