⚡ LeetCode Tracker

A local web app for tracking coding interview prep with spaced repetition (Leitner system). Built with Flask + SQLite.

Instead of randomly grinding problems, this tracker schedules your daily review queue using science-backed techniques — so you spend most of your time on problems you're weakest at.

Features

• 📅 Daily Review Queue — Leitner spaced repetition tells you exactly what to practice today
• 📊 Dashboard — stats, topic heatmap, confidence-vs-reality calibration chart
• 🤖 AI Feedback — log solutions via GitHub Copilot CLI for scoring, complexity analysis, and improvement tips
• 📈 Progress Tracking — per-problem attempt history with confidence trends and solution diffs
• 🔌 REST API — log attempts programmatically from scripts or CLI tools
• 75 problems pre-loaded from the LeetCode 75 list

How the Leitner System Works

Every problem lives in a box (1–5). Get it right → moves up (reviewed less often). Get it wrong → back to Box 1.

Box	Review Interval	Meaning
1	Every day	New or struggling
2	Every 2 days	Starting to learn
3	Every 4 days	Getting comfortable
4	Every 8 days	Nearly mastered
5	Every 16 days	Mastered

Quick Start

# Clone
git clone https://github.com/diegoruv20/leetcode-tracker.git
cd leetcode-tracker

# Setup
python -m venv venv
.\venv\Scripts\Activate.ps1   # Windows
# source venv/bin/activate    # macOS/Linux
pip install -r requirements.txt

# Seed the database with LeetCode 75 problems
python seed.py

# Run
python app.py

Open http://127.0.0.1:5000

Logging Attempts

Via the Web UI

Go to /log, select a problem, enter your results, and paste your solution.

Via Copilot CLI (recommended)

Paste your solution in a Copilot CLI session. Copilot will:

1. Analyze your code for correctness, complexity, and code quality
2. Quiz you on time/space complexity
3. Score it 1–10 with detailed feedback
4. POST everything to the tracker API automatically

Via API

curl -X POST http://127.0.0.1:5000/api/attempts \
  -H "Content-Type: application/json" \
  -d '{
    "problem_id": 1,
    "passed": true,
    "time_taken_minutes": 15,
    "confidence": 4,
    "solution_code": "def twoSum(nums, target): ...",
    "ai_score": 8,
    "ai_feedback": "Clean solution using hash map.",
    "complexity_score": 5
  }'

API Reference

Method	Endpoint	Description
GET	/api/problems	List problems (filters: category, topic, difficulty, box, status)
GET	/api/problems/<id>	Problem detail with all attempts
GET	/api/due	Today's Leitner review queue
POST	/api/attempts	Log an attempt (auto-updates Leitner box)
GET	/api/stats	Dashboard statistics
GET	/api/topics	Per-topic analytics

Built With

• Flask + SQLAlchemy — backend
• Chart.js — visualizations
• GitHub Copilot CLI — AI feedback integration