📚 Cardsy — Intelligent Spaced Repetition Platform

Cardsy is a scalable, production-grade learning platform built around spaced repetition, analytics, and gamified practice.
It combines Node.js microservices, a C++ native scheduling engine, and a Mongo + PostgreSQL + Redis data strategy to deliver high performance and deep learning insights.

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🖥️ Screenshots (Key Pages)

• Dashboard →

• Analytics →

• Decks Page →

• Flashcard (Question) →

• Flashcard (Answer & Evaluation) →

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⚙️ Technical Architecture

Microservices Overview

Service	Responsibility	Stack
Auth	JWT, OAuth, sessions	Node.js + PostgreSQL
Deck	Deck & card CRUD	Node.js + MongoDB
Practice Engine	Card ranking & SRS	Node.js + C++ + C++ Addon
Gamification	XP, streaks, leaderboards	Node.js + Redis
Analytics	Aggregations & insights	Node.js + PostgreSQL (TimescaleDB)

Communication

• gRPC / REST via API Gateway
• Realtime updates via WebSockets

Frontend

• React + Tailwind CSS
• Recharts / Chart.js

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🧠 Practice Session Flow

1. User starts a session
2. Due cards fetched from MongoDB
3. Metadata sent to C++ SRS engine
4. Engine ranks cards (Regular / Hard mode)
5. Node streams cards to frontend
6. User responses update progress
7. XP & analytics processed asynchronously

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🧩 Spaced Repetition Algorithm (SM-2 Variant)

EF' = EF + (0.1 - (5 - quality) * (0.08 + (5 - quality) * 0.02))
interval = previous_interval * EF'

• quality: 0–5 (derived from correctness + response time)
• EF starts at 2.5, min 1.3
• Implemented in C++ (Node Addon) for 10–20× speedup

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⚡ Gamification Engine

• XP, levels, streaks, badges
• Redis Sorted Sets for leaderboards:

ZINCRBY leaderboard:userXP <value> <user_id>

• Weekly resets via Redis Lua scripts

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📊 Analytics

• Daily activity heatmaps (365 days)
• Deck-wise accuracy & mastery
• Retention curves & streak tracking
• Auto-tagging of easy / medium / hard cards

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🧱 Database Strategy

Data	Database
Users, Auth	PostgreSQL
Decks, Cards	MongoDB
User Progress	MongoDB
XP, Streaks	Redis + PostgreSQL
Analytics	PostgreSQL (TimescaleDB)

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🎯 Core Features

Learning

• Spaced repetition (Easy / Normal / Hard modes)
• Paragraph & MCQ answers
• Markdown, LaTeX & images in cards

Decks

• Public & private decks
• Deck sharing & invites
• Collaborative decks
• Deck duplication

Gamification

• XP & levels
• Daily / monthly streaks
• Achievements & badges
• Deck-level leaderboards

Social

• Friend profiles
• Popular deck discovery
• Competitive challenges

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🧠 C++ Native Engine Responsibilities

• Card priority ranking
• Adaptive difficulty tuning
• Recall probability estimation
• Full-text search & ranking (optional)

#include <napi.h>
Napi::Object CalculateNextReview(const Napi::CallbackInfo& info);

const srs = require('./build/Release/srs.node');
const ranked = srs.rankCards(payload);

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🔍 Recruiter Highlights

• Hybrid Node.js + C++ backend
• Custom SM-2 spaced repetition engine
• Event-driven architecture with Redis & queues
• Tri-database design for scale & performance
• Real-time gamification & analytics

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Built for learning. Engineered for scale.