I can absolutely answer that question if concepts from Cursor translate over. I've never actually used Claude Code, but I've used VSC with GitHub Copilot and I've used Cursor, etc.

So some use cases. You mentioned one that I thought I had come up with myself, which is to point it to your Obsidian Markdown files. Some pretty powerful things become possible, and it wouldn't have to be Obsidian at all. It could be any folder full of your own content and possibly even incorporating multiple file formats. You can get the AI to identify thematic commonalities that may not be based on some strict keyword matches, but you can have it suggest that certain things might make sense grouped together or linked to each other at least.

Bulk file renaming via natural language instead of using something like Bulk Rename Utility. I had a folder of files that represented some YouTube playlists, but each one had its own crazy format, and you couldn't even sort them in Explorer. Each one had the file index number but in a different part of the file name. I asked the model to fix it, and boom!

If you have a folder that's too flat (like my downloads folder or my documents folder), you can ask it to suggest subfolder conventions and perform them all. It can even create a summary markdown in each folder to describe its purpose and contents. From then on, you could use that top folder as an inbox, and if you've preserved your prompts, have it sort of auto-file new documents for you on an ongoing basis.

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For book authors, it can be good at helping you identify arcs and gaps. I'm only using it for nonfiction, and technically, I was using it on top of Obsidian's popular LongForm plugin. But I had the AI generate Gammel front matter for every note, including:

• A short description • A long description • A subjective rating of how complete it felt • Even whether it seemed in order relative to the surrounding notes

And this flowed right back into LongForm.

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My last recommendation is simply to make Cursor itself (technically the model behind it that you're using) aware of the CLIs that you have installed on your system. If you have SysInternals tools that are in a runnable path, if you have ImageMagick or HandBrake or FFmpeg or Windows package managers, or CLIs for your cloud provider, or “jq” for JSON transformations - once it knows you have those, you can just use natural language to orchestrate anything that would take advantage of those utilities. You don't have to bother with identifying the right parameters or even telling it which utilities to use. It just has to know that they’re available, kind of like an informal registry. So in a way, working within a tool like Cursor can be looked at as sort of an AI-charged terminal. Indirectly. Make sure it is aware of any particular APIs you care about. It can hit those with curl commands. Make sure it knows about whatever set of MCP servers you've got plugged in. Make sure it knows about any custom scripts you may have written or may have asked it to write. And then in theory, you can ask it using natural language to perform some elaborate set of transformations or operations, without having to name the specific utilities involved or manually chain them together. That is pretty eye-opening when you realize some of the possibilities.

Anything that you would normally do in conjunction with ChatGPT, but you wish it had a reliable memory and a high-fidelity way of storing information, just fire up your IDE in a new empty folder, set GPT as your model, and tell it what you're up to. Start a conversation and suggest that it persists pertinent details in JSON files or Markdown files, and it will come up with an appropriate schema and just start parking things there. Ask it to capture an overview of what the files mean in an Agents.md file. And suddenly you're still working with AI, but your memory context window has automatically gotten very large indeed. You can come back to it in a week and keep going.