In this episode of Talking to AI, Paul shares a candid, behind-the-scenes account of his journey automating podcast production from start to (almost) finish using artificial intelligence. Drawing on several months of hands-on experimentation and coding, he breaks down both the successes and challenges in building a semi- and then fully-automated workflow. From handling audio quality and multitrack recording issues, to harnessing AI tools for content creation and managing the intricacies of APIs, Paul offers practical insights for podcasters and developers exploring similar paths.

Through trial and error, Paul discovered the limitations of off-the-shelf tools, why multitrack audio is crucial, and how ChatGPT (along with other LLMs) played a pivotal role in generating titles, summaries, tags, and artwork. He also reveals why some manual oversight—especially approving podcast titles—remains essential, and maps out the evolving stack he's adopted: from custom Python scripts to APIs for ChatGPT, WordPress, and hosting providers.

Looking ahead, Paul reflects on what these automation lessons reveal about the broader future of work with AI. He predicts a growing demand for high-level technical skills in orchestrating automation, and urges listeners (especially other creators and tech enthusiasts) to embrace disciplined experimentation as they shape their own AI-driven workflows.

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