For the past 18 months, I’ve been building a podcast discovery app because I felt like existing platforms don’t make it easy to find new episodes. Most recommendation systems focus on entire podcast series, which can be limiting—what if the best content for you is hidden in an episode of a show you’ve never heard of? I wanted to create something that surfaces great episodes, not just popular shows.

To do this, I built a system that streams, downloads, transcribes, and analyzes a huge number of podcast episodes. Instead of relying on metadata or user behavior alone, it evaluates episodes individually based on content, merit, and inspiration. The recommendation engine is designed to balance relevance with diversity, avoiding echo chambers while still keeping suggestions engaging.

On the technical side, I’m running a Django backend with a PostgreSQL database, supported by two NVIDIA GPU-based HyperStack servers that handle Whisper-based transcription and deeper semantic analysis. The model doesn’t just surface what’s already popular—it actively works to highlight lesser-known but high-quality episodes that might otherwise go unnoticed.

I’d love to hear your thoughts. What frustrates you most about podcast discovery? What would make this useful for you?

Hey, @bjar2 - would love to cover this for Podnews, the industry newsletter.

I guess I'm curious about how the discovery platform works, and what data you're using. An API would be fun, wouldn't it?

editor@podnews.net