Not OP, but a very simple example: I use AI to review my work before opening a PR for my colleagues to review. I ask it to review the commits in my branch. Instead of consuming tokens just to instruct it how to use git operations and other tools to find the commits since the base commit, I asked AI to create a little bash script to make patch files commit1.patch, commit2.patch, commit3.patch, etc, for all the commits in my branch since the base commit. Now I just use this script to prepare the context of commits to review.

I feel like an imposter here, I’m definitely not using AI as much as it seems everyone is :( I can’t imagine using hundreds of dollars of tokens a day. But maybe this little tip for reviews might be helpful to someone.

Not op, made a tool to convert Microsoft OneNote notes to Obsidian canvas and Markdown. First it used a python lib which was too limiting. Then it used windows API to plug into OneNote and read the doc in its original XML form. That made the conversion correct and fully featured.

Not OP, but I've been focusing on linting and automation.

Custom lint rules to encode best practices that previously relied on astute/alert code reviewer to call attention to. This is handy not just for humans but it steers the bots too. Or turning on some existing rule that required a big cleanup/migration to be compliant with. Now I just throw an LLM at it, since they're often laborious but mechanical changes. Which is the sweet spot for an LLM.

Also automating everything I can. That annoying release process that everyone hates but wasn't quite long/arduous enough to justify the time before? It's now automated. GitHub workflows for all the things.

This kind of stuff will forever be useful, even if the bottom drops out and the bubble bursts. And none of it is reliant on AI to run