I’m currently slapping together something like this, though the highlighting of code is automated – in a sense – when an error is piped into the program and the

I’m currently slapping together something like this, though the highlighting of code is automated – in a sense – when an error is piped into the program and the error stack is used as a prompt to find a solution to the error. The goal is to splice a solution into a file on a new branch, write a test to ensure it resolves the issue (if necessary), then create a pull request describing the change set and how the test addresses the issue. I’ve so far worked through it manually and it works okay, but for fairly limited scopes. Like, why didn’t this environmental variable load properly? Why couldn’t we parse this input? Why did this time out? No big picture stuff. I have a feeling GitHub will be adding this directly to repositories so initial solutions and ideas (perhaps even fixes) can be generated by an AI trained on your repo and its issues/PRs/dependencies. I’m not doing this with aspirations of building a successful tool so much as learning to think with AI as a tool for improving systems. It’s a very tractable thing at the moment and I think future iterations of GPT will make it truly useful.