And for the second part, I'm planning to include SRS features @markvdb pointed out in comments, combining both contextual learning with SRS would be interested I guess.
"To recognize 99% of all the words in Netflix's subtitles, you'd need to know 37,247 words"
Interesting approach! I really don't know how they managed to gather this list, but it's an interesting and clever method.
*other than those blocked for privacy reasons
I can contact you when it's added, if you like - perhaps you could email alex (at) nuenki.app?
As far as where I'm at: - I've listened to it in my target language for N hours. To my ear, it sounds correct and I've learned some new words that I then heard used consistently in native media. - Next, I'd like to set it to teach me a language that I already know, so that I can more reliably and easily spot errors. This will require some changes, since my target language is currently hardcoded. - Longer-term, validation based on languages I speak can't generalize 100% to other languages, nor can validation of version N make assertions about version N + 1. Correctness would benefit from native speakers periodically checking results, and usefulness would benefit from user feedback (even if only in the form of engagement or lack thereof).
I originally planned to add some kind of SRS to it, but I found that I learned much better just reading things in context instead of explicitly using SRS to memorize them. Steve Kaufmann (creator of LingQ) explains this better here [2]
[1] https://www.langturbo.com
[2] https://www.youtube.com/watch?v=t26IPxExmzs