Idea for learning: feature flag service built on etcd, with a OpenFeature provider implementation and CEL for server-side runtime evaluation

I did avtually follow up on this, though not quite as described. I went for an implementation using BadgerDB instead, with clustering provided through the Raft consensus protocol. Whole project is here: https://github.com/frodejac/switch Incidentally, this was built when testing out Cursor for the first time, and I was pretty amazed at how quickly I could go from nothing to a functional MVP. Have to admit the amazement faded a bit after working with it for a while, as the tool quickly reaches some limits once the complexity starts to grow. And some routine tasks such as refactoring can quickly eat up hours of your time if left to the AI to figure out. The simple task of moving around existing code can get really hard when it’s done through «memorizing» the existing code and then reproducing it from memory instead of just copy-pasting things or using proper rafctoring tools.

I found that a much better approach for AI assisted refactoring is to have the AI suggest changes instead of running in agent mode. Get it to suggest new structure, which pieces can be extracted, etc., then have it create the scaffolding for the new files, and then lastly do all the copying and fixing manually. Much less hassle. And you don’t run the risk of suddenly getting a new feature you didn’t ask for.