lingo.dev VS fbtee-migrate

RAL configuration. Each augmented engine contained 72 glossary terms per locale pair (70 custom translations plus 2 non-translatables), a brand voice profile (formal EU regulatory register), and 13 locale-specific instructions. Glossary terms were extracted from a training set of articles separate from the test set used for evaluation. Example entries: EN "provider" → PT "prestador" (not "fornecedor"); EN "high-risk AI system" → PT "sistema de IA de risco elevado" (not "sistema de IA de alto risco"). At inference time, only terms matching the current paragraph are retrieved and passed to the model — glossary size does not bloat the context window. Engines were configured on Lingo.dev as stateful localization engines — persistent context applied to every request.

If you've read my previous articles on HTML5 elements you didn't know you need or CSS modal windows, you know we like unusual tech at Lingo.dev. These upcoming JavaScript features solve real problems that have annoyed developers for years.

At Lingo.dev, we've been using this a lot deep inside our localization API, and it's dramatically simplified our text processing pipeline. What used to require multiple specialized libraries and server-side processing can now be handled directly in the browser with just a few lines of code.

As you know, we love unusual tech at Lingo.dev. So when in my last essay, David commented he was surprised to not see "dialog" HTML element, it hit me - wait, but there's so many HTML5 elements that we don't even know about!

Wait, you can build modal windows without JavaScript? Yep, that's CSS secretly laughing at JavaScript behind its back. At Lingo.dev, we love diving into weird engineering rabbit holes that make other devs do a double-take.

At lingo.dev, we're always on top of the latest models to ensure our translations are perfect. So when Meta released Llama 4, I immediately dug into what makes it special and how we developers can leverage its power without the infrastructure headaches.

In this post, I'll walk you through how to build an automated CI/CD translation pipeline using Lingo.dev and Knock that will handle localized notifications for your app without requiring any manual translation work from your team. We’ll introduce some tools that can help you scale your application to a global audience, automate translations locally using Lingo.dev's CLI, and finally integrate that CLI into a CI/CD pipeline so that translations are automatically created when developers open a pull request.

Hey! Max here, one of the co-founders - great question!

Technically - it's a compiler plugin: it augments build artifacts, so that extracting text from the app into JSON files for further translation is no longer required.

Since we're solving the problem at compile time, React code remains the same, but the app becomes just one cli command away from being fully translated into other languages!

Here's me speedrunning (~2min?) the localization of a typical next js app from english to spanish: https://github.com/replexica/replexica/blob/main/getting-sta...

3. Fun fact: this was made entirely on my phone (~2.5h) while I was walking around Tokyo. Voice prompting + o1-pro. Why? My friend was working on porting fbt to TS and said he was planning to build this. I wanted to one-up him + convince him to start using LLMs =)

One thing you should be aware of is that for at least Japanese, localization is far from just translating the text. There are lots and lots of Japan-specific cultural nuances you have to take into account for web users and even down to actually just having an entirely different design for your landing page often because those you'll find those just convert better when you know certain things are done that are typically not done for you know non-Japan websites.

Notta (multi-lingual meeting transcriptions + reports) is a great example if you compare their Japanese [1] and English [2] landing pages.

Note how drastically different the landing pages are. Furthermore, even linguistically, Japanese remains a challenge for proper context-dependent interpretation. Gemini 2.5 actually likely performs best for this thanks to Shane Gu [3], who's put in tons of work into having it perform well for Japanese (as well as other "tough" languages)

[0] https://github.com/f8n-ai/fbtee-migrate