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Explore why leading AI applications like Cursor, v0, chai.new, lovable, and bolt achieve superior performance by building with AI primitives rather than traditional frameworks. Learn from real-world experience scaling thousands of AI agents to handle over 100 million monthly runs, discovering how primitive-based approaches enable faster iteration, greater scalability, and simplified maintenance in rapidly evolving AI environments. Examine the challenges frameworks face keeping pace with weekly LLM updates and emerging standards like MCP, while APIs change at unprecedented speeds. Master eight practical agent architectures covering memory management, automatic tool integration, and serverless deployment strategies for building reliable, scalable AI agents. Challenge conventional engineering wisdom and understand why the AI industry needs to rethink infrastructure approaches, focusing on AI-native primitives instead of heavy, outdated frameworks that quickly become obsolete in the fast-moving AI landscape.
