This repository focuses on the concept of an "agent" as a trained model, not just a framework or prompt chain. It emphasizes building a "harness" – the tools, knowledge, and interfaces that allow the model to function effectively in a specific domain. The core idea is that the model *is* the agent, and the engineer’s role is to create the environment it needs to succeed.
The content details a 12-session learning path, reverse-engineering the architecture of Claude Code to understand how to build robust and scalable agent harnesses. It highlights the importance of separating the agent (model) from the harness, and provides resources for extending this knowledge into practical applications.
The content details a 12-session learning path, reverse-engineering the architecture of Claude Code to understand how to build robust and scalable agent harnesses. It highlights the importance of separating the agent (model) from the harness, and provides resources for extending this knowledge into practical applications.
LLM-powered Rust development assistant with meta-cognition framework.
