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It provides industry-level expertise through advanced AI models, optimized for infrastructure operations, reliability engineering, and cloud architecture.\n\n### 🌟 Vision\nTo democratize intelligent infrastructure management by providing developers worldwide with AI-driven insights, automation, and best practices - making reliable, scalable infrastructure accessible to everyone.\n\n\u003e 📋 **See our detailed [6-Month Roadmap](ROADMAP.md)** for upcoming features and community goals!\n\n### 🌟 Key Features\n\n- 🤖 **AI-Powered Analysis**: Advanced DevOps/SRE expertise using open source models (gpt-oss:latest)\n- 🏠 **Local Development**: Optimized for local development with Ollama - no cloud dependencies\n- 🎯 **Cursor Integration**: Works as MCP server with Cursor for seamless AI assistance\n- ⚡ **High Performance**: Sub-second response times with intelligent caching\n- 🔓 **Open Source**: MIT licensed, community-driven development\n- 📊 **Multiple Domains**: DevOps, SRE, Cloud Architecture, Platform Engineering expertise\n- 🛠️ **Developer Friendly**: Comprehensive docs, examples, and development tools\n\n## 🏗️ Architecture Overview\n\n\n```mermaid\n\ngraph TB\n    subgraph \"Client Layer\"\n        UI[Web UI]\n        API[REST API]\n        CLI[CLI Tool]\n    end\n    \n    subgraph \"API Gateway\"\n        LB[Load Balancer]\n        AUTH[Authentication]\n        RATE[Rate Limiting]\n    end\n    \n    subgraph \"Application Layer\"\n        MCP1[MCP Server 1]\n        MCP2[MCP Server 2]\n        MCP3[MCP Server N]\n    end\n    \n    subgraph \"AI/ML Layer\"\n        OLLAMA[Ollama Service]\n        MODELS[Fine-tuned Models]\n        CACHE[Model Cache]\n    end\n    \n    subgraph \"Data Layer\"\n        POSTGRES[(PostgreSQL)]\n        REDIS[(Redis Cache)]\n        S3[(Object Storage)]\n    end\n    \n    subgraph \"Infrastructure\"\n        K8S[Kubernetes]\n        DOCKER[Docker]\n        CLOUD[Multi-Cloud]\n    end\n    \n    subgraph \"Monitoring\"\n        PROM[Prometheus]\n        GRAF[Grafana]\n        JAEGER[Jaeger]\n    end\n    \n    UI --\u003e LB\n    API --\u003e LB\n    CLI --\u003e LB\n    \n    LB --\u003e AUTH\n    AUTH --\u003e RATE\n    RATE --\u003e MCP1\n    RATE --\u003e MCP2\n    RATE --\u003e MCP3\n    \n    MCP1 --\u003e OLLAMA\n    MCP2 --\u003e OLLAMA\n    MCP3 --\u003e OLLAMA\n    \n    OLLAMA --\u003e MODELS\n    MODELS --\u003e CACHE\n    \n    MCP1 --\u003e POSTGRES\n    MCP1 --\u003e REDIS\n    MCP2 --\u003e POSTGRES\n    MCP2 --\u003e REDIS\n    MCP3 --\u003e POSTGRES\n    MCP3 --\u003e REDIS\n    \n    POSTGRES --\u003e S3\n    \n    K8S --\u003e CLOUD\n    DOCKER --\u003e K8S\n    \n    MCP1 --\u003e PROM\n    MCP2 --\u003e PROM\n    MCP3 --\u003e PROM\n    PROM --\u003e GRAF\n    MCP1 --\u003e JAEGER\n    \n    %% Client Layer Styling\n    style UI fill:#e3f2fd,stroke:#2196f3,stroke-width:2px\n    style API fill:#e8f5e8,stroke:#4caf50,stroke-width:2px\n    style CLI fill:#fff3e0,stroke:#ff9800,stroke-width:2px\n    \n    %% API Gateway Styling\n    style LB fill:#ffcdd2,stroke:#d32f2f,stroke-width:3px\n    style AUTH fill:#ffab91,stroke:#ff5722,stroke-width:2px\n    style RATE fill:#80cbc4,stroke:#00695c,stroke-width:2px\n    \n    %% Application Layer Styling\n    style MCP1 fill:#90caf9,stroke:#1976d2,stroke-width:3px\n    style MCP2 fill:#a5d6a7,stroke:#388e3c,stroke-width:3px\n    style MCP3 fill:#ffcc80,stroke:#f57c00,stroke-width:3px\n    \n    %% AI/ML Layer Styling\n    style OLLAMA fill:#ce93d8,stroke:#7b1fa2,stroke-width:3px\n    style MODELS fill:#f8bbd9,stroke:#c2185b,stroke-width:2px\n    style CACHE fill:#b39ddb,stroke:#512da8,stroke-width:2px\n    \n    %% Data Layer Styling\n    style POSTGRES fill:#81c784,stroke:#2e7d32,stroke-width:3px\n    style REDIS fill:#ef5350,stroke:#c62828,stroke-width:3px\n    style S3 fill:#ffb74d,stroke:#ef6c00,stroke-width:3px\n    \n    %% Infrastructure Styling\n    style K8S fill:#42a5f5,stroke:#1565c0,stroke-width:3px\n    style DOCKER fill:#29b6f6,stroke:#0277bd,stroke-width:2px\n    style CLOUD fill:#66bb6a,stroke:#2e7d32,stroke-width:2px\n    \n    %% Monitoring Styling\n    style PROM fill:#ff7043,stroke:#d84315,stroke-width:2px\n    style GRAF fill:#ffa726,stroke:#ef6c00,stroke-width:2px\n    style JAEGER fill:#ab47bc,stroke:#6a1b9a,stroke-width:2px\n\n```\n\n## 📁 Project Structure\n\n```\nInfraGenius/\n├── 📁 environments/           # Environment-specific configurations\n│   ├── 📁 test/              # Test environment configs\n│   ├── 📁 staging/           # Staging environment configs\n│   └── 📁 production/        # Production environment configs\n├── 📁 src/                   # Source code\n│   ├── 📁 core/              # Core application logic\n│   ├── 📁 plugins/           # Extensible plugins\n│   └── 📁 ui/                # Web interface\n├── 📁 docker/                # Docker configurations\n│   ├── 📁 development/       # Development containers\n│   └── 📁 production/        # Production containers\n├── 📁 kubernetes/            # K8s manifests\n│   ├── 📁 test/              # Test cluster configs\n│   ├── 📁 staging/           # Staging cluster configs\n│   └── 📁 production/        # Production cluster configs\n├── 📁 docs/                  # Documentation\n│   ├── 📁 architecture/      # Architecture diagrams\n│   ├── 📁 api/               # API documentation\n│   └── 📁 deployment/        # Deployment guides\n├── 📁 tests/                 # Test suites\n│   ├── 📁 unit/              # Unit tests\n│   ├── 📁 integration/       # Integration tests\n│   └── 📁 e2e/               # End-to-end tests\n├── 📁 scripts/               # Automation scripts\n│   ├── 📁 setup/             # Setup and installation\n│   ├── 📁 deploy/            # Deployment automation\n│   └── 📁 utils/             # Utility scripts\n├── 📁 monitoring/            # Monitoring configurations\n│   ├── 📁 grafana/           # Grafana dashboards\n│   └── 📁 prometheus/        # Prometheus configs\n├── 📁 security/              # Security configurations\n├── 📁 backup/                # Backup and recovery\n├── 📁 migrations/            # Database migrations\n├── 📁 examples/              # Usage examples\n├── 📁 tools/                 # Development tools\n└── 📄 README.md              # This file\n```\n\n## 🚀 Quick Start\n\n\u003e **🎯 Focus**: InfraGenius is currently optimized for **local development** with Ollama and open source models. This is perfect for learning, contributing, and building amazing DevOps/SRE solutions locally!\n\n### ⚡ One-Click Local Setup\n\n```bash\n# Clone the repository\ngit clone https://github.com/your-username/infragenius.git\ncd infragenius\n\n# 🚀 One-click setup (installs everything automatically)\n./scripts/quick-local-setup.sh\n\n# 🎉 That's it! Server will start automatically\n# 📊 Health check: http://localhost:8000/health\n# 📚 API docs: http://localhost:8000/docs\n```\n\n### 🛠️ Manual Setup (Step by Step)\n\n#### 1. **Install Ollama** \n```bash\n# macOS\nbrew install ollama\n\n# Linux\ncurl -fsSL https://ollama.ai/install.sh | sh\n\n# Windows\nwinget install ollama\n```\n\n#### 2. **Start Ollama \u0026 Download Model**\n```bash\n# Start Ollama service\nollama serve\n\n# Download AI model (in new terminal)\nollama pull gpt-oss:latest\n\n# Verify model is ready\nollama list\n```\n\n#### 3. **Setup InfraGenius**\n```bash\n# Create Python environment\npython -m venv venv\nsource venv/bin/activate  # On Windows: venv\\Scripts\\activate\n\n# Install dependencies\npip install -r requirements.txt\npip install -r requirements-dev.txt\n\n# Create configuration\ncp mcp_server/config.json.example mcp_server/config.json\n\n# Start InfraGenius\npython mcp_server/server.py\n```\n\n#### 4. **Test Your Setup**\n```bash\n# Test API health\ncurl http://localhost:8000/health\n\n# Test AI analysis\ncurl -X POST http://localhost:8000/analyze \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"prompt\": \"My Kubernetes pods are crashing with OOMKilled errors\", \n    \"domain\": \"devops\",\n    \"context\": \"Production cluster on AWS EKS\"\n  }'\n```\n\n### 🎯 Cursor Integration (MCP Server)\n\nInfraGenius works as an **MCP (Model Context Protocol) server** with Cursor, giving you a specialized DevOps/SRE AI assistant directly in your IDE!\n\n#### **🚀 Quick Setup**\n\n```bash\n# 1. Setup Cursor integration\nmake cursor-setup\n\n# 2. Install MCP dependency\nsource venv/bin/activate\npip install mcp\n\n# 3. Test the integration\npython -m mcp_server.cursor_integration\n```\n\n#### **⚙️ Cursor Configuration**\n\nAdd InfraGenius to your Cursor MCP configuration file at `~/.cursor/mcp.json`:\n\n```json\n{\n  \"mcpServers\": {\n    \"infragenius\": {\n      \"command\": \"\u003cfile_path\u003e/InfraGenius/venv/bin/python\",\n      \"args\": [\n        \"-m\", \"mcp_server.cursor_integration\"\n      ],\n      \"cwd\": \"\u003cfile_path\u003e/InfraGenius\",\n      \"env\": {\n        \"OLLAMA_BASE_URL\": \"http://localhost:11434\",\n        \"OLLAMA_MODEL\": \"gpt-oss:latest\",\n        \"PYTHONPATH\": \"\u003cfile_path\u003e/InfraGenius\"\n      }\n    }\n  }\n}\n```\n\n**📝 Replace `YOUR_USERNAME` with your actual username!**\n\n**💡 Quick Copy**: Use the template at [`examples/cursor-mcp-template.json`](examples/cursor-mcp-template.json) and update the paths.\n\n#### **🔄 Adding to Existing MCP Configuration**\n\nIf you already have other MCP servers configured, just add the `infragenius` entry to your existing `mcpServers` object:\n\n```json\n{\n  \"mcpServers\": {\n    \"existing-server\": {\n      \"command\": \"some-other-mcp-server\",\n      \"args\": [\"...\"]\n    },\n    \"infragenius\": {\n      \"command\": \"\u003cfile_path\u003e/InfraGenius/venv/bin/python\",\n      \"args\": [\"-m\", \"mcp_server.cursor_integration\"],\n      \"cwd\": \"\u003cfile_path\u003e/InfraGenius\",\n      \"env\": {\n        \"OLLAMA_BASE_URL\": \"http://localhost:11434\",\n        \"OLLAMA_MODEL\": \"gpt-oss:latest\"\n      }\n    }\n  }\n}\n```\n\n#### **🎯 Usage in Cursor**\n\nOnce configured, use InfraGenius tools directly in Cursor:\n\n```javascript\n// DevOps Issue Analysis\n@infragenius analyze_devops_issue {\n  \"prompt\": \"My Kubernetes pods are crashing with OOMKilled\",\n  \"context\": \"Production EKS cluster with 50+ microservices\",\n  \"urgency\": \"high\"\n}\n\n// SRE Incident Response  \n@infragenius analyze_sre_incident {\n  \"incident\": \"Database connection pool exhausted\",\n  \"severity\": \"critical\",\n  \"affected_services\": \"user-service, payment-service\"\n}\n\n// Cloud Architecture Review\n@infragenius review_cloud_architecture {\n  \"architecture\": \"3-tier web app on AWS with RDS and ElastiCache\",\n  \"cloud_provider\": \"aws\", \n  \"focus_area\": \"cost\"\n}\n\n// Generate Configurations\n@infragenius generate_config {\n  \"tool\": \"kubernetes\",\n  \"requirements\": \"Redis cluster with persistence and monitoring\",\n  \"environment\": \"production\"\n}\n\n// Log Analysis\n@infragenius explain_logs {\n  \"logs\": \"ERROR: Connection timeout after 30s in database pool\",\n  \"log_type\": \"application\"\n}\n\n// Platform Engineering Advice\n@infragenius platform_engineering_advice {\n  \"challenge\": \"Improve developer onboarding and reduce time-to-first-commit\",\n  \"team_size\": \"30 developers\",\n  \"tech_stack\": \"Node.js, React, Kubernetes, PostgreSQL\"\n}\n```\n\n#### **🛠️ Available Tools**\n\n| Tool | Purpose | Best For |\n|------|---------|----------|\n| 🔧 `analyze_devops_issue` | DevOps problem solving | CI/CD issues, deployment problems |\n| 🚨 `analyze_sre_incident` | Incident response guidance | Outages, performance issues, alerts |\n| ☁️ `review_cloud_architecture` | Architecture analysis | Cost optimization, security, scaling |\n| ⚙️ `generate_config` | Configuration generation | K8s manifests, Docker, Terraform |\n| 📋 `explain_logs` | Log analysis \u0026 debugging | Error investigation, troubleshooting |\n| 🏗️ `platform_engineering_advice` | Platform guidance | Developer experience, internal tools |\n\n#### **✅ Verification Steps**\n\n1. **Restart Cursor** completely after updating mcp.json\n2. **Check MCP Status** in Cursor's settings/extensions\n3. **Test Integration**: Type `@infragenius` in any chat\n4. **Verify Tools**: You should see tool suggestions appear\n\n#### **🔧 Troubleshooting**\n\n**If `@infragenius` doesn't appear:**\n\n```bash\n# Check if integration works\ncd /path/to/InfraGenius\nsource venv/bin/activate\npython -c \"import mcp_server.cursor_integration; print('✅ Integration OK')\"\n\n# Verify Ollama is running\ncurl http://localhost:11434/api/tags\n\n# Check your paths in mcp.json are correct\n```\n\n**Common Issues:**\n- ❌ **Wrong paths** in mcp.json → Update to your actual paths\n- ❌ **Virtual env not activated** → Use full path to venv/bin/python\n- ❌ **Ollama not running** → Start with `ollama serve`\n- ❌ **Model not available** → Download with `ollama pull gpt-oss:latest`\n\n#### **💡 Pro Tips**\n\n- **Combine with other MCP servers** - InfraGenius works alongside other AI models\n- **Use specific tools** - Each tool is optimized for different scenarios\n- **Provide context** - More context = better, more actionable responses\n- **Save configurations** - Generated configs can be saved directly to files\n\n### Docker Deployment\n\n```bash\n# Development environment\ndocker-compose -f docker/development/docker-compose.yml up\n\n# Production environment\ndocker-compose -f docker/production/docker-compose.yml up\n```\n\n### Kubernetes Deployment\n\n```bash\n# Test environment\nkubectl apply -f kubernetes/test/\n\n# Staging environment\nkubectl apply -f kubernetes/staging/\n\n# Production environment\nkubectl apply -f kubernetes/production/\n```\n\n\n\n## 🌍 Environment Management\n\n### Test Environment\n- **Purpose**: Development and testing\n- **Resources**: Minimal (1 CPU, 2GB RAM)\n- **Data**: Synthetic test data\n- **Monitoring**: Basic metrics\n\n```bash\n./scripts/deploy/deploy-test.sh\n```\n\n### Staging Environment\n- **Purpose**: Pre-production validation\n- **Resources**: Production-like (2 CPU, 4GB RAM)\n- **Data**: Anonymized production data\n- **Monitoring**: Full observability stack\n\n```bash\n./scripts/deploy/deploy-staging.sh\n```\n\n### Production Environment\n- **Purpose**: Live customer traffic\n- **Resources**: Auto-scaling (2-20 instances)\n- **Data**: Live production data\n- **Monitoring**: Enterprise monitoring + alerting\n\n```bash\n./scripts/deploy/deploy-production.sh\n```\n\n## 🛠️ Core Features\n\n### 🤖 AI-Powered DevOps Analysis\n\n```python\n# Example API usage\nimport requests\n\nresponse = requests.post('http://localhost:8080/analyze', {\n    \"prompt\": \"My Kubernetes pods are crashing with OOMKilled errors\",\n    \"domain\": \"devops\",\n    \"context\": \"Production cluster with 100+ microservices\"\n})\n\nprint(response.json())\n# Returns detailed analysis with:\n# - Root cause identification\n# - Step-by-step resolution\n# - Prevention strategies\n# - Best practices\n```\n\n### 📊 Comprehensive Domains\n\n| Domain | Features | Use Cases |\n|--------|----------|-----------|\n| **DevOps** | CI/CD, IaC, Automation | Pipeline optimization, deployment strategies |\n| **SRE** | Reliability, Incidents, SLOs | Incident response, reliability improvements |\n| **Cloud** | Architecture, Security, Cost | Cloud migration, cost optimization |\n| **Platform** | Developer Experience, APIs | Platform design, developer productivity |\n\n### ⚡ Performance Features\n\n- **Sub-second response times** with intelligent caching\n- **Auto-scaling** based on demand (2-100 instances)\n- **Multi-level caching** (Redis + in-memory)\n- **Connection pooling** for optimal resource usage\n- **Async processing** for high throughput\n- **Response streaming** for large analyses\n\n### 🔒 Enterprise Security\n\n- **JWT authentication** with refresh tokens\n- **Role-based access control** (RBAC)\n- **Rate limiting** by user tier\n- **API key management**\n- **Audit logging** for compliance\n- **Data encryption** at rest and in transit\n- **Network security** with VPC and firewalls\n\n## 📊 Monitoring \u0026 Observability\n\n### Metrics \u0026 Dashboards\n- **Prometheus** metrics collection\n- **Grafana** dashboards and visualization\n- **Application performance** monitoring\n- **Infrastructure** monitoring\n- **Business metrics** tracking\n- **Custom alerts** and notifications\n\n### Health Checks\n```bash\n# System health\ncurl http://localhost:8080/health\n\n# Detailed health check\ncurl http://localhost:8080/health/detailed\n\n# Metrics endpoint\ncurl http://localhost:8080/metrics\n```\n\n## 🔧 Dependencies\n\n### Core Dependencies\n```json\n{\n  \"runtime\": {\n    \"python\": \"\u003e=3.11\",\n    \"docker\": \"\u003e=20.10\",\n    \"kubernetes\": \"\u003e=1.24\"\n  },\n  \"databases\": {\n    \"postgresql\": \"\u003e=15\",\n    \"redis\": \"\u003e=7.0\"\n  },\n  \"ai_models\": {\n    \"ollama\": \"\u003e=0.1.0\",\n    \"gpt-oss\": \"latest\"\n  },\n  \"monitoring\": {\n    \"prometheus\": \"\u003e=2.40\",\n    \"grafana\": \"\u003e=9.0\"\n  }\n}\n```\n\n### System Requirements\n\n| Component | Minimum | Recommended | Production |\n|-----------|---------|-------------|------------|\n| **CPU** | 2 cores | 4 cores | 8+ cores |\n| **Memory** | 4GB | 8GB | 16+ GB |\n| **Storage** | 20GB | 50GB | 100+ GB |\n| **Network** | 1Mbps | 10Mbps | 100+ Mbps |\n\n## 🚀 Deployment Options\n\n### Local Development\n```bash\n# Quick start for development\n./scripts/setup/local-dev.sh\n\n# With monitoring stack\n./scripts/setup/local-dev.sh --monitoring\n\n# With sample data\n./scripts/setup/local-dev.sh --sample-data\n```\n\n### Cloud Deployment\n\n#### AWS\n```bash\n./scripts/deploy/aws-deploy.sh \\\n  --region us-east-1 \\\n  --environment production \\\n  --instance-type t3.large\n```\n\n#### Google Cloud\n```bash\n./scripts/deploy/gcp-deploy.sh \\\n  --region us-central1 \\\n  --environment production \\\n  --machine-type e2-standard-4\n```\n\n#### Azure\n```bash\n./scripts/deploy/azure-deploy.sh \\\n  --region eastus \\\n  --environment production \\\n  --vm-size Standard_D4s_v3\n```\n\n### On-Premises\n```bash\n./scripts/deploy/on-premises.sh \\\n  --kubernetes-config ~/.kube/config \\\n  --storage-class fast-ssd\n```\n\n## 🧪 Testing\n\n### Unit Tests\n```bash\n# Run all unit tests\npytest tests/unit/\n\n# Run with coverage\npytest tests/unit/ --cov=src --cov-report=html\n```\n\n### Integration Tests\n```bash\n# Run integration tests\npytest tests/integration/\n\n# Test specific service\npytest tests/integration/test_ollama_integration.py\n```\n\n### End-to-End Tests\n```bash\n# Run E2E tests\npytest tests/e2e/\n\n# Run against specific environment\npytest tests/e2e/ --env=staging\n```\n\n### Performance Tests\n```bash\n# Load testing\n./scripts/utils/load-test.sh --concurrent=50 --requests=1000\n\n# Stress testing\n./scripts/utils/stress-test.sh --duration=300s\n```\n\n## 📚 Documentation\n\n### Architecture\n- [System Architecture](docs/architecture/system-design.md)\n- [Database Schema](docs/architecture/database-schema.md)\n- [API Design](docs/architecture/api-design.md)\n- [Security Model](docs/architecture/security-model.md)\n\n### API Documentation\n- [REST API Reference](docs/api/rest-api.md)\n- [WebSocket API](docs/api/websocket-api.md)\n- [Authentication](docs/api/authentication.md)\n- [Rate Limiting](docs/api/rate-limiting.md)\n\n### Deployment\n- [Local Setup](docs/deployment/local-setup.md)\n- [Docker Deployment](docs/deployment/docker.md)\n- [Kubernetes Deployment](docs/deployment/kubernetes.md)\n- [Cloud Deployment](docs/deployment/cloud.md)\n\n## 🤝 Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\n\n### Development Workflow\n1. Fork the repository\n2. Create a feature branch\n3. Make your changes\n4. Add tests\n5. Submit a pull request\n\n### Code Standards\n- **Python**: Follow PEP 8\n- **Testing**: Minimum 80% coverage\n- **Documentation**: Document all public APIs\n- **Commits**: Use conventional commits\n\n## 📄 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## 🆘 Support\n\n### Community Support\n- 📚 [Documentation](docs/)\n- 💬 [GitHub Discussions](https://github.com/aryasoni98/infragenius/discussions)\n- 🐛 [GitHub Issues](https://github.com/aryasoni98/infragenius/issues)\n- 🤝 [Contributing Guide](CONTRIBUTING.md)\n\n## 📊 Performance Benchmarks\n\n| Metric | Performance |\n|--------|------------|\n| **Response Time** | \u003c2s |\n| **Throughput** | 100+ req/s |\n| **Uptime** | 99.5%+ |\n| **Support Response** | Community-driven |\n\n## 🚀 Get Started Today!\n\nChoose your deployment option and get started in minutes:\n\n```bash\n# Quick local setup\ncurl -sSL https://get.# | bash\n\n# Or manual setup\ngit clone https://github.com/your-org/infragenius.git\ncd infragenius\n./scripts/setup/quick-start.sh\n```\n\n**Transform your DevOps operations with AI-powered expertise!** 🎉\n\n---\n\n\u003cdiv align=\"center\"\u003e\n  \u003cstrong\u003eMade with ❤️ for the DevOps community\u003c/strong\u003e\u003cbr\u003e\n  \u003ca href=\"#\"\u003eWebsite\u003c/a\u003e •\n  \u003ca href=\"#\"\u003eDocumentation\u003c/a\u003e •\n  \u003ca href=\"#\"\u003eCommunity\u003c/a\u003e •\n  \u003ca href=\"#\"\u003eContact\u003c/a\u003e\n\u003c/div\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faryasoni98%2Finfragenius","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faryasoni98%2Finfragenius","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faryasoni98%2Finfragenius/lists"}