The article discusses various open problems in machine learning inspired by a graduate class. It critiques current methodologies, emphasizing the need for a design-based perspective, better evaluation methods, and innovations in large language models. The author encourages researchers to explore these under-addressed areas.

This article presents a collection of skills focused on context engineering for AI agents. It covers the principles of managing context, designing memory systems, and optimizing agent operations. The skills are platform-agnostic and include practical examples for implementation.