Nick Nisi and Zach Proser from WorkOS explain how to build, manage, and scale AI 'skills'—reusable, portable instructions that make AI agents like Claude more powerful and consistent. They cover the core anatomy of a skill, best practices for writing them, advanced techniques like progressive disclosure, and their application beyond coding, from video generation to automating business workflows.
AI agents can interact with the world through either the Command Line Interface (CLI) or the Model Context Protocol (MCP). This summary explores the trade-offs between the two, highlighting CLI's efficiency for tasks the model is trained on, versus MCP's power of abstraction and governance for more complex, high-level operations.
As AI handles more of the coding, the role of a software engineer is shifting from writing code to planning and reviewing the work of AI agents. This talk explores the implications of this shift, the new workflows it demands, and the tools required to manage them effectively.
As the number of AI agents grows, humans become the bottleneck. This talk introduces AgentCraft, a game-inspired orchestrator that draws lessons from Real-Time Strategy (RTS) games to enhance human-agent collaboration, improve visibility, and increase agent autonomy, ultimately raising the ceiling of what's possible.
Gergely Orosz, author of The Pragmatic Engineer, discusses the bizarre trend of 'token maxing' in Big Tech, the evolving role of software engineers in the AI era, and why companies are heavily investing in internal AI infrastructure despite uncertain productivity gains.
Weiwei Wu and Jeff An, co-founders of Momentic, discuss their AI-powered testing platform that acts as a verification layer for software. They explore how the rise of AI-generated code makes robust testing more critical than ever and share their vision for a future of "truth-driven development" where engineers write specs, not code.