Correct, but it's the same as what OP shared.

You should use Aider/Cursor for proper indexing/intelligent codebase referencing

not sure if it's cursor's fault, but very often it doesn't give me the real or complete code of my codebase when auto editing/auto completing.

any tips?

I'm still puzzled how come people are convinced by Cursor, while my experience was meh at best. Can it index your stuff? okay it can. Can it refactor a simple function? No it cannot, it can't even rename a damn Java class. How can I trust it to generate then code based on my codebase? So, what is your use case then? Or can anybody point me to some blog/articles/videos showing some real use cases for Cursor? Real as in, something that it provenly can do?

Correct. It's Web 3.0 2.0. You're supposed to play along to make the stock prices go up and to the right.

I think you know the correct answer:)

I hoped to be wrong but no comment so far even tried to bring a based argument... eh, maybe I'll try again in a year.

>Java

found the problem

>Can it refactor a simple function?

Certainly, I do that several times a day.

Listen, I don't want to brag too much, but it even made me a function today.

Cramming thousands of tokens of potentially irrelevant context through unclear indexing paths isn't "proper".

The best results come from feeding precisely targeted context directly into the prompt, where you know exactly what the model sees and how it processes it. The prompt receives the most accurate use of attention—whereas god knows what the pipeline is for cursor or what extra layers and context restrictions they add on top of base Claude.

Giving the model a clean project hierarchy accomplishes a lot efficiently in terms of context tokens. The key is ensuring it only sees what's relevant, without diluting its attention.

Tools like reopmix and OP's version, feeding targeted context straight into models like Claude or Google's offerings, outperform Copilot and Cursor in my experience, even though they use the same base models. Use the highest-quality attention (the prompt context) directly, rather than layers of uncertainty and "proper indexing".