{"podcast":{"title":"The CTO Podcast with Fexingo: Technical Leadership, Architecture, and Engineering Org","slug":"the-cto-podcast-with-fexingo-technical-leadership-architecture-and-engineering-org-7871807","podcast_index_feed_id":7871807,"rss_url":"https://feeds.fexingo.com/business/the-cto-podcast.xml","website_url":"https://www.fexingo.com/","image_url":"https://audio.fexingo.com/business/the-cto-podcast/cover.png","author":"Fexingo","episode_count":76,"summary":"Lucas and Luna sit down in front of a whiteboard to dissect the decisions that shape technical organizations. Each episode of The CTO Podcast with Fexingo examines a specific engineering leadership challenge — from scaling a microservices architecture without creating a distributed monolith, to managing the cognitive load of a 200-engineer org, to choosing between a monorepo and polyrepo strategy based on team topology. The conversations are grounded in real-world cases: how Etsy restructured its data pipeline after a 2019 outage, why Stripe’s API versioning policy reduces breaking changes, or what Basecamp’s choice of SQLite over PostgreSQL says about product philosophy. Lucas brings the journalistic rigor — citing commit histories, RFCs, and postmortems — while Luna pushes back with the pragmatics of org dynamics, hiring constraints, and technical debt. There are no hot takes, no vendor pitches, no ‘best practices’ without trade-offs. Each episode ends with a specific tension left unresolved: the optimal number of direct reports for a VP of Engineering, the point at which a monolith should be broken apart, or whether a platform team should own the CI/CD pipeline. The listener is…","last_synced_at":"2026-06-27T14:18:24.326804+00:00","page_url":"https://stenobird.com/podcast/the-cto-podcast-with-fexingo-technical-leadership-architecture-and-engineering-org-7871807"},"episode":{"title":"How Pinterest Rebuilt Its Recommendation Engine for 500 Million Users","slug":"how-pinterest-rebuilt-its-recommendation-engine-for-500-million-users","published_at":"2026-06-20T19:40:54+00:00","page_url":"https://stenobird.com/podcast/the-cto-podcast-with-fexingo-technical-leadership-architecture-and-engineering-org-7871807/how-pinterest-rebuilt-its-recommendation-engine-for-500-million-users","show_page_url":"https://stenobird.com/podcast/the-cto-podcast-with-fexingo-technical-leadership-architecture-and-engineering-org-7871807","url":"https://audio.fexingo.com/business/the-cto-podcast/episode-0063.mp3","audio_url":"https://audio.fexingo.com/business/the-cto-podcast/episode-0063.mp3","summary":"In this episode of The CTO Podcast, Lucas and Luna dive into how Pinterest's engineering team rebuilt its core recommendation engine from a batch-processing pipeline to a real-time, graph-based system serving over 500 million monthly active users. They explore the specific architectural decisions Pinterest made: moving from collaborative filtering to a heterogeneous graph neural network called PinSage, deploying it on TensorFlow Serving with Kubernetes for low-latency inference, and handling the cold-start problem for new pins and users. The discussion covers the trade-offs between offline batch precomputation and online inference, how Pinterest reduced recommendation latency from hours to milliseconds, and the infrastructure costs involved. Lucas explains the graph-based approach that captures user intent through 'pins' and 'boards,' while Luna questions how this impacts content discovery and engagement. Tune in for a detailed look at a real-world scale challenge in modern machine learning systems. #Pinterest #RecommendationEngine #GraphNeuralNetworks #PinSage #MachineLearning #TensorFlow #Kubernetes #RealTimeInference #ColdStart #ContentDiscovery #EngineeringArchitecture #Scale #BusinessAndTechnology #FexingoBusiness #BusinessPodcast #CTO #TechnicalLeadership #MLInfrastructure Keep every episode free: buymeacoffee.com/fexingo","meta_description":"In this episode of The CTO Podcast, Lucas and Luna dive into how Pinterest's engineering team rebuilt its core recommendation engine from a batch-processi…","key_points":[],"chapters":[],"topics":[],"duration_seconds":824,"processing_state":"not_requested","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/the-cto-podcast-with-fexingo-technical-leadership-architecture-and-engineering-org-7871807/episodes/how-pinterest-rebuilt-its-recommendation-engine-for-500-million-users/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/the-cto-podcast-with-fexingo-technical-leadership-architecture-and-engineering-org-7871807/how-pinterest-rebuilt-its-recommendation-engine-for-500-million-users.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}