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Learn how to overcome AI's 20% success rate on real-world React development tasks in this conference talk that addresses the "Complexity Cliff" where AI models fail on complex projects. Discover engineering strategies to move from unreliable "vibe coding" to production-quality AI-assisted development through the Model Context Protocol (MCP). Explore the root causes of AI failures in React development, including context failures versus model limitations, and understand why AI-generated designs often look generic despite technical correctness. Master practical solutions including implementing MCP servers for Next.js and Chrome DevTools to provide AI agents with runtime state access, using Skills to enforce design system compliance, and architecting closed-loop workflows that validate code before deployment. Examine real-world benchmarks showing AI performance gaps in framework-specific tasks, learn context engineering techniques using the Iceberg Model, and understand how to manage AI tools like junior developers requiring structured guidance. Gain insights into measuring code quality beyond logic to include human taste factors, explore agentic loops and code-specific models, and discover strategies for improving AI performance in UI components, 3D visualization, and data visualization within React applications.
