So I’ve started a thing with Jim’s, and the first effort I am doing is erring the “tone” meaning I’m building a project template that will keep the bots focused.

I think that one part of the deep loop needs to be a check-in on expectations and goals…

So instead of throwing a deep task: I find that bots work better in small iterative chucks of objectives..

I haven’t formulated it completely yet but as an example ive been working extensively with cursors whole anthropic abstraction ai as a service:

So many folks suffer from “generating” quagmire;

And I found that telling the bot to “break any response into smaller chunks to avoid context limitations” works incredibly well…

So when my scaffold is complete the goal is to use Fabric Patterns for nursery assignments to the deep bots.. whereby they constantly check in.

Prior to “deep” things I found this to work really well by telling the bots about obsessively development_diray.md and .json tracking of actions (even still their memory is super small, and I envisioned a multi layer of agents where the initial agents actions feed the context of agents who follow along and you have a waterfall of context between agents so as to avoid context loss on super deep iterative research…

(I’ll type out something more salient when I have a KVM…

(But I hope that doesn’t sound stupid)