Comment by shepherdjerred

For personal projects, I have found it to be transformative. I've always struggled with perfection and doing the "boring parts". AI has allowed me to add lots of little nice-to-have features and focus less on the code.

I'm lucky enough that my workplace also uses Cursor + Claude Code, so my experience directly transfers. I most often use Cursor for day-to-day work. Claude has been great as a research assistant when analyzing how data flows between multiple repos. As an example I'm writing a design doc for a new feature and Claude has been helping me with the investigation. My workflow is more or less to say: "here are my repos, here is the DB schema, here are previous design docs, now how does system X work, what would happen if I did Y, etc."

AI is still fallible so you _do_ of course have to do lots of checking and validation which can be boring, but much easier if you add a prompt like "support every claim you make with a concrete reference".

When it comes to implementation, I generally give it smaller, more concrete pieces to work with. e.g. for a personal project I would say something like "here is everything I want to do, make a plan, do part 1, then do part 2, example: https://github.com/shepherdjerred/scout-for-lol/tree/227e784...)

At work, I tend to give it PR-sized units of work. e.g. something very well-scoped and defined. My workflow is: prompt, make a PR on GitHub, add comments on GitHub, tell Cursor "I left comments on your PR, address them", repeat. Essentially I treat AI as a coworker submitting code to me.

I don't really know that I can quantify the productive gain.. I can say that I am _much_ more motivated in the last few months because AI removes so much friction. I think it's backed up by my commit history since June/July which is when I started using Cursor heavily: https://github.com/shepherdjerred