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Tue, April 21 at 4:30 PM - 5:30 PM GMT+5:30TechLead ArchitectureDeepTech
Early studies suggest that AI coding assistants can boost productivity by 26% to 55%. However, newer research highlights a paradox. Experienced engineers working on real codebases can become 19% slower when using AI, even though they perceive themselves to be faster. This session examines this “productivity placebo” and the underlying reasons behind it. The focus is on the cognitive overhead introduced by AI-assisted development, where responsibility shifts from writing code to validation, review, and integration. This shift can increase context switching as developers balance understanding AI-generated outputs with maintaining focus and flow. The session also explores how rapid code generation can introduce downstream costs in debugging and maintenance, and presents strategies to mitigate these challenges.
What You Will Learn
Who Should Attend
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Ragunath Jawahar is the Founder of Legacy Code HQ, where he specializes in helping developers and organizations master massive, complex codebases. With nearly 15 years in the industry and 5 years working with large codebases across startups and enterprises, he discovered that software complexity is fundamentally a human comprehension problem, not just a technical one.
To solve this challenge, Ragunath has built innovative visualization tools including Eureka and Timelapse (open-sourced on GitHub), which help developers navigate complex systems by surfacing relevant information while filtering out noise. His unique expertise combines legacy codebase rescue with 2+ years of AI-assisted development experience, positioning him to address a critical emerging problem: AI's acceleration of generating hard-to-maintain codebases.
Through his work at Legacy Code HQ, Ragunath teaches developers how to harness generative AI to build production-grade applications while avoiding maintainability pitfalls—leveraging first principles from human cognition, software development, and AI. This rare combination of legacy code mastery and AI expertise makes him uniquely qualified to help teams build maintainable software in the age of AI acceleration.
