Comment by Draiken
Are you using these in your day job to complete real world tasks or in greenfield projects?
I simply cannot see how I can tell an agent to implement anything I have to do in a real day job unless it's a feature so simple I could do it in a few minutes. Even those the AI will likely screw it up since it sucks at dealing with existing code, best practices, library versions, etc.
I've found it useful for doing simple things in parallel. For instance, I'm working on a large typescript project and one file doesn't have types yet. So I tell the AI to add typing to it with a description while I go work on other things. I check back in 5-10 mins later and either commit the changes or correct it.
Or if I'm working on a full stack feature, and I need some boilerplate to process a new endpoint or new resource type on the frontend, I have the AI build the api call that's similar to the other calls and process the data while I work on business logic in the backend. Then when I'm done, the frontend API call is mostly set up already
I found this works rather well, because it's a list of things in my head that are "todo, in progress" but parallelizable so I can easily verify what its doing