I've been building and using these tools for well more than a year now, so here's my journey on building and using them (ORDER BY DESC datetime).

(1) My view now (Nov 2024) is that code building is very conversational and iterative. You need to be able to tweak aspects of generated code by talking to the LLM. For example: "Can you use a params object instead of individual parameters in addToCart?". You also need the ability to sync generated code into your project, run it, and pipe any errors back into the model for refinement. So basically, a very incremental approach to writing it.

For this I made a Chrome plugin, which allowed ChatGPT and Claude to edit source code (using Chrome's File System APIs). You can see a video here: https://www.youtube.com/watch?v=HHzqlI6LLp8

The code is here, but its WIP and for very early users; so please don't give negative reviews yet: https://github.com/codespin-ai/codespin-chrome-extension

(2) Earlier this year, I thought I should build a VS Code plugin. It actually works quite well, allows you to edit code without leaving VSCode. It does stuff like adding dependencies, model selection, prompt histories, sharing git diffs etc. Towards the end, I was convinced that edits need to be conversations, and hence I don't use it as much these days.

Link: https://github.com/codespin-ai/codespin-vscode-extension

(3) Prior to that (2023), I built this same thing in CLI. The idea was that you'd include prompt files in your project, and say something like `my-magical-tool gen prompt.md`. Code would be mostly written as markdown prompt files, and almost never edited directly. In the end, I felt that some form of IDE integration is required - which led to the VSCode extension above.

Link: https://github.com/codespin-ai/codespin

All of these tools were primarily built with AI. So these are not hypotheticals. In addition, I've built half a dozen projects with it; some of it code running in production and hobby stuff like webjsx.org.

Basically, my takeaway is this: code editing is conversational. You need to design a project to be AI-friendly, which means smaller, modular code which can be easily understood by LLMs. Also, my way of using AI is not auto-complete based; I prefer generating from higher level inputs spanning multiple files.