Using ChatGPT to generate a GPT project end-to-end

I've found the best way to pair program with ChatGPT is with GPT4 API through a VSCode extension by @jakear [1] that uses the Notebook interface. Instead of setting a language for each cell, you set roles like "system", "user", or "assistant" and when you run a cell it sends the cells as chat messages.

A huge benefit of this format is that you can delete cells, edit the responses from GPT4 to incorporate changes from future queries, and even rearrange or add mock assistant messages to prime the conversation. As ChatGPT suggests changes, I incorporate them into the main code cells and replace the old queries/feedback with new queries feedback. Since the old changes are incorporated into the parent cells, it loses track a lot less and I can also touch it up to use the right file paths, APIs, etc when it messes up.

You can go a step further and monitor the llm file with inotify and extract assistant messages, infer the file path from the responses, and automatically write them to file as you update the notebook. That eliminates the back and forth copy pasting.

It'd be nice to extend that interface to include Jupyter notebook cells so we can use ChatGPT to generate notebook cells that can be parsed and executed in the interface directly.

Edit to add another tip: I use a variation of the below system prompt for working on larger sessions. Each user message begins with a file path and contains a code block with the contents of the file. After each user message containing a file, I manually add an assistant message that just says "continue", which allows adding several files at different paths. The last user message, the one I actually execute, contains the tokens and the description of the modifications I want in the code. I incorporate the suggested changes into the messages then rinse and repeat. Prompt (sadly I forgot to record where I found it):

    You are a Rust AI programming assistant. The user will send you the relevant code over several requests. Please reply "continue" until you receive a message from the user starting with the tokens "". Upon receiving a message from the user starting with the tokens "" please carry out the request with reference to the code that the user previously sent. Assume the user is a senior software engineer who needs minimal instruction. Limit your commentary as much as possible. Under ideal circumstances, your response should just be code with no commentary. In some cases, commentary may be necessary: for example, to correct a faulty assumption of the user or to indicate into which file the code should be placed.

[1] https://news.ycombinator.com/item?id=35605159

So what if you don't "just put it into a loop and hope" but actually make a complex AI agent with static code analysis capabilities, a graph DB, a work memory etc?

I'm doing just that and it works surprisingly well. Currently it's as good as people with 2-3 years of experience. Do you really believe it's not going to improve?

Now I'm making a virtual webcam so it has a face and you can talk to it on a Zoom meeting...

I've also made six small apps completely coded by ChatGPT (with GitHub Copilot contributing a bit as well). Here are the two largest:

PlaylistGPT (https://github.com/savbell/playlist-gpt): A fun little web app that allows you to ask questions about your Spotify playlists and receive answers from Python code generated by OpenAI's models. I even added a feature where if the code written by GPT runs into errors, it can send the code and the error back to the model and ask it to fix it. It actually can debug itself quite often! One of the most impressive things for me was how it was able to model the UI after the Spotify app with little more than me asking it to do exactly that.

WhisperWriter (https://github.com/savbell/whisper-writer): A small speech-to-text app that uses OpenAI's Whisper API to auto-transcribe recordings from a user's microphone. It waits for a keyboard shortcut to be pressed, then records from the user's microphone until it detects a pause in their speech, and then types out the Whisper transcription to the active window. It only took me two hours to get a working prototype up and running, with additions such as graphic indicators taking a few more hours to implement.

I created the first for fun and the second to help me overcome a disability that impacts my ability to use a keyboard. I now use WhisperWriter literally every day (I'm even typing part of this comment with it), and I used it to prompt ChatGPT to write the code for a few additional personal projects that improve my quality-of-life in small ways. If people are interested, I may write up more about the prompting and pair programming process, since I definitely learned a lot as I worked through these, including some similar lessons to the article!

Personally, I am super excited about the possibilities these AI technologies open up for people like me, who may be facing small challenges that could be easily solved with a tiny app written in a few hours tailored specifically to their problem. I had been struggling to use my desktop computer because the Windows Dictation tool was very broken for me, but now I feel like I can use it to my full capacity again because I can type with WhisperWriter. Coding now takes a minimal amount of keyboard use thanks to these AI coding assistants -- and I am super grateful for that!

[dead]

Spot on, right! Glad you achieved all of the above. By design, tech advances to enhance human's ability to create. In your case, the AI tech (LLMs) truly augment your own capabilities. Therefore reaching the comfort that others enjoy freely. Hope to see more use cases, like yours, brought forward to inspire some anxious humans who are terrified by the rapid advancement of AI tech.