Tan Wang | Software Engineer, Agent FoundationsOver the last year, Pinterest has gone from “MCP sounds interesting” to running a growing ecosystem of Model Context Protocol (MCP) servers, a central registry, and production integrations in our IDEs, internal chat surfaces, and AI agents. This post wa...
Unified Context-Intent Embeddings for Scalable Text-to-SQL
Your Analysts Already Wrote the Perfect PromptAuthors: Keqiang Li, Bin YangIn our previous blog post, we shared how Pinterest built Text-to-SQL with RAG-based table selection (Retrieval-Augmented Generation). That system introduced schema-grounded SQL generation and retrieval-augmented table selecti...
Scaling Global Storytelling: Modernizing Localization Analytics at Netflix
Valentin Geffrier, Tanguy CornuauEach year, we bring the Analytics Engineering community together for an Analytics Summit — a multi-day internal conference to share analytical deliverables across Netflix, discuss analytic practice, and build relationships within the community. This post is one of se...
Optimizing Recommendation Systems with JDK’s Vector API
By Harshad SaneRanker is one of the largest and most complex services at Netflix. Among many things, it powers the personalized rows you see on the Netflix homepage, and runs at an enormous scale. When we looked at CPU profiles for this service, one feature kept standing out: video serendipity scori...
Mount Mayhem at Netflix: Scaling Containers on Modern CPUs
Authors: Harshad Sane, Andrew HalaneyImagine this — you click play on Netflix on a Friday night and behind the scenes hundreds of containers spring to action in a few seconds to answer your call. At Netflix, scaling containers efficiently is critical to delivering a seamless streaming experience to ...