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Memory Systems That Actually Work

SM-2 and custom SRS algorithms with adaptive scheduling, performance analytics, and gamification. The science of memory, implemented correctly in production apps.

Timeline 3–8 weeks
Investment $3,000–$10,000
FlutterPythonDjangoPostgreSQLFirebase Analytics
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SRS algorithm

SM-2 implementation with ease factor, interval calculation, and performance-based scheduling.

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Progress analytics

Per-item and per-user performance tracking — retention rates, struggle points, and learning velocity.

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Gamification layer

Streaks, XP, badges, and level progression that motivate without distracting from learning.

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Adaptive difficulty

Easier exercises for new material, harder ones for familiar content — automatic calibration.

01
Algorithm design

Choose SRS model (SM-2, Leitner, custom) based on content type and learning goals. Define interval parameters.

02
Data model

Database schema for items, reviews, intervals, and user performance — designed for efficient scheduling queries.

03
Review engine

Scheduling logic, due item querying, performance recording, and interval recalculation.

04
Analytics & gamification

Progress dashboards, streak calculation, and achievement system integrated with the review flow.

Case Study
Estonian Verbs SRS

Five-tier word status system (NEW → LEARNING → REVIEW → MATURE → MASTERED) with SM-2 scheduling, adaptive exercise difficulty per tier, session-based review queues, and streak/XP gamification.

5-tier SRS · Adaptive difficulty · Streak mechanics
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SM-2 (SuperMemo 2) schedules reviews based on how well you remembered an item — easy items are shown less, hard items more. It's the algorithm behind Anki and is the most research-validated approach to long-term retention.
Yes — SM-2 is the baseline, but I can adapt interval logic for specific content types, session length constraints, or learning goals.
Five-tier classification: New → Learning → Review → Mature → Mastered. Each state has different scheduling rules and exercise difficulty.
Yes — vocabulary, grammar rules, reading comprehension, and listening exercises can all be managed by one SRS with content-type-specific difficulty parameters.
The system detects long gaps and resets intervals appropriately — avoiding the overwhelming backlog problem that kills Anki usage for most people.
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