Cicy T.

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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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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Developing a large ecosystem with both open- and closed-source components comes with some interesting challenges. Back when we were bootstrapping CKEditor, my purist mindset pushed us to develop each of our 30+ packages in separate repositories. The goal was to sort of "force" higher modularity and more responsible architecture for every component. As you can probably guess 😅, it did not scale. Later, we consolidated most of the packages into two monorepos – one open source, one closed source – while keeping a few granular packages separate where it still made sense. This helped a lot, but it didn’t fully solve the problem. We were still working across multiple repositories and kept running into issues like dependency coordination and lockfiles. Recently, we took another step forward. We migrated to pnpm and redesigned our overall approach. In this post, Filip Sobol explains the new setup implemented by the CKEditor 5 Platform team and what we learned along the way: https://lnkd.in/eHS28qxe

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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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My Startup Was Working… Until It Wasn’t We had paying customers. We had local press. We were gaining traction. It was 2016. Then UberEats, DoorDash, and Postmates entered Portland with billions in funding. At the same time, Intel—where I worked full-time—announced massive layoffs. Overnight, my 40-hour week almost doubled. Midnight fab shifts, weekend work, endless overtime. My startup didn’t die because the idea was bad. It died because I didn’t have the financial runway to compete. Here’s what that painful chapter taught me: 1. Passion + grit aren’t enough. Startups need time, capital, and breathing room to survive. 2. Corporate is just built for stability. It can’t nurture the creative chaos that startups need. 3. Adaptation beats stubbornness. Sometimes the smartest move is to regroup, save, and come back stronger. So I made a choice: doubled down on my W-2 career, moved to Seattle, and joined Amazon to maximize income. Every dollar earned—and every loan I could pull—went straight into real estate. One property a year turned into two… then into double digits in a single year. Deals kept coming, I kept closing, and I built a local team to keep momentum alive. By the end of 2022, my portfolio was generating more passive income than my paycheck. That’s when WoolooAI.com was born—not as a flashy startup, but as a necessary system to simplify and automate the chaos: acquisitions, CRM, portfolio management, project management, all streamlined. The truth is: - Without the grind of my early startup days, I wouldn’t have landed the Amazon role. - Without Intel’s layoff threats, I wouldn’t have learned the importance of building a financial runway first. - And without the expertise I developed at Amazon, I wouldn’t have gained the skills that helped me build WOOLOO AI. The irony isn’t lost: the same challenges that once crushed me ended up shaping the freedom I enjoy today. 💡 Takeaway: Sometimes what feels like an ending is really just the foundation for the next chapter. 👉 If you want the full story, I wrote it here: https://lnkd.in/geEVye36 💬 I’d love to hear your story too—what twists or setbacks ended up shaping where you are today? #Entrepreneurship #Startups #RealEstateInvesting #AmazonAlumni #FinancialFreedom #LessonsLearned #HumbleGrowth

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A reminder that our next RocksDB meetup is this coming Monday at Meta HQ! FoundationDB(https://lnkd.in/gnmY7A6q) and CloudFlare will both be presenting. We'll also share how internally at Meta we have built a unified storage management plane to manage all our RocksDB instances in production, a series of lightning talks about new RocksDB functionality, and how RocksDB is innovating and evolving in the age of AI. Dinner and RocksDB swag(first come, first serve) will be provided! More details and signup here: https://lnkd.in/g9eackSG

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Honored to have been invited to fal and Henry Shi’s Lean AI Leaderboard Celebration Dinner, celebrating some of the leanest and fastest-growing startups in SF. Here are some of the most overwhelming trends that were discussed among high-growth companies working with small, high-output teams: 1/ Headcount is no longer used as a success metric. As Henry Ward said, “Hiring means we failed to execute and need help.” Headcount is not a flex measure anymore. People aren’t asking “How big is your team?” anymore. They’re asking “How many customers do you have?” 2/ They're finding ways to leverage AI across every function. Every engineer uses Cursor or Windsurf. Every GTM leader is in Clay. The most efficient teams are built AI-first across the board. Thank you to Henry Shi, Burkay Gur, and Gorkem Yurtseven for putting this all together!

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