Da Ding

If you're a Senior Engineer looking to move up, the next role will likely be a Staff Engineer. And in this guide, Shruti shares tips from her own experience of getting promoted to Staff Engineer at PayPal and Slack. She talks about what Staff Engineers do (and how it's different from Seniors), why you might not be getting promoted, and how to take that next step. https://lnkd.in/g58dnFEG

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I wrote an article on how we translate user-generated content at scale at Mercari. It summarizes two years of work with various models, LLMs but not only, and how cost evolved, reducing it 100x over the period. I also go over non-AI features and user experience. https://lnkd.in/gbpQAQmX

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Huge thanks to Zhe Jia, Shuai Wang, Aditi Bamba, and Martin Wang from DoorDash for publishing this incredibly practical breakdown of their RAG implementation for Dasher support. This post saved me countless hours of trial and error. The two-tier guardrail approach is what really clicked for me: * Run a cheap semantic similarity check first (sliding window comparison between response and KB articles) * Only if that fails, escalate to the full LLM evaluator Result: 90% fewer hallucinations, 99% fewer compliance issues, without killing your latency budget When you're actually shipping RAG to users, this kind of practical filtering makes all the difference. Everyone talks about prompt engineering, but nobody talks enough about how to catch bad outputs before users see them. Their broader setup—quality monitoring across 5 dimensions, regression test suites, the whole LLM Judge framework—gives you a real blueprint beyond the basic tutorials. You can't fix what you're not measuring. The article is https://lnkd.in/dbC9BG7a

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Terminal Use (YC W26): Vercel for background agents. Terminal Use is hosting infrastructure for background agents that makes harness engineering easy. https://lnkd.in/gv99wN2t

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Localization is more than translation. Pendo.io just rolled out our first localized experience—in Japanese 🇯🇵—designed for Japan’s fast-growing, AI-driven tech market. Go Pendo.io Japan ! From login screens to product areas, we’re bringing product-led growth to users in their language. Learn how we're doing it—and why Japan was first! 👇

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Over the past year, my team and I built Pinterest's Model Context Protocol (MCP) ecosystem from the ground up: a central registry, a growing fleet of domain-specific MCP servers, and production integrations across our IDEs, internal chat surfaces, and AI agents. MCP is an open standard that gives large language models a unified way to talk to tools and data sources. At Pinterest, we used it as the substrate for AI agents that can safely automate real engineering tasks, from querying Presto data on demand to diagnosing Spark job failures and surfacing institutional knowledge. A few things I'm particularly proud of: * We designed for security from day one: two-layer auth (end-user JWTs + mesh identities), business-group-based access gating, and mandatory human-in-the-loop for sensitive actions. * We made it easy for any team to ship a new MCP server by creating a unified deployment pipeline that handles all the infrastructure, so domain experts just write business logic. * The ecosystem has scaled to 66,000+ invocations per month across 844 monthly active users, saving an estimated 7,000 engineering hours per month. This was a cross-functional effort. Thank you to everyone on the Agent Foundations, Security Engineering, and Traffic Engineering teams who made this possible, and to our engineering sponsors for their support and guidance. Full write-up on the Pinterest Engineering Blog: https://lnkd.in/e-mH26Bw

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