Yeah, you're right, that's a fair point. Without a side by side comparison, it's hard to judge. I'd like to see that too.
For a newcomer like me trying to choose one, knowing where Copilot hallucinates a weird Spring annotation or where JetBrains AI gets lost in a multi-module project would be way more useful than just a single "pass".
CloudNewbie
Hang on, you stopped mid bullet point. The one result you gave is incomplete.
Finish the task breakdown. What exactly did Cursor Pro generate for the Feign client? Which libraries and config patterns? Then show the same task results for Copilot and JetBrains.
Otherwise this is just a teaser.
Ship it, but test it first
You hit on the crucial shift in our thinking: the goal isn't generation, it's *constrained* reuse. The internal model became a pattern encyclopedia. Teams stopped debating "the right way" to do a distributed lock or a paginated response because the assistant just offered the one we've already vetted.
Code consistency improved dramatically, but the build cost is still significant. The real payoff was in PR reviews. We saw a measurable drop in comments about architectural deviations because the generated code started cleaner. The side benefit that justified the cost for us was actually the automated drift detection; the model's suggestions acted as a canary when a team started coding around a flawed library version or a deprecated API, flagging it weeks before it became a production issue.