My project, Lokalized (from 2017, in Java), has the same goal but took a different approach to the "little language" design. I'm guessing I had the same inspiration as the Fluent authors - existing solutions were just not expressive enough for the real world. Mentioning here because I'm always super interested in seeing how others approach the problem of effective i18n (it's a bit complex). Making Fluent more of a spec was the right call imo; I did not do that with my work.
In my experience, LLMs are terrific for most translation tasks, but you still need a way to encode the data (rules for genders, cardinalities, ordinalities, ...) for storage on disk/database/etc. for 1. performance and 2. consistency/durability. So LLMs are a big part of the solution, but not the whole picture.
revetkn•2h ago
https://lokalized.com