In this episode, Kyle Polich sits down with Cory Zechmann, a content curator working in streaming television with 16 years of experience running the music blog "Silence Nogood." They explore the intersection of human curation and machine learning in content discovery, discussing the concept of "algatorial" curation—where algorithms and editorial expertise work together. Key topics include the cold start problem, why every metric is just a "proxy metric" for what users actually want, the challenge of filter bubbles, and the importance of balancing familiarity with discovery. Cory shares insights on why TikTok's algorithm works so well (clean data and massive interaction volume), the crucial role of homepage curation, and how human curators help by contextualizing content, cleaning data, and identifying positive feedback loops that algorithms might miss.

The conversation covers practical challenges like measuring "surprise and delight," the content deluge created by democratized creation tools, and why trust in tech companies is essential for better personalization. Cory emphasizes that discovery is "a good type of friction" and explains how the CODE framework (Capture, Organize, Distill, Express, plus Analysis) guides professional curation work. Looking to the future, they discuss the need for systems thinking that creates narrative connections between content, the potential for conversational AI to help users articulate preferences, and why diverse perspectives beyond engineering are crucial for building effective discovery systems. Resources mentioned include the newsletter "Top Information Retrieval Papers of the Week" and Notebook LM for synthesizing research.