When your company grows from a single flagship product to 55+ tools, one challenge becomes unavoidable: how do you keep localization fast, accurate, and consistent across all of them? That’s exactly the problem Semrush has solved. In a recent episode of The Agile Localization Podcast, host Stefan Huyghe sat down with Elena Sadchikova, Team Lead for Localization and UX Writing at Semrush, and Robbin van Schagen, Owner at TYPE Translations and former Semrush localization team member, to discuss how Semrush scaled its localization operations without sacrificing speed or quality. Listen to the new episode on: - Apple Podcasts - Spotify - YouTube

When you initially develop an application, internationalization of that app or website doesn't come to mind. Eventually, however, you discover a substantial untapped audience blocked by the language barrier. Consider that there are people who only understand their native language and originate from different countries, such as Poland, England, the USA, and Mexico, who are accessing your App or Website. How will they understand the services or products you are offering? Here, JavaScript Localization comes into the picture. Let me tell you why we need it. According to statistics, more than 76% of online buyers prefer to purchase a product if the description is in their native language. At the same time, 40% of them do not prefer to buy from websites only in English. JavaScript localization is one of the most effective ways to adapt your entire application into a language users can easily understand. What you will master from this guide: - What is JavaScript Localization, and why use it? - Comprehensive knowledge of JavaScript Localization and its Implementation. - Perfect tool selection and workflow optimization. - We’ll walk through some of the best JS Localization tools, including i18next, FormatJS, and more.. - An advanced tool integration with Crowdin - Get the best production-ready practices.

If you’ve ever tried to scale a multilingual CMS across departments, regions, and regulatory environments, you know the pain: bolt-on translation plug-ins, scattered workflows, and expensive “customizations” that age badly. In this episode of The Agile Localization Podcast, host Stefan Huyghe sits down with Mathias Bolt Lesniak, Project Ambassador for the TYPO3 Association, to unpack how one of Europe’s leading open-source, community-driven CMS platforms delivers enterprise-grade multilingual content at massive scale. Listen to the new episode on: - Apple Podcasts - Spotify - YouTube

In August, we delivered 365 updates and completed 523 tasks. We've presented audio generation and localization with the Crowdin Dubbing Studio. We also continue to enhance our AI tools. Our Agentic AI is now smarter, with new capabilities for editing source texts, adding and managing comments, while the Context Harvester now gathers context with much more accuracy. This release features new integrations with leading AI models and editor updates, including a new UI for localizing non-text file formats (e.g., images and videos), a new way to switch between files, and more.

Speed and accuracy in localization can mean the difference between delighting a user and losing them entirely. Agile localization has transformed how language professionals work. But while much of the focus has been on enterprise tools and large-scale vendor operations, freelance translators are carving out their own space, leveraging technology to deliver high-quality results while building lasting client relationships. On a recent episode of The Agile Localization Podcast, host Stefan Huyghe sat down with Simona Kisser, a technical translator and software localization specialist, to talk about the evolving role of freelance linguists. From the importance of context to the art of balancing speed and quality, Simona shared practical insights from over a decade in the field. Listen to the new episode on: - Apple Podcasts - Spotify - YouTube

Model Context Protocol (MCP) is a technology that allows you to connect large language models (LLMs) to external, dynamic data sources and tools to provide more contextual information. Unlike Retrieve Augmented Generation (RAG), which accesses static documents, MCP connects to live resources that change constantly, such as code repositories or content management systems. This dynamic access helps LLMs provide more accurate and context-rich outputs. MCP usage can reduce "hallucinations" in which the AI generates incorrect information.