Spec-Driven Development offers a structured, reproducible, and reliable alternative to 'vibe coding' in the AI era. Al Harris from the Kiro team explains how to leverage specs as living documentation, integrate external tools via MCPs, and use property-based testing to create a tight feedback loop from natural language requirements to verified code.
Mukhund and Madhav Jha, co-founders of Emergent, detail their journey of building an AI-powered app builder that reached $15M ARR in three months. They discuss their pivot from enterprise agents, the multi-agent architecture that enables production-ready apps, and their vision for a future with a 'billion builders'.
Dhanji Prasanna, CTO of Block, discusses the company's AI transformation, centered on their open-source agent, Goose. He details how Goose leverages the Model Context Protocol (MCP) to automate complex workflows, saving engineers 8-10 hours weekly. Prasanna also explains Block's strategic shift to a functional organizational structure to accelerate AI adoption and shares his vision for the future, where swarms of smaller AI models will outperform today's monolithic LLMs.
A hands-on walkthrough of Codex, now a single, unified agent across your IDE, CLI, and GitHub. This summary covers new features like the IDE extension, automated code review, and best practices for delegating tasks to the local and cloud agent for a more efficient development workflow.
GitHub's Christopher Harrison explains how to leverage GitHub Copilot's agent capabilities. This summary covers using Copilot as an AI pair programmer, the importance of providing context, its different workloads, and how to use the new Copilot Coding Agent with the Model Context Protocol (MCP) to accelerate development responsibly.
Scott Wu, CEO of Cognition AI, discusses the exponential growth of AI capabilities in software engineering, likening it to a "Moore's Law for AI agents" with a doubling time of every 70 days. He chronicles the evolution of their AI agent, Devin, from handling repetitive code migrations to autonomously managing entire backlogs, highlighting the key technical challenges and paradigm shifts at each stage.