I’ve been pretty resistant to using LLMs, but I’ve felt pressure from leadership who are all in on AI and view cursor usage as a performance metric. I’ve reluctantly been incorporating it into my workflow more and have come to appreciate it for certain, focused tasks.
The best use I’ve found is as an additional reviewer on merge requests. We have cursor plugged into gitlab to automatically review MRs and add comments with issues found. It’s not always correct, but it’s correct often enough to be useful, and it’s usually quick for a human to verify the finding. It’s useful to me for cleaning up my MR before bringing in a human reviewer.
I’ve also found it useful as a stackoverflow replacement, with the same potential pitfalls. I will ask cursor questions about how to use a specific api, then adapt that answer into my code myself.
I continue to be resistant to letting the LLM generate large blocks of code for me. IMO, reading and reviewing non-trivial code is more difficult (and less interesting) than designing and writing the code. Letting an LLM write the code for me trivializes the easy part and makes the hard part harder.