11 0 2 min read

Weekend Reading #79

This week: Pinterest traces the evolution of their feed re-ranking from DPP to Sliding Spectrum Decomposition, showing why diversity drives long-term retention. Airbnb shares a battle-tested migration from StatsD to OpenTelemetry with a dual-write approach that cut metrics CPU overhead by 10x. Uber optimized Petastorm to resolve a GPU utilization bottleneck, slashing training time from 22 hours to 3 hours. And Netflix details the architecture behind their multimodal video search, unifying character, scene, and dialogue models into a real-time creative discovery tool.

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28 0 2 min read

Weekend Reading #78

This week: a comprehensive .NET desktop development interview guide covering WinUI 3, MAUI, and Avalonia. Lyft goes beyond A/B testing, using surrogates and region splits to measure long-term marketplace effects. Airbnb shares how COVID broke their forecasting models and the Bayesian architecture they built to survive the next shock. And Meta reveals the ML behind Friend Bubbles, blending social closeness models with content signals to power social discovery on Facebook Reels.

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47 0 2 min read

Weekend Reading #77

This week: a comprehensive .NET testing guide covering everything from unit test fundamentals to gRPC contract testing. Netflix introduces MediaFM, their first tri-modal foundation model for deep content understanding. LinkedIn reveals how LLM embeddings and Generative Recommender models are powering the next generation of feed ranking for 1.3 billion users. And Pinterest walks through building a full MCP ecosystem — from registry and security to 66K monthly invocations, saving thousands of engineering hours.

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