get a lot of questions on how to use @cursor_ai in large codebases and monorepos. while it depends™, here are some tips and tricks enable project structure to make the model more aware of how the project is structured

i've built 10+ internal tools at @cursor_ai using cursor. when you have a platform in place, it makes it so easy to spin up new tools

here's how ↓ the first piece is to have the primitives in place like database, servers, networking, workers etc

i've setup this on aws with terraform

rolling out @cursor_ai 0.49

here’s what’s new ↓ you can now generate rules from conversation using the command /Generate Cursor Rules

great way to capture decisions from back and forth chats you might want to reuse later

i asked some people at @cursor_ai how they use cursor

some nuggets in here ↓ @shaoruu
- create dedicated "playgrounds" to iterate on the ui/ux or logic with the ai, kinda like a free-roam space until you get something you like, and then integrate it back to your main app

- use @ pr and @ commit to review changes & undo things you don't like

context management in @cursor_ai is key to getting the results you want

here are some of the @-symbols you can use to make sure you include relevant context in your prompt ↓ use @files and @folders to explicitly reference specific pieces of code. great when you know what code you want to include. personally, i always include specific files when i know what i want to change

we've been hard at work improving @cursor_ai Agent, allowing you to delegate more tasks and let it work alongside you

agent works just like a human developer, with access to your tools, codebase context, and the ability to take actions

here's what Agent can do ↓ using tools, Cursor Agent have the capability to reason about when it should
- read & write code
- search your codebase
- call MCP servers
- run terminal commands

an example where it connects to a postgres database, runs a query and writes to file