This is really fascinating, thanks for sharing. I'm also working on generating natural language representations of Python packages. My approach is:

1. Extract a call graph from the package, where each node is a function and two nodes are connected if one contains a call to the other.
2. Generate natural language summaries of each function by convolving over the graph. This involves generating summaries of the terminal nodes (i.e. functions with no dependencies), then passing those summaries to their dependents to generate summaries, and so on. Very similar to how message passing works in a GNN. The idea here is that summarizing what a function does isn't possible without summaries of what its dependencies do.
3. Summaries of each function within a file are chained to generate a summary of that file.
4. Summaries of each file within a directory are chained to generate a summary of that directory, and so on until the root directory is reached.

I'd love to learn more about the differences/advantages of your approach compared to something like this. Thanks again for your contribution, this is insanely cool!

Try it out here: https://chatoverflow.ai

Github repository: https://github.com/shobrook/ChatOverflow

Yep! Try it out here: https://useadrenaline.com/

I haven’t used this yet, but my understanding is it can explain code and generate unit tests. It can’t explain and fix errors.

You can use it here: https://useadrenaline.com

What could go wrong?

I didn't. Adrenaline won’t always correctly fix your error, but it can at least give you a starting point.

Thanks! Feel free to try it out here. Let me know if any of y’all get some impressive bug fixes.

Yeah, right now it’s just a thin wrapper around GPT-3, but there’s a lot that could be done to improve it, like using static code analysis to build a better prompt or even training a more specialized model (like this).

Right now, this is just a simple demo of what’s possible with AI-driven debugging. But I’d like to build it out so that instead of just explaining errors, Adrenaline provided a ChatGPT-style assistant that can answer questions about your error, and teach you during the debugging process.

This is open-source, so if anyone’s interested in contributing, here’s the GitHub repository: https://github.com/shobrook/adrenaline

Try it out here: https://useadrenaline.com

This is unrelated. StackExplain doesn’t find and fix bugs, it explains error messages.

What mistakes were you talking about then?

No, it’s also proposing a fix for the error.

In my experience, it has explained every error I’ve encountered in a way that’s at least directionally correct. Can you post a counterexample?

Working on something better than an extension. Coming soon.

It's not too wordy if you're a beginner.

I noticed that. Generally it’s “right enough” to help you fix your error, though.

Github repo: https://github.com/shobrook/stackexplain

I made this algorithm to "fill the gaps" in point clouds with synthetic data points to increase the density of the cloud. I figured this would be useful for reducing overfitting in machine learning models trained on point cloud data, or otherwise just enriching sparse point cloud datasets.

Please let me know what y'all think! Here's the Github repository.

Not sure I understand what you mean by depth.