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Explore why AI coding tools fail to deliver promised productivity gains and learn how context engineering can unlock their true potential in this conference talk. Discover how developers waste countless hours navigating fragmented information across GitHub, Slack, Jira, and Confluence to understand system decisions and evolution. Learn about context engineering as a solution that provides both humans and AI with direct access to scattered decisions, history, and discussions across development tools. Understand how creating this contextual layer enables AI models like Claude and Cursor to truly comprehend production systems rather than making educated guesses. Master techniques for reducing AI hallucinations, accelerating iteration cycles, minimizing team interruptions, and making AI genuinely useful in real production codebases. Gain insights into building the foundational context layer that transforms AI from a promising concept into a practical development tool for actual engineering teams.
