BlendLang

Real books.
Readable sooner.

BlendLang turns your favorite ebooks into a self-paced, immersive, language-learning experience. By gradually replacing words with context-aware, offline, AI translations at a pace you choose, you can enjoy all the books you're already looking forward to reading — Romance, Fiction, Biography and more — while naturally building your target-language vocabulary.

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How it works

The core idea is simple: start with a real book, use word usage frequency information to identify which words you should learn next, and rely on fully local translation to choose high-quality replacements in context.

  1. BlendLang ingests an EPUB file and a frequency list for the source language, ranked by common usage. It can also use optional files that preserve your learned vocabulary from previous books and reduce compute time.
  2. A fully local AI translation model then uses full sentence context to generate the best possible target-language equivalents for words selected from the text. This helps avoid many of the errors that come from simple dictionary-style replacement.
  3. The app replaces words according to user-defined pacing rules, so learners are not pushed to absorb too many new items too quickly.
  4. As the learner progresses, known vocabulary carries forward into future books, allowing the reading experience to become more advanced over time.

What makes BlendLang different

Reading stays enjoyable

BlendLang is built for people who already love to read. Instead of pulling you out of the book with constant drills, lookups, or flashcards, it lets reading itself do more of the work. New vocabulary is introduced at a manageable pace, reinforced throughout the book, and carried forward into the next one.

Translation happens locally

The translation pipeline is built around offline, fully local AI. That keeps the workflow secure, self-contained, and aligned with Steluminary’s broader privacy-first philosophy.

Frequency-driven progression

Source-language words are introduced in a controlled order based on frequency data, making the learning curve more natural.

Cross-book memory

Learned words are stored and reused, so each new book starts from the learner’s current level instead of resetting from zero.

Long-term bridge to fluency

Over time, the system can move beyond individual words toward larger language chunks and eventually sentence-level support.

Shared translation cache

BlendLang is more than a tool, it is a growing community of language learners helping one another move further into real books, real vocabulary, and real progress.

Processing a book locally can take real compute time. When a user finishes that work, BlendLang can save the resulting translation cache as a JSON file for that specific book. That file can then help another learner skip much of the waiting and begin from work that has already been done.

This creates two privacy-friendly ways to support the project and the community around it. A user can contribute their compute effort by sharing a cache they generated, effectively donating the value of that processing to fellow learners. Another user can support development financially by purchasing that cache as a shortcut.

In that model, one learner gives time, another gives money, and both help keep BlendLang improving without pushing it toward ads, surveillance, or data harvesting. It is a way for the community itself to help fund better software while making the path easier for the next reader.

Important elements

  • EPUB-based workflow built around real books.
  • Source-language frequency list controls the order of exposure.
  • Offline AI model provides context-aware word translation.
  • User-customized pacing rules control how quickly new target language words appear.
  • Persistent learned-word tracking carries progress into the next book.
  • Possible future support for idiomatic phrases and full-sentence replacement.
  • Book-specific JSON cache reduces repeat compute for shared titles.

Who it is for

BlendLang is for language learners who want to grow vocabulary through sustained, enjoyable reading, without giving up context, momentum, or privacy.

BlendLang is a reading-first path into a new language.

It begins with assisted reading, grows with the learner from book to book, and aims toward the point where reading native material becomes natural.