whisper-writer VS easy-chat

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!

You can ask GPT for new features, improvements, and bug fixes and aider will directly apply the changes to your source files. Each change is automatically committed to git with a sensible commit message. These frequent, automatic commits provide a comforting safety net. You can confidently collaborate with aider, because it's easy to use git to undo missteps or manage a long series of changes.

You can find out more about aider on GitHub: https://github.com/paul-gauthier/aider

I was initially using GPT to generate code snippets with the OpenAI web chat UI and generic ChatGPT command line tools like `aichat`. But that involved a somewhat klunky workflow where I had to cut and paste code into ChatGPT and then back into my source files.

I streamlined my process while developing a children's chat interface called EasyChat (https://github.com/paul-gauthier/easy-chat). I adopted a "whole file in, whole file out" workflow. I would send GPT-3.5 the entire source code of my project along with a change request and had it reply with the modified version of all the code. This approach was way less tedious than cutting and pasting code between the chat and my source files. I had some simple command line tooling to feed source files to GPT, overwrite them with GPT's modified version and display diffs. This workflow was also quite reliable: GPT-3.5 could consistently produce the code changes I requested without getting lost or confused. But it was slow waiting for GPT to retype all the code, and I quickly hit context window limits asking GPT to read and rewrite every line of the entire codebase.

Access to the GPT-4 API really unlocked a lot of possibilities for improving my tooling. GPT-4 is much better than GPT-3.5 at following directions and replying in a stable, parsable format. Aider still sends GPT-4 entire source files, but asks for replies in a concise `diff` like format. Aider automatically applies these diffs to the source files and git commits them with a GPT generated commit message. Aider lets you easily manage which of your source files are "in the chat session" to control how much code you send to GPT-4 with each request. The ability to reply with diffs makes it much less likely to overflow GPT-4's larger context window.

The resulting workflow is quite effective. You can bounce back and forth between the aider chat and your editor to collaborate on code changes. Aider's code changes aren't always perfect, but wow they are great for blasting through boilerplate or quickly integrating unfamiliar libraries or packages into your code. And if you don't like a code edit, you can quickly discard it by typing `/undo` into the chat.

I now use aider as a force multiplier for a lot of my coding. I even use aider to improve the tool itself.

Let me know if you try aider and find it helpful.

I had chat gpt 3.5 build a small web app for me too. I have since been building some tooling for this sort of GPT-assisted programming.

https://github.com/paul-gauthier/easy-chat

GPT is significantly better at modifying code when following this "all code in, all code out" pattern. This pattern has downsides: you can quickly exhaust the context window, it's slow waiting for GPT to re-type your code (most of which it hasn't modified) and of course you're running up token costs. But the ability of GPT to understand and execute high level changes to the code is far superior with this approach.

I have tried quite a large number of alternative workflows. Outside the "all code in/out" pattern, GPT gets confused, makes mistakes, implements the requested change in different ways in different sections of the code, or just plain fails.

If you're asking for self contained modifications to a single function, that's all the code that needs to go in/out. On the other side of the spectrum, I had GPT build an entire small webapp using this pattern by repeatedly feeding it all the html/css/js along with a series of feature requests. Many feature requests required coordinated changes across html/css/js.

https://github.com/paul-gauthier/easy-chat#created-by-chatgp...

Another HN user has also released a command line tool along these lines called gish:

https://github.com/drorm/gish

Gish looks really nice. I'm going to give it a try.

It seems like you've been using similar workflows to what I've been trying for coding with gpt?

https://github.com/paul-gauthier/easy-chat#created-by-chatgp...