When we shipped the vocabulary graph, we said it was infrastructure — a map of ~3,000 words per language with your personal knowledge layered on top — and that the features built on it would come next. This is the first one, and it lands in the feature the graph was always pointed at: Smart Blend. It turned out to be the first of a family rather than a one-off: the same mastery layer now also drives word families and Related Words in your dictionary and review sessions that keep confusable words apart.
A blend is a budget — stop spending it on solved words#
Think of a 30% blend of a 500-word article: about 150 words will appear in your target language. Those 150 slots are the entire learning surface of that reading session. Every slot spent on a word you've known cold for months is a slot not spent on a word you're actually acquiring.
The old engine couldn't know the difference — it saw text, not you. The new Blend Engine v2 selector knows what you've demonstrated, computed on your device from the same signals your spaced repetition already tracks, and spends the budget accordingly:
| Your relationship to a word | What the blend does now |
|---|---|
| Mastered — you've proven it repeatedly, over time | Skipped — the slot goes to something you'll learn from |
| Learning or due for review — in active rotation, or scheduled to come back | Favored — meeting it in real context is the review, and words you keep missing get the extra encounters they need |
| New to you | Chosen by usefulness, with a preference for words connected to vocabulary you already know — one connection away is where learning is cheapest |
The due-for-review case deserves a highlight: it turns reading into invisible spaced repetition. A word scheduled for review that appears inside a sentence you chose to read — anchored by context, understood in use — is a better review than any flashcard, and the research on contextual learning has said so for decades.
Inflections, mastery, and why the graph matters here#
Text never politely serves you dictionary forms. You mastered despertar; the article says despierta. Without the graph's form resolution, the engine would treat despierta as an unknown word and happily spend a slot on it. With it, your mastery follows the word through its forms — including irregulars like Italian va → andare.
This is also why the whole thing costs you nothing at runtime: your knowledge profile is derived on your device from data the app already has, used only for that blend, and never stored on our servers. No extra AI calls, no waiting.
The immersion twist: high up the slider, the logic flips#
Here's a subtlety we only saw because a real reader pushed the slider up. At 20–40%, skipping mastered words is obviously right — that's acquisition mode. But someone requesting an 80–90% blend is asking for immersion, and there the old rule backfires: your mastered words are precisely the ones you'd read most comfortably in the target language, so keeping exactly those in your native language fights the request.
So at the immersion end of the slider, mastered words become filler of last resort: words you're learning always come first, but if the text can't otherwise reach the density you asked for, your mastered vocabulary fills the gap — zero-effort reading that gets you to real immersion instead of a patchy, under-delivered page. In acquisition mode, the skip guarantee is absolute.
What new users get#
Nothing about this makes the app worse before you have data. With an empty dictionary, selection runs on pure usefulness — the words most worth learning, spread through the whole text — which is exactly the configuration we benchmarked hardest. As your saved words and game history accumulate, the personalization grows in automatically: no settings, no thresholds, no setup. And if you want the blend matched to your overall level from day one, that's the companion feature: level-aware blending.