Blueprint for a flashcard language-learning app

Fluxlingo is a blueprint for a next-generation language-learning app: flashcards with spaced repetition at the core, then AI personalization, social features, and a minimal interface — so study stays effective and compelling enough that people come back every day.

Market context

App	Strengths	Opportunity for Fluxlingo
Anki	A very effective SRS algorithm, deep customization	A UI/UX that is friendly to beginners; seamless AI integration
Memrise	Native-speaker videos, gamification	Tests generated from study history; community features and group study
Quizlet	A huge library of user-created content, many study modes	An AI-personalized path instead of relying only on existing content; a leaderboard
Duolingo	Market-leading gamification, very engaging	Deeper customization of study sets, sharing personal flashcard decks, focused study groups
Drops	Visual vocabulary, 5-minute lessons	Extend it to grammar and tests, cross-platform sync, and social features

Positioning: Anki's SRS strength + Duolingo's appeal + Quizlet's personal content, in an interface that is easy to use.

Core: spaced repetition

Each card is reviewed just before the learner is about to forget it. A correct answer lengthens the gap until the next review; a miss sends the card back to a short gap. The classic algorithm is SM-2:

sm2.ts

type CardState = { repetitions: number; intervalDays: number; easeFactor: number };

// grade: 0 (total blackout) … 5 (recalled at once)
export function review(card: CardState, grade: number): CardState {
  if (grade < 3) return { ...card, repetitions: 0, intervalDays: 1 };

  const repetitions = card.repetitions + 1;
  const intervalDays =
    repetitions === 1 ? 1 : repetitions === 2 ? 6 : Math.round(card.intervalDays * card.easeFactor);
  const easeFactor = Math.max(1.3, card.easeFactor + 0.1 - (5 - grade) * (0.08 + (5 - grade) * 0.02));

  return { repetitions, intervalDays, easeFactor };
}

A new card starts with easeFactor = 2.5. Once there is review data, you can move to FSRS — the algorithm Anki has used by default since version 23.10. It learns its parameters from that user's own history and predicts more accurately. This is also where "AI personalization" has the most real value.

Features

Feature	Description	Phase
🔁 SRS review	Decks, a daily review session, a schedule driven by the algorithm	MVP
🔄 Sync	Save progress on the server, sync up to 3 devices	MVP
📝 Generated tests	Build a test from study history, focused on cards the learner often misses	After MVP
🎯 AI personalization	An adaptive path, difficulty adjustment, content suggestions	After MVP
🤖 AI-generated material	Generate flashcard decks and new example sentences	After MVP
👥 Group study	Create a group, share decks, discuss	After MVP
🏆 Leaderboard	Compete with friends and the community	After MVP

AI-generated content needs a look from the user before it goes into a deck — a grammatically wrong example sentence will be reviewed over and over.

Design

Principle	How
🎨 Minimal	The review screen shows only the card and the grade buttons; remove anything that distracts
📱 Personalized interface	Custom theme, font, and font size
✨ Subtle motion	Card flip and smooth transitions between cards; respect prefers-reduced-motion
🌗 Light / dark	Follow the system, with a manual switch — learners often review at night

Architecture

A review session should run offline: keep a queue of review results on the device and sync when the network is back. The schedule is recomputed from review history, so conflicts between devices are not a concern.

Data model

• card_states: one user's SRS state for one card (interval, ease_factor, due_at) — kept separate from cards so many people can study the same deck.
• reviews: one review (grade, response time) — the input for generated tests, statistics, and FSRS.
• The most important query is "cards due today": index (user_id, due_at).