Evaluating Netflix Show Synopses with LLM-as-a-Judge
by Gabriela Alessio, Cameron Taylor, and Cameron R. WolfeIntroductionWhen members log into Netflix, one of the hardest choices is what to watch. The challenge isn’t a lack of options — there are thousands of titles — but finding the most intriguing one is complex and deeply personal. To help, we sur...
Stop Answering the Same Question Twice: Interval-Aware Caching for Druid at Netflix Scale
By Ben SykesIn a previous post, we described how Netflix uses Apache Druid to ingest millions of events per second and query trillions of rows, providing the real-time insights needed to ensure a high-quality experience for our members. Since that post, our scale has grown considerably.With our data...
Smarter Live Streaming at Scale: Rolling Out VBR for All Netflix Live Events
By Renata Teixeira, Zhi Li, Reenal Mahajan, and Wei WeiOn January 26, 2026, we flipped an important switch for Live at Netflix: all Live events are now encoded using VBR (Variable Bitrate) instead of CBR (Constant Bitrate). It sounds like a small configuration change, but it required us to revisit s...
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...