Are we raising a generation of developers who can fix bugs but don't understand why their solutions work? In this episode, we explore how AI tools like ChatGPT and Claude are transforming debugging—and what junior developers might be losing in the process.While LLMs can instantly generate solutions to coding problems, they often bypass the crucial investigative skills that separate good programmers from great ones. We dive into a real-world debugging story where an AI suggested sorting libraries for what turned out to be a simple database logic error, illustrating how quick fixes can lead us away from genuine understanding.This isn't anti-AI—it's about using these powerful tools as collaborators rather than replacements for deep thinking. We examine the hidden productivity costs of copy-paste solutions and offer a framework for debugging that combines human investigation with AI assistance.Essential listening for any developer who wants to thrive in an AI-augmented world without losing the core skills that make programming a craft, not just a job.Key Quotes"The time you invest in describing the problem is often the time you spend resolving it.""It's like being handed a complex puzzle that's already assembled. You have the solution, but you don't understand the pieces or how they fit together.""In the age of AI, the developers who thrive won't be the ones who can prompt an LLM the fastest. They'll be the ones who can think the deepest about the problems they're trying to solve."

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