MindBites
A podcast recommendation engine powered by a patented Hash Search algorithm, with a React Native mobile app and embeddable web player.
The discovery problem
Podcast discovery is broken. Listeners find new shows through word of mouth, platform algorithms optimized for engagement over relevance, or curated lists that go stale. The result is that most people listen to the same 5 podcasts and never find the ones they would actually love.
MindBites tackles this with a recommendation engine built on a fundamentally different approach to content matching.
The Hash Search algorithm
The core differentiator is the patented Hash Search algorithm, which powers the recommendation engine. Rather than relying on collaborative filtering (“people who liked X also liked Y”) or simple content tagging, Hash Search uses a proprietary approach to match listeners with podcasts based on deeper content signals.
The patent covers this recommendation methodology, giving MindBites a defensible technical moat in the podcast discovery space.
The ecosystem
MindBites is not a single app. It is an ecosystem of interconnected products:
- mdb-reco-engine: The recommendation API that powers everything. This is the core service built around Hash Search.
- mindbites-react-native: A mature mobile app with 35+ screens covering discovery, playback, library management, and personalized recommendations.
- mdb-js-player: An embeddable web player (Vue and React variants) that can be integrated into third-party sites, extending reach beyond the native app.
Why it matters
MindBites represents the venture side of my work. It combines a patented technology with a real market need (podcast discovery is a known pain point with a large addressable market) and a multi-platform product strategy. The mobile app is mature, the recommendation engine is in active development, and the ecosystem is designed to grow through integrations and partnerships.