Learn to spot AI-generated content through engaging gameplay and contribute to vital research in AI literacy.
✅ FULLY AUTOMATED DEVELOPMENT - This entire project was built using Claude Code SPARC mode, an agentic workflow for automated software development created by Reuven Cohen (@rUv). This implementation serves as a comprehensive test-drive of Cohen's innovative SPARC methodology integrated with Anthropic's Claude Code CLI.
The SPARC methodology (Specification, Pseudocode, Architecture, Refinement, Completion) breaks down complex development into manageable phases handled by specialized AI assistants. Created by Reuven Cohen (GitHub: @ruvnet), this approach enables:
- Parallel Development Tracks: Backend, frontend, and infrastructure built concurrently
- Test-Driven Development: London School TDD with comprehensive coverage
- Automated Research: Web research and competitive analysis phases
- Multi-Agent Coordination: Sophisticated AI collaboration patterns
- Quality Assurance: Automated linting, testing, and security validation
This ThinkRank project demonstrates SPARC's capabilities in practice:
- Phase 0 (Research): Automated web research on AI detection, gaming platforms, and tech stacks
- Phase 1 (Specification): Requirements analysis and user story generation
- Phase 2 (Pseudocode): High-level architecture and algorithm design
- Phase 3 (Architecture): Detailed component, data, and infrastructure design
- Phase 4 (Refinement): TDD implementation across multiple parallel tracks
- Phase 5 (Completion): Integration, documentation, and deployment readiness
Results Achieved:
- Frontend: React app with complete game components, Redux state management, and API integration
- Backend: Microservices architecture with AI service, Auth service, Game service, and Social service
- Infrastructure: Docker containers, Kubernetes manifests, monitoring, and logging configurations
- Tests: 183 backend tests + 109 frontend tests - all passing ✅
- Documentation: Complete API docs, architecture guides, and deployment instructions
This project is built upon the pioneering work of Reuven Cohen and his SPARC methodology. Cohen's vision of "blending human creativity with machine intelligence" through structured AI-assisted development is fully realized in this implementation. The foundational concepts of specialized AI agents, boomerang task orchestration, and multi-phase development coordination all originate from his innovative framework.
Learn More: Explore Cohen's work at github.com/ruvnet and the SPARC framework at github.com/ruvnet/sparc.
ThinkRank is a pioneering platform at the intersection of gamified learning, citizen science, and AI literacy education. It addresses the critical challenge of identifying AI-generated content by empowering users to develop their detection skills in an interactive environment. Our core mission is to help bridge the gap in AI content detection – a field that is not yet an exact science – through engaging, game-like experiences, inspired by the success of citizen science projects like McGill's Phylo game.
Our Vision: To be the leading platform for advancing AI literacy globally, fostering a community where users learn to critically evaluate AI-generated content and contribute to cutting-edge research.
Our Mission: ThinkRank's core mission is to create a "gamified" platform that helps users learn to spot AI-generated language and images. We aim to make AI literacy accessible and engaging, transforming complex detection challenges into interactive experiences. By participating, users not only enhance their own skills but also contribute valuable data to the ongoing research in AI content identification, helping to address the nuances of an evolving digital landscape.
Potential Roadmap & Development Ideas: https://www.perplexity.ai/search/persona-you-are-an-advanced-ai-7fIpbB0PT6a775_pBgRr8g
ThinkRank is a "gamified" platform designed to enhance AI literacy by teaching users to identify AI-generated language and images. It's an ecosystem where users engage in game-like challenges, improve their detection skills, and contribute to valuable research in AI content identification. Our platform aims to make learning about AI engaging, accessible, and impactful, drawing inspiration from successful citizen science projects.
- Gamified AI Literacy: Making learning to spot AI-generated content engaging and fun.
- Citizen Science: Empowering users to contribute to real AI detection research.
- Bridging the Gap: Addressing the current limitations in AI content detection through education and crowdsourced data.
- Ethical AI Awareness: Promoting critical thinking about AI-generated content and its implications.
- Community-Driven Learning: Fostering a collaborative environment for skill development and knowledge sharing.
- Core Focus: Developing robust AI detection algorithms for text and images, integrated into an engaging gamified experience.
- Platform: Cross-platform accessibility (initially web-focused prototype, with mobile considerations for future scaling) leveraging modern web technologies.
- Technology Stack: Emphasis on Python for AI/ML, React/TypeScript for frontend, and Node.js/Express or FastAPI for backend services, with Supabase for real-time features and data management.
- Data & Research: Systems for content management, user progression tracking, and data aggregation to support citizen science research contributions.
- User Experience: Intuitive UI/UX designed for learning, engagement, and clear feedback, incorporating accessibility (WCAG) principles.
ThinkRank/
├── client/ # Unity mobile client
│ ├── unity-project/ # Unity 2023.3 LTS project
│ ├── mobile-builds/ # Platform-specific builds
│ └── build-scripts/ # Automated build scripts
├── backend/ # Backend microservices
│ ├── services/ # Individual microservices
│ ├── shared/ # Shared libraries and types
│ └── tests/ # Integration and performance tests
├── infrastructure/ # Infrastructure as Code
│ ├── terraform/ # AWS infrastructure
│ ├── docker/ # Container configurations
│ └── kubernetes/ # K8s deployments
├── deployment/ # CI/CD and monitoring
│ ├── ci-cd/ # GitHub Actions workflows
│ └── monitoring/ # Observability stack
└── documentation/ # Project documentation
Ensure your development environment meets these requirements:
- Node.js 20.10.0+ (LTS recommended)
- Unity 2023.3.0f1 (exact version required)
- Docker 24.0.7+ with Compose V2
- AWS CLI 2.13.0+ (configured with appropriate credentials)
- kubectl 1.28+ (for Kubernetes deployments)
- Terraform 1.6.0+ (for infrastructure management)
-
Clone and Initialize
git clone https://github.com/clduab11/thinkrank.git cd thinkrank npm install -
Environment Configuration
cp .env.example .env # Configure your environment variables: # - SUPABASE_URL and SUPABASE_ANON_KEY # - AWS credentials and region # - Database connection strings # - JWT secrets and API keys
-
Local Development Environment
# Start all backend services npm run dev # In a separate terminal, initialize infrastructure docker-compose -f infrastructure/docker/docker-compose.yml up -d # Verify all services are healthy npm run health-check
-
Unity Client Setup
- Launch Unity Hub
- Add project:
client/unity-project - Open with Unity 2023.3.0f1
- Wait for initial compilation and dependency resolution
# Build and containerize all services
npm run build:production
npm run docker:build:all
# Deploy to Kubernetes cluster
kubectl apply -k infrastructure/kubernetes/overlays/production# iOS Build (requires macOS and Xcode)
npm run unity:build:ios:release
# Android Build
npm run unity:build:android:release
# Automated testing
npm run test:mobile:automatedOur backend architecture exemplifies modern microservices best practices:
- Languages: TypeScript for type safety and developer experience
- Framework: Express.js with domain-driven design principles
- Database Strategy: PostgreSQL for ACID transactions, Supabase for real-time features
- Caching: Redis for session management and performance optimization
- API Design: RESTful APIs with GraphQL for complex data relationships
The mobile client represents cutting-edge game development:
- Version: Unity 2023.3 LTS for stability and long-term support
- UI Framework: UI Toolkit for responsive, performant interfaces
- Networking: Unity Netcode for real-time multiplayer capabilities
- Performance: Advanced profiling and 60fps optimization strategies
- Platform Support: iOS 13+ and Android API 23+ with adaptive UI
- Linting: ESLint + Prettier for consistent TypeScript/JavaScript formatting
- Static Analysis: Unity Code Analysis for C# quality assurance
- Git Workflow: Conventional Commits with semantic versioning
- Branching: GitFlow strategy (main/develop/feature/hotfix)
- Code Review: Mandatory peer review with automated quality gates
# Execute complete test suite
npm test
# Service-specific testing
npm test --workspace=backend/services/auth-service
# Integration testing across services
npm run test:integration
# Performance and load testing
npm run test:performance
# End-to-end user journey testing
npm run test:e2e
# Security vulnerability scanning
npm run test:security# Deploy to development cluster
kubectl apply -k infrastructure/kubernetes/overlays/dev
# Monitor deployment status
kubectl get pods -n thinkrank-dev# Initialize cloud infrastructure
cd infrastructure/terraform
terraform init
terraform plan -var-file="production.tfvars"
terraform apply
# Deploy application stack
kubectl apply -k infrastructure/kubernetes/overlays/production
# Verify deployment health
kubectl get pods -n thinkrank-productionOur observability stack provides comprehensive insights into system health and user experience:
- Metrics Collection: Prometheus for system metrics, custom application metrics
- Visualization: Grafana dashboards for real-time system monitoring
- Logging: ELK Stack (Elasticsearch, Logstash, Kibana) for centralized log management
- Distributed Tracing: Jaeger for request flow analysis across microservices
- Alerting: AlertManager with multi-channel notifications (Slack, email, PagerDuty)
- Mobile Analytics: Unity Analytics integration with custom event tracking
| Service | Port | Purpose | Technology Stack |
|---|---|---|---|
| API Gateway | 3000 | Request routing, rate limiting, authentication | Express.js, Kong |
| Auth Service | 3001 | User authentication, authorization, JWT management | Express.js, Passport.js |
| Game Service | 3002 | Game logic, progression, achievements | Express.js, Redis |
| AI Research Service | 3003 | Research problem distribution, ML model integration | Express.js, TensorFlow.js |
| Social Service | 3004 | Social features, leaderboards, community | Express.js, WebSocket |
| Analytics Service | 3005 | User analytics, performance metrics, insights | Express.js, ClickHouse |
- Container Orchestration: AWS EKS with auto-scaling node groups
- Database: Amazon RDS PostgreSQL with read replicas
- Caching: Amazon ElastiCache Redis cluster
- Storage: Amazon S3 with CloudFront CDN distribution
- Real-time Backend: Supabase for real-time subscriptions and edge functions
- Message Queue: Amazon SQS for asynchronous processing
- Monitoring: AWS CloudWatch with custom metrics and alarms
Security is paramount in our design philosophy:
- Authentication: JWT with secure refresh tokens and multi-factor authentication
- Authorization: Role-based access control (RBAC) with fine-grained permissions
- Data Protection: TLS 1.3 for data in transit, AES-256 encryption at rest
- Privacy Compliance: GDPR and CCPA compliant with user data sovereignty
- Security Scanning: Continuous vulnerability scanning with Snyk and OWASP tools
- Secrets Management: AWS Secrets Manager with automatic rotation
- Network Security: VPC isolation, WAF protection, DDoS mitigation
- Frame Rate: Consistent 60fps (95th percentile) across target devices
- Memory Efficiency: <500MB RAM usage on minimum specification devices
- Launch Performance: <3s cold start, <1s scene transitions
- Battery Optimization: <10% battery drain per hour of active gameplay
- Network Efficiency: Optimized data usage with intelligent caching
- Response Latency: <200ms API response time (99th percentile)
- Throughput: 10,000+ concurrent requests per service instance
- Availability: 99.9% uptime SLA with automatic failover
- Scalability: Horizontal auto-scaling from 2-100 pods per service
- Database Performance: <50ms query response time for 95% of operations
We welcome contributions from developers, researchers, educators, and enthusiasts who share our vision of democratizing AI research participation.
- Fork the Repository: Create your own fork of the ThinkRank repository
- Create a Feature Branch:
git checkout -b feature/your-amazing-feature - Implement Your Changes: Follow our coding standards and best practices
- Write Comprehensive Tests: Ensure your changes include appropriate test coverage
- Commit with Conventional Format:
git commit -m 'feat: add your amazing feature' - Push and Create PR:
git push origin feature/your-amazing-feature
- Code Quality: Adhere to established linting rules and architectural patterns
- Testing Requirements: Maintain >90% test coverage for new features
- Documentation: Update relevant documentation for any new functionality
- Performance: Ensure changes meet our performance benchmarks
- Review Process: All changes require approval from core team members
- Community Standards: Follow our Code of Conduct and be respectful in all interactions
- Game Mechanics: New challenge types and engagement features
- AI Research Integration: Novel approaches to crowdsourced research
- Performance Optimization: Mobile and backend performance improvements
- Accessibility: Features to make the platform more inclusive
- Localization: Multi-language support and cultural adaptation
- Educational Content: Learning resources and tutorial development
- Architecture Deep Dive: Comprehensive system design documentation
- API Reference: Complete API specification and examples
- Development Guide: Detailed setup and development workflows
- Deployment Guide: Production deployment and operations
- Contributing Guide: Detailed contribution guidelines and standards
- Security Guidelines: Security best practices and protocols
- ✅ Complete project architecture and development environment
- 🔄 Core authentication and user management systems
- 🔄 Basic game mechanics and AI research integration
- 🔄 Initial mobile client with core gameplay
- 📝 Advanced bias detection game mechanics
- 📝 Sophisticated research problem distribution system
- 📝 Comprehensive player progression and achievement system
- 📝 Real-time multiplayer capabilities
- 📝 Social media integration and sharing features
- 📝 Community leaderboards and competitive elements
- 📝 Collaborative research challenges
- 📝 Educational institution partnerships
- 📝 Beta testing program with research institutions
- 📝 Performance optimization and scalability testing
- 📝 App store submission and approval process
- 📝 Production deployment and monitoring
- 📝 Advanced AI model integration
- 📝 International expansion and localization
- 📝 Research publication and academic partnerships
- 📝 Platform ecosystem development
ThinkRank exists because of the groundbreaking work and visionary thinking of several key influences that have profoundly shaped our approach to gamified research and educational technology.
I would like to extend my heartfelt gratitude to McGill University for your remarkable work in hosting Phylo, found at https://phylo.cs.mcgill.ca/. Your innovative approach and dedication to advancing scientific understanding through engaging, accessible platforms have been truly inspiring.
Phylo has not only showcased the power of combining education with interactive design but has also served as the foundational inspiration for the development of ThinkRank. The creativity and commitment evident in Phylo have propelled our efforts, shaping the direction and vision of our project.
We also acknowledge the broader community of researchers, educators, and technologists who have contributed to the fields of serious games, crowdsourced research, and AI education. Their collective work has created the intellectual ecosystem that makes ThinkRank possible.
The open-source community, whose collaborative spirit and commitment to knowledge sharing have enabled us to build upon existing tools and frameworks, deserves particular recognition. The principles of transparency, collaboration, and community-driven development that characterize the open-source movement are deeply embedded in ThinkRank's values and operational philosophy.
These inspirations continue to guide our development process as we work to create a platform that honors their legacy while pushing the boundaries of what's possible when games, education, and research converge. ThinkRank represents our commitment to carrying forward the torch of innovation lit by these pioneering efforts, while adapting their insights to address the unique challenges and opportunities of AI research in the 21st century.
This project is licensed under the MIT License - see the LICENSE file for complete details.
The MIT License ensures that ThinkRank remains accessible to researchers, educators, and developers worldwide while encouraging innovation and collaboration within the community.
- 📚 Documentation: docs.thinkrank.com - Comprehensive guides and API documentation
- 🐛 Issues: GitHub Issues - Bug reports and feature requests
- 💬 Discussions: GitHub Discussions - Community Q&A and general discussion
- 📧 Email: support@thinkrank.com - Direct support for critical issues
- 💬 Discord: ThinkRank Community - Real-time community chat and collaboration
Our community is built on principles of respect, inclusivity, and collaborative learning. We welcome contributors from all backgrounds and experience levels who share our passion for advancing AI research through innovative approaches.
Built with 💝 and ⚡ by the ThinkRank Team
Transforming curiosity into discovery, one challenge at a time.