You mentioned using Claude to help set up a GitHub Action for reviewing Rails migrations. How do you see agentic tools like Claude evolving in their ability to reason about big-picture concerns—not just boilerplate generation, but things like validating database changes, architectural decisions, or spotting long-term risks that aren’t immediately visible?

They are really good if you give them guidance and tight scope. For example for the database migrations review bot, we give it our Cursor rules file on database migrations (which is about 200 lines) and tell it to review the PR based on that.

It works particularly well for migrations because all the context is in the PR. We haven't had as much luck with reviewing general PRs where the reason for a change being good or bad could be outside the diff, and where there aren't as clearly defined rules for what should be avoided.

How do you square this with the fact that LLM-based tools aren’t actually doing any analysis whatsoever, but are just pachinko machines that produce statistically likely output tokens when given input tokens?

Have you noticed people more productive, in general, using Claude Code or Cursor? Obviously it varies, but curious if one or the other is the clear productivity champion at this point.

I wouldn't say there's an obvious winner yet. I do think it depends on the person and how they like to work / how they reason about problems.

For Claude specifically, people who take more time to write long detailed prompts tend to get much better outcomes. Including me, since I made the effort to get better at prompt writing.