Yeah that's somewhat close to what I meant, though there's an irony here in that your comment (and this one) are pretty reddit-esque.

I don't think lived experience matters too much to me. In some sense, AI has very unique "lived" experience, which is what creates the voice it uses ("doesn't have a voice" seems like an impossibility to me by definition).

I find AI very "human-esque", and its "self-reported" phenomenology is very entertaining to me, at least.

I also think AI writing might feel trashy also because most human writing is trashy.

Really interesting. I was thinking about something similar regarding the shape of code. I have no qualms recommending my agents take static analysis to the extreme, though it would cumbersome for most people.

What about someone inexperienced but skeptical, using AI to learn + fix their own code before opening the PR?

Linear walkthrough: I ask my agents to give me a numbered tree. Controlling tree size specifies granularity. Numbering means it's simple to refer to points for discussion.

Other things that I feel are useful:

- Very strict typing/static analysis

- Denying tool usage with a hook telling the agent why+what they should do (instead of simple denial, or dangerously accepting everything)

- Using different models for code review