Also an eng in big tech and there are constantly new initiatives I think we get asked to think about or do but main value I've found is by solving for my own problems with AI.

Specifically, I built out a workflow layer for my coding agents. One key workflow I use is an end-to-end pipeline that starts with deep researching the codebase, feeds into my custom ralph or goal implementation depending on the scope, and follows through all the way to automated PR review. The upfront engineering investment to get it working reliably was heavy, but the ROI on my daily velocity has been absolutely worth it. But it took a lot of iteration to not generate slop and burn tokens.

Beyond just the productivity gains, the process of building it from scratch was actually the biggest win. It forced me to get hands-on and learn the critical, pragmatic nuances of agentic engineering. I did this all outside of working hours as my own personal open source project. Other folks around me are using it. I don't know how things will shape up but part fear, part curiosity drove me to just try to learn as much as I can with so many things outside of my control.