The very nature of LLMs means you can't invent a thing for current agents to use that they'll be better at using than the things they already know how to use from their immense training data. You can give them skills, sure, and that's useful, but it's still not their native tongue.

To make a thing that's really for agents, you need to have made a popular thing for humans ten years ago, so there's a shitload of code and documentation for them to train on.

Everything which just works "by convention" or by "opinionated defaults" (allowing a tightly coupled but very feature rich framework) helps to reduce the noise / lines that needs to be reviewed.

While this approach might not be optimal for every project, I'm certain the opinionated defaults can work for many endeavours. And the reduction of complexity might be one important aspect, which can make an "agentically engineered" project sustainable.

Django code is pretty easy to review quickly. LLMs are good at writing it.

Django is just old and bloated, so the fork is a good idea. Maybe I will use this for my next side project.

- fork of django

- it's opinionated

- typed

- comes with skills / rules / docs baked in

I'm not against this idea in principle, but I'm also not sure why that is better than what's already out there, except maybe you save some tokens by not vibe coding this yourself?

I do think in the future we'll see some novel libraries that are agent-optimized first. I'm not sure if this is it, though.

(edit: formatting)