Aprelendo vs LinguaCafe
Aprelendo
aprelendo.comLinguaCafe
github.comSide by side
| Figure | Aprelendo | LinguaCafe |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| Self-hosted | — | Yes |
| Plans published | 1 | 1 |
| Spec score · Language Immersion Apps | 8.0 (#9) | 7.9 (#13) |
| Purpose | Aprelendo helps learners build usable vocabulary from ebooks, videos, and web content. | LinguaCafe is a self-hosted web app for learning foreign languages by reading and building vocabulary. |
| Review | Saved vocabulary can be reviewed with classic flashcards or cloze cards. | LinguaCafe includes spaced-repetition reviews similar to the Leitner system and can export highlighted cards to Anki or other SRS software. |
| Support | Users can contact support through a form, and the maker says the form stores IP and user-agent details for security reasons. | The project lists GitHub, Discord, Reddit, and email as contact channels, and says the maker will try to help with update problems reported on GitHub or Discord. |
Plans
In their own words
LinguaCafe
LinguaCafe is a free, open-source web app for learning languages through reading and vocabulary review. It is self-hosted, so users can run it on their own machine or private web server and control their data. Reading material can come from plain text, text files, ebooks, YouTube subtitles, subtitle files, Jellyfin external subtitles, or webpages. Readers can look up words and phrases, save translations, mark items as known or ignored, and track vocabulary by learning level. Spaced-repetition reviews follow an approach similar to the Leitner system, and highlighted cards can be exported to Anki or other SRS software. DeepL translations are available for lookups when enabled. Text-to-speech uses browser SpeechSynthesis, with voices dependent on the browser. The project lists many supported languages and supports Progressive Web App installation on mobile devices; it works in browsers on screens at least 340 pixels wide. Setup uses Docker Compose. The project supports x64, with an additional step for Apple silicon, but other Armv8 devices such as Raspberry Pi 3 and newer do not currently work. One user per server is supported.