Crowdin

Short UI words are a real problem for localization. Without knowing where the word lives on the screen, human translators and AI models are just guessing. Instead of spending hours writing out explanations for hundreds of text strings, this is a task you can now delegate entirely to AI. With our new Crowdin Skills, your local AI Agent (like Cursor, Copilot, or Claude Code) can now: - Download text strings missing context. - Analyze your code to figure out exactly what they do. - Write precise UI descriptions and push them straight back to Crowdin. Read the guide here 👉 https://lnkd.in/dQyYfQ63 Share this guide with your dev team! 🧑💻

How do you know if your project configuration is actually ready for translation? Translating without proper configuration leads to expensive post-deployment bugs - from broken, overlapping UI layouts to mistranslated buttons. To fix this, we built Crowdin Advisors - an automated background analyzer for your localization setup. It continuously monitors your projects for 11 core checks (like missing screenshots, weak context, or loose character limits) and gives you direct solutions to fix them before translation even begins. Whether you are running a team without a dedicated localization manager or scaling across dozens of enterprise projects, Advisors acts as your automated quality gate. Already available on crowdin.com and Crowdin Enterprise. Big thanks to Dorota Pawlak for the video overview! 👉 https://lnkd.in/dGVQHXmk Check out the blog post (link in the comments) 👇

Ian Cowley from Salto shares a technical writer's perspective on why late-stage translation is broken and why strings need real context from day one. 👇 Read the main highlights in our newsletter

Listen on: Spotify 👉 https://lnkd.in/ez8ewtwm YouTube 👉 https://lnkd.in/eXWE9QDk Apple Podcasts 👉 https://lnkd.in/e6FBS_a9

Please, no more Lorem Ipsum in designs. Designing with filler text creates an illusion. It masks layout flaws that immediately break the moment your text expands or flips (RTL) in other languages. In the latest episode of the Agile Localization Podcast, Ian Cowley, Technical Writer at Salto, explains to host Stefan Huyghe why real strings need to be used from the start. Ian also told how his role evolved into a Context Engineer, how they manage l10n for Salto through an automated GitHub and Crowdin pipeline, and how they use an AI-first, human-refined workflow to translate tens of thousands of strings without losing product nuance. Listen to the full episode on your favorite platform: 🎙️ Spotify https://lnkd.in/ez8ewtwm 🎙️ Apple Podcasts https://lnkd.in/e6FBS_a9 🎬 YouTube https://lnkd.in/eXWE9QDk

The more case studies we publish, the more we realize: no two localization workflows are ever the exact same. When we started diving deeper into how our customers use Crowdin, we expected to see standard patterns. Instead, we’re finding a treasure trove of unique setups. Take our latest case study with Bejo - a global leader in vegetable seed breeding with over 2,300 employees and 31 international subsidiaries. We’ve been proudly partnering with them since 2021. We had the pleasure of chatting with Jorrit Stroet, Product Owner at Bejo Zaden, who walked us through how they completely transformed their localization pipeline during an ERP migration: The challenge was in moving away from a legacy system where translations lived in massive, code-heavy .txt files. Their setup - an automation pipeline connecting Crowdin with Azure DevOps Repos. Bejo is using regional managers to review translations instead of outsourcing, so they needed one place to work in. Every company has a unique problem to solve. Check out how Jorrit and the Bejo Zaden team solved theirs: https://lnkd.in/divZZEU5

Highlights of the real-world localization stories shared by Aydın Gür on the recent episode of the Agile Localization podcast👇

Your tools should adapt to your workflow, not your workflow to your tools. When localization stalls a launch, most teams just throw more manual processes at it. Marcel Pintó Biescas (Head of Mobile at Holafly) took a developer's approach instead. He used the newest AI and context-sharing technologies to flip the entire handoff upside down. Instead of manual copy-pasting, his team built a custom /localize skill using Claude and MCP. Now, Holafly developers write code first, type a single command in their IDE, and the AI automatically extracts text, reads full-screen UI layouts, and generates type-safe assets. But the real magic is how this custom developer workflow connects to Crowdin. By connecting this skill directly to Crowdin, Holafly built an end-to-end automation that achieved: ✅ 5x faster engineering cycle ✅ 4 new European languages added with 0 extra development work Want to see how they achieved that? Read the full case study here 👉 https://lnkd.in/dfZ4Hz-7

What do you do when you need to translate a rare language pair? Imagine you need to localize your product from Simplified Chinese to Estonian. You have two choices: Option 1️⃣ : Spend weeks searching for a rare bilingual linguist. They will be incredibly hard to find, they will charge premium rates, and if they get sick or leave, your entire release pipeline will get stuck. Option 2️⃣ : Build a smart localization workflow. You translate your Chinese source text into English using your standard workflow (for example, Translation + Proofreading). Then, you use a Switch Source Language step to automatically flip the source text to English for your final target languages. Your workflow now treats English as the new source. This gives you access to a massive, cost-effective pool of English-to-any translators. But most translation management systems force you to manage this "bridge" process manually. You have to create separate tasks, manually export files, and reconfigure project settings every single time developers push code. In Crowdin Enterprise, the Switch Source Language step is a completely automated, visual block in your workflow. You connect the steps once, and the platform handles the continuous routing automatically. 👉 Want to see exactly how to design, automate, and manage complex routing logic for your global content? Read our guide on localization workflows: https://lnkd.in/dtppdHP8

What happens when a linguistically perfect translation turns into a viral meme overnight? Apple Retail Globalization Editor Aydın Gür learned this the hard way. The translation was normal on paper, but real-world ambiguity weaponized it on social media. In our latest episode of the Agile Localization Podcast, Aydin sits down with Stefan Huyghe to share how his team has managed over 10 million words for the Turkish market. You will hear about: ▪️ Why the most valuable localization insights come from retail staff, not QA metrics ▪️ How local pronunciation of terms like "iOS" completely rewrites Turkish grammar rules ▪️ The contrast between localizing for enterprise clients and indie developers ▪️ A personal story from Aydin about how he started in localization This episode is full of practical wisdom on building human-first translation workflows. Listen on: 🎙️ Spotify https://lnkd.in/gKXh3iub 🎙️ Apple Podcasts https://lnkd.in/gJKF7j3e 📽️ YouTube https://lnkd.in/gQdedKeU