Its great that cursor is working for you. I do think LLMs in general are far far better on Typescript and Python compared to other languages (reflects from the training data)
What features of cursor were the most compelling to you? I know their autocomplete experience is elite but wondering if there are other features which you use often!
Their autocomplete experience is decent, but I've gotten the most value out of Cursor's "chat + codebase context" (no idea what it's called). The feature where you feed it the entire codebase as part of the context, and let Cursor suggest changes to any parts of the codebase.
ohh inserting.. I tried it on couple of big repos and it was a bit of a miss to me. How large are the codebases on which you work? I want to get a sense check on where the behavior detoriates with embedding + gpt3.5 based reranker search (not sure if they are doing more now!)
that's a good metric to aim for... creating a full local index for 600k lines is pretty expensive but there are a bunch of huristics which can take us pretty far
- looking at git commits
- making use of recently accesses files
- keyword search
If I set these constraints and allow for maybe around 2 LLM round trips we can get pretty far in terms of performance.
What features of cursor were the most compelling to you? I know their autocomplete experience is elite but wondering if there are other features which you use often!