Stanford’s Meta-Harness paper argues that optimizing the code around the model matters more than optimizing the model itself. I applied it to my Claude Code setup and got the first real numbers: 3,723 tokens always in context, with no evidence they’re the right ones.
Structured requirements, deviation rules, and the art of selective theft: five concepts borrowed from GSD, twenty rejected.
From token optimization to an orchestrated development workflow with Claude Code: teaching an AI your patterns is only part of the problem.
Another chapter in our automation journey at Wishew, where we bridge the gap between project management and development workflows, and discover what happens when your AI pair programmer actually knows your patterns.
How we built automated Slack notifications for ECS deployments with smart commit filtering, SSM state tracking, and Terraform IaC at Wishew.