The CTO Podcast with Fexingo · 2026-07-17 · 10 min
In this episode, Lucas and Luna dive into Pinterest's recent rebuild of its image search backend, handling 5 billion pins and 200 million monthly users. They walk through the shift from a monolithic search index to a distributed vector-based retrieval system, the decision to move from GPUs to custom TPUs for embedding inference, and how the engineering team tackled cold-start problems for new pins. The conversation covers real-world trade-offs in similarity search, the role of approximate nearest neighbor algorithms, and the implications for visual discovery at scale. #Pinterest #ImageSearch #VectorSearch #ANN #TPU #GPU #ColdStart #ComputerVision #Embedding #Scale #DistributedSystems #MachineLearning #RecommendationEngine #SearchBackend #VisualDiscovery #EngineeringRebuild #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo
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