{"id":32745806,"url":"https://github.com/aalexuser/ai-micro-project-generator","last_synced_at":"2026-02-21T05:04:29.677Z","repository":{"id":313927152,"uuid":"1050618454","full_name":"AaLexUser/AI-micro-project-generator","owner":"AaLexUser","description":"Project as part of the AI Product Hack track Yandex#6","archived":false,"fork":false,"pushed_at":"2025-10-03T16:55:57.000Z","size":2167,"stargazers_count":1,"open_issues_count":7,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-10-03T18:46:25.479Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AaLexUser.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-09-04T17:29:06.000Z","updated_at":"2025-10-03T16:56:02.000Z","dependencies_parsed_at":"2025-09-09T16:40:46.332Z","dependency_job_id":"bf4dc4d2-d604-42be-b494-41748e4e375d","html_url":"https://github.com/AaLexUser/AI-micro-project-generator","commit_stats":null,"previous_names":["aalexuser/ai-micro-project-generator"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/AaLexUser/AI-micro-project-generator","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AaLexUser%2FAI-micro-project-generator","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AaLexUser%2FAI-micro-project-generator/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AaLexUser%2FAI-micro-project-generator/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AaLexUser%2FAI-micro-project-generator/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AaLexUser","download_url":"https://codeload.github.com/AaLexUser/AI-micro-project-generator/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AaLexUser%2FAI-micro-project-generator/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":282494912,"owners_count":26678637,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-11-03T02:00:05.676Z","response_time":108,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-11-03T17:06:07.038Z","updated_at":"2025-11-03T17:06:07.889Z","avatar_url":"https://github.com/AaLexUser.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🚀 AI Micro-Project Generator\n\n\u003cdiv align=\"center\"\u003e\n\n[![Python Version](https://img.shields.io/badge/python-3.12+-blue.svg)](https://python.org)\n[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)\n[![Build Status](https://img.shields.io/badge/build-passing-brightgreen.svg)]()\n[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/AaLexUser/AI-micro-project-generator)\n\n*Transform learning challenges into focused, hands-on micro-projects with AI*\n\n[🎯 Features](#-features) • [🚀 Quick Start](#-quick-start) • [📖 Documentation](#-documentation) • [🐳 Docker](#-docker-deployment) • [🛠️ Development](#%EF%B8%8F-development)\n\n\u003c/div\u003e\n\n---\n\n## 🎯 What It Does\n\nThe AI Micro-Project Generator is an intelligent educational tool that transforms error descriptions, learning challenges, or coding issues into **structured, bite-sized learning projects**. Perfect for educators, students, and developers who want to learn from their mistakes through practical, hands-on experience.\n\n\nhttps://github.com/user-attachments/assets/8882d5fb-5818-487c-8a88-29610b79e405\n\n\n### ✨ Key Features\n\n- **🎓 Smart Project Generation**: Converts issue descriptions into structured learning tasks\n- **🔍 AI-Powered Analysis**: Uses advanced LLMs to understand and categorize problems\n- **⚡ Safe Code Execution**: Sandboxed Python environment with preinstalled libraries\n- **📝 Detailed Feedback**: Get personalized feedback on your solutions\n- **🎨 Beautiful Web Interface**: Modern React-based frontend with Tailwind CSS\n- **🔧 Flexible Configuration**: Extensive customization through YAML configs\n- **🐳 Production Ready**: Complete Docker setup for easy deployment\n\n### 🎯 Generated Projects Include\n\n- **📋 Task Description**: Clear, focused learning objectives\n- **✅ Success Criteria**: Measurable outcomes for completion\n- **👨‍💻 Expert Solution**: Reference implementation and guidance\n- **🔄 Interactive Feedback**: AI-powered code review and suggestions\n\n---\n\n## 🚀 Quick Start\n\n### Prerequisites\n\nBefore you begin, ensure you have:\n\n- **Python 3.12+** installed\n- **[uv](https://github.com/astral-sh/uv)** package manager\n- **Docker** (for sandbox execution and deployment)\n- **Node.js 18+** (for frontend development)\n\n### ⚡ Installation\n\n1. **Clone the repository**\n```bash\ngit clone https://github.com/AaLexUser/AI-micro-project-generator.git\ncd AI-micro-project-generator\n```\n\n2. **Install dependencies**\n```bash\nuv sync\n```\n\n3. **Set up configuration**\n```bash\ncp .env_example .env\n```\n\n4. **Configure your environment**\n```bash\n# Edit .env with your API keys\nvim .env\n```\n\n### 🎮 Basic Usage\n\n**Generate a micro-project from an issue description:**\n```bash\n# Simple usage\nuv run aipg \"I keep mixing up Python list comprehensions with map/filter\"\n\n# With custom configuration\nuv run aipg --config-path custom.yaml \"My function returns None instead of expected value\"\n\n# Override specific config values\nuv run aipg -o llm.model_name=\"gpt-4\" \"Database connection fails with timeout\"\n```\n\n**Start the API server:**\n```bash\nuv run python -m aipg.api\n# Server runs at http://localhost:8000\n```\n\n**Launch the frontend:**\n```bash\ncd frontend\nnpm install\nnpm run dev\n# Frontend runs at http://localhost:5173\n```\n\n---\n\n## 📖 API Documentation\n\n### 🔗 Endpoints\n\nThe FastAPI server exposes the following endpoints:\n\n| Method | Endpoint | Description |\n|--------|----------|-------------|\n| `POST` | `/projects` | Generate micro-projects from issue descriptions |\n| `POST` | `/feedback` | Get AI feedback on user solutions |\n| `GET` | `/health` | Health check endpoint |\n\n### 📝 API Examples\n\n**Generate Projects:**\n```bash\ncurl -X POST \"http://localhost:8000/projects\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"comments\": [\n      \"I struggle with async/await in Python\",\n      \"My recursive function causes stack overflow\"\n    ]\n  }'\n```\n\n**Get Feedback:**\n```bash\ncurl -X POST \"http://localhost:8000/feedback\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"project\": {...},\n    \"user_solution\": \"def fibonacci(n): return n if n \u003c= 1 else fibonacci(n-1) + fibonacci(n-2)\"\n  }'\n```\n\n---\n\n### 🌐 Environment Variables\n\n| Variable | Description | Default |\n|----------|-------------|---------|\n| `AIPG_LLM_MODEL` | LLM model name | `openai/gpt-4o` |\n| `AIPG_LLM_API_KEY` | API key for LLM | - |\n| `AIPG_SANDBOX_DOCKER_IMAGE` | Sandbox Docker image | `aipg-sandbox:latest` |\n| `LANGFUSE_PUBLIC_KEY` | Langfuse public key | - |\n| `LANGFUSE_SECRET_KEY` | Langfuse secret key | - |\n| `LANGFUSE_HOST` | Langfuse host URL | `https://cloud.langfuse.com` |\n| `ENVIRONMENT` | Runtime environment | `production` |\n| `DEBUG` | Debug mode flag | `false` |\n| `LOG_LEVEL` | Logging level | `INFO` |\n\n**Docker Environment Setup:**\n```bash\n# Create .env file for Docker deployment\ncp .env_example .env\n\n# Edit with your API keys\nvim .env\n```\n\n---\n\n## 🐳 Docker Deployment\n\n### 🚀 Quick Deployment\n\n**Production deployment:**\n```bash\n# Start all services with health checks and volumes\ndocker compose up -d\n\n# Check service health\ndocker compose ps\n```\n\n**Development with hot reload:**\n```bash\n# Start with development overrides\ndocker compose -f docker-compose.yml -f docker-compose.dev.yml up\n\n# API available at: http://localhost:8000\n# Frontend available at: http://localhost:5173 (dev) or http://localhost:80\n```\n\n**Production with optimizations:**\n```bash\n# Start with production optimizations and scaling\ndocker compose -f docker-compose.yml -f docker-compose.prod.yml up -d\n```\n\n### 🏗️ Service Architecture\n\nThe deployment includes three interconnected services:\n\n- **🔧 API Service** (port 8000, internal) - FastAPI backend with LLM integrations\n  - Health checks and dependency management\n  - Persistent volumes for data and cache\n  - Non-root user security\n\n- **🎨 Frontend Service** (port 80) - React app with Nginx\n  - SPA routing and API proxy\n  - Gzip compression and security headers\n  - Production-optimized build\n\n- **🛡️ Sandbox Service** (internal) - Secure Python execution environment\n  - Read-only filesystem and dropped capabilities\n  - Network isolation and resource limits\n  - Preinstalled ML libraries (pandas, numpy, torch, etc.)\n\n### 📁 Data Persistence\n\nVolumes are automatically created for:\n- `api_data` - Application data and configurations\n- `cache_data` - ChromaDB vector database and LLM caches\n\n```bash\n# View volumes\ndocker volume ls | grep ai-micro-project-generator\n\n# Backup data\ndocker run --rm -v ai-micro-project-generator_api_data:/data \\\n  -v $(pwd):/backup alpine tar czf /backup/api_backup.tar.gz -C /data .\n```\n\n### 🔧 Docker Compose Files\n\n- **`docker-compose.yml`** - Main configuration with health checks and security\n- **`docker-compose.dev.yml`** - Development overrides with hot reload\n- **`docker-compose.prod.yml`** - Production optimizations and scaling\n- **`DOCKER.md`** - Comprehensive deployment guide\n\n### 🛠️ Build Commands\n\n```bash\n# Build all images\nmake docker-build\n\n# Build specific services\ndocker compose build api\ndocker compose build frontend\ndocker compose build sandbox\n\n# Force rebuild without cache\ndocker compose build --no-cache\n```\n\n### 🔍 Monitoring \u0026 Debugging\n\n```bash\n# View service status and health\ndocker compose ps\n\n# Stream logs\ndocker compose logs -f api\ndocker compose logs -f --tail=100\n\n# Access running containers\ndocker compose exec api bash\ndocker compose exec sandbox python\n\n# Restart services\ndocker compose restart api\n```\n\n### 🚨 Quick Troubleshooting\n\n**Services won't start:**\n```bash\n# Check logs for errors\ndocker compose logs api\ndocker compose logs frontend\n\n# Verify environment file\ncat .env\n\n# Check port conflicts\nsudo lsof -i :80 -i :8000\n```\n\n**API health check failing:**\n```bash\n# Test API directly\ncurl http://localhost:8000/health\n\n# Check API logs\ndocker compose logs -f api\n\n# Restart API service\ndocker compose restart api\n```\n\n**Frontend not loading:**\n```bash\n# Check nginx configuration\ndocker compose exec frontend cat /etc/nginx/conf.d/default.conf\n\n# Test frontend container\ndocker compose exec frontend wget -qO- http://localhost/\n```\n\n**Sandbox execution issues:**\n```bash\n# Test sandbox directly\ndocker compose exec sandbox python -c \"import pandas; print('OK')\"\n\n# Check sandbox security settings\ndocker compose exec --user root sandbox ls -la /home/sandbox\n```\n\n**Volume permission issues:**\n```bash\n# Fix API data permissions\ndocker compose exec --user root api chown -R app:app /app/data\n\n# Reset volumes (⚠️ data loss)\ndocker compose down -v\ndocker compose up -d\n```\n\n\u003e 📘 **Need more help?** Check the comprehensive [DOCKER.md](DOCKER.md) guide for detailed troubleshooting and configuration options.\n\n---\n\n## 🛠️ Development\n\n### 🏗️ Setup Development Environment\n\n```bash\n# Install with development dependencies\nuv sync --group dev\n\n# Install pre-commit hooks\nmake pre-commit-install\n\n# Run all quality checks\nmake quality\n```\n\n### 🧪 Code Quality \u0026 Testing\n\n```bash\n# Run linting\nmake lint\n\n# Auto-fix linting issues\nmake lint-fix\n\n# Format code\nmake format\n\n# Run tests\nuv run pytest\n\n# Run tests with coverage\nuv run pytest --cov=aipg\n\n# Run specific test categories\nuv run pytest -m unit\nuv run pytest -m integration\n```\n\n### 📁 Project Architecture\n\n```\naipg/\n├── 🎯 assistant.py           # Main AI assistant orchestration\n├── 🔌 api.py                # FastAPI web server\n├── 🧠 llm.py                # LLM client abstractions\n├── 📊 domain.py             # Core data models\n├── ⚙️ configs/              # Configuration management\n│   ├── app_config.py        # Application config schema\n│   ├── loader.py            # Config loading logic\n│   └── default.yaml         # Default configuration\n├── 🎨 prompting/            # AI prompt templates\n│   ├── project_generator.md # Project generation prompts\n│   ├── feedback.md          # Feedback generation prompts\n│   └── prompt_generator.py  # Prompt building utilities\n├── 🔍 rag/                  # Retrieval-Augmented Generation\n│   ├── service.py           # RAG orchestration\n│   ├── adapters.py          # Vector database adapters\n│   └── ports.py             # RAG interfaces\n├── 🔒 sandbox/              # Safe code execution\n│   ├── service.py           # Sandbox orchestration\n│   ├── adapters.py          # Docker integration\n│   └── domain.py            # Execution result models\n└── 🤖 task_inference/       # AI task processing pipeline\n    └── task_inference.py    # Main inference logic\n```\n\n### 🔄 Assistant Pipeline Architecture\n\nThe system consists of two separate AI assistants that handle different phases of the workflow:\n\n#### 📋 Phase 1: Project Generation (ProjectAssistant)\n\n```mermaid\ngraph TD\n    %% Input Layer\n    A[User Comments/Issues] --\u003e B[ProjectAssistant]\n\n    %% ProjectAssistant Pipeline\n    B --\u003e E[DefineTopicsInference]\n\n    %% Parallel Processing Box - For Each Topic\n    E --\u003e ParallelBox\n\n    subgraph ParallelBox [\" 🔄 Parallel Execution (For Each Topic) \"]\n        H[RAGServiceInference]\n        H --\u003e I{Candidates Found?}\n        I --\u003e|Yes| J[LLMRankerInference]\n        I --\u003e|No| K[ProjectGenerationInference]\n        J --\u003e L{Best Project Selected?}\n        L --\u003e|No| K\n        L --\u003e|Yes| M[Project Found]\n\n        %% Project Generation \u0026 Validation Branch\n        K --\u003e N[ProjectValidatorInference]\n        N --\u003e O{Valid Project?}\n        O --\u003e|No| P[ProjectCorrectorInference]\n        P --\u003e Q{Correction Successful?}\n        Q --\u003e|Yes| N\n        Q --\u003e|No| R[Use Previous Version]\n        O --\u003e|Yes| S[CheckAutotestSandboxInference]\n\n        %% Bug Fixing Loop\n        S --\u003e T{Bugs Detected?}\n        T --\u003e|Yes| U[BugFixerInference]\n        U --\u003e S\n        T --\u003e|No| V[Save to RAG]\n        R --\u003e V\n        M --\u003e V\n    end\n\n    %% Final Output\n    ParallelBox --\u003e W[Projects Generated]\n    W --\u003e X[🏁 ProjectAssistant Ends]\n\n    %% Styling\n    classDef assistantClass fill:#e1f5fe,stroke:#01579b,stroke-width:2px\n    classDef inferenceClass fill:#f3e5f5,stroke:#4a148c,stroke-width:2px\n    classDef decisionClass fill:#fff3e0,stroke:#e65100,stroke-width:2px\n    classDef outputClass fill:#e8f5e8,stroke:#2e7d32,stroke-width:2px\n    classDef sandboxClass fill:#ffebee,stroke:#c62828,stroke-width:2px\n    classDef endClass fill:#ffcdd2,stroke:#d32f2f,stroke-width:3px\n\n    class B assistantClass\n    class E,H,J,K,N,P,U inferenceClass\n    class I,L,O,Q,T decisionClass\n    class W outputClass\n    class S sandboxClass\n    class X endClass\n\n    %% Parallel Box Styling\n    style ParallelBox fill:#f0f8ff,stroke:#4169e1,stroke-width:3px,stroke-dasharray: 5 5\n```\n\n#### 🔄 Phase 2: Feedback Generation (FeedbackAssistant)\n\n```mermaid\ngraph TD\n    %% Input Layer - New Agent Starts\n    A[🚀 FeedbackAssistant Starts] --\u003e B[User Solution Input]\n    C[Generated Projects] --\u003e D[Project Context Available]\n\n    %% FeedbackAssistant Pipeline\n    B --\u003e E[CheckUserSolutionSandboxInference]\n    D --\u003e E\n    E --\u003e F[Execute AutoTests]\n    F --\u003e G[FeedbackInference]\n    G --\u003e H[AI-Generated Feedback]\n\n    %% Styling\n    classDef assistantClass fill:#e1f5fe,stroke:#01579b,stroke-width:2px\n    classDef inferenceClass fill:#f3e5f5,stroke:#4a148c,stroke-width:2px\n    classDef outputClass fill:#e8f5e8,stroke:#2e7d32,stroke-width:2px\n    classDef sandboxClass fill:#ffebee,stroke:#c62828,stroke-width:2px\n    classDef startClass fill:#c8e6c9,stroke:#388e3c,stroke-width:3px\n    classDef contextClass fill:#fff9c4,stroke:#f57f17,stroke-width:2px\n\n    class A startClass\n    class E,G inferenceClass\n    class H outputClass\n    class F sandboxClass\n    class B,C,D contextClass\n```\n\n#### 🔍 Pipeline Components\n\n**Phase 1 - ProjectAssistant Pipeline:**\n1. **DefineTopicsInference** - Extracts learning topics from user comments\n2. **RAGServiceInference** - Searches existing project database for similar topics\n3. **LLMRankerInference** - Ranks and selects best matching projects\n4. **ProjectGenerationInference** - Generates new projects when no matches found\n5. **ProjectValidatorInference** - Validates project structure and content\n6. **ProjectCorrectorInference** - Fixes validation issues (up to 3 attempts)\n7. **CheckAutotestSandboxInference** - Tests project autotests in sandbox\n8. **BugFixerInference** - Fixes bugs found during testing\n9. **🏁 Pipeline Ends** - ProjectAssistant completes with generated projects\n\n**Phase 2 - FeedbackAssistant Pipeline:**\n1. **🚀 New Agent Starts** - FeedbackAssistant initializes with project context\n2. **CheckUserSolutionSandboxInference** - Executes user code safely in sandbox\n3. **FeedbackInference** - Generates personalized feedback based on execution results\n\n#### ⚡ Key Features\n\n- **Two-Phase Architecture**: Separate specialized agents for project generation and feedback\n- **Clear Separation**: ProjectAssistant ends after generating projects, FeedbackAssistant starts fresh\n- **Parallel Processing**: Topics are processed concurrently for better performance\n- **Validation Loop**: Projects undergo multiple validation and correction cycles\n- **Bug Detection**: Automated testing and fixing of generated project code\n- **Safe Execution**: All code runs in isolated Docker containers\n- **RAG Integration**: Leverages existing project database to avoid duplication\n\n### 🔒 Sandbox System\n\nThe sandbox provides secure Python code execution with preinstalled libraries:\n\n**📦 Preinstalled Libraries:**\n- `pandas` - Data manipulation and analysis\n- `numpy` - Numerical computing\n- `torch` - Machine learning framework\n- `scikit-learn` - Machine learning library\n- `matplotlib` - Plotting and visualization\n- `requests` - HTTP client library\n- `beautifulsoup4` - HTML/XML parsing\n- `lxml` - XML processing\n\n**🛡️ Security Features:**\n- Network isolation (`--network none`)\n- Read-only filesystem\n- Memory and CPU limits\n- Process limits\n- Non-root user execution\n\n**📝 Usage Example:**\n```python\nfrom aipg.sandbox.builder import build_sandbox_service\nfrom aipg.configs.app_config import AppConfig\n\n# Initialize sandbox\nconfig = AppConfig()\nservice = build_sandbox_service(config)\n\n# Execute code safely\nresult = service.run_code(\"\"\"\nimport pandas as pd\nimport numpy as np\n\ndf = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})\nprint(f'DataFrame shape: {df.shape}')\n\narr = np.array([1, 2, 3, 4, 5])\nprint(f'Array sum: {arr.sum()}')\n\"\"\")\n\nprint(result.stdout)  # Output: DataFrame shape: (3, 2)\\nArray sum: 15\n```\n\n### 🎨 Frontend Development\n\nThe frontend is built with modern React and includes:\n\n**🛠️ Tech Stack:**\n- **React 18** with TypeScript\n- **Tailwind CSS** for styling\n- **Radix UI** for accessible components\n- **React Router** for navigation\n- **React Hook Form** for form handling\n- **Vite** for fast development\n\n**🚀 Development Commands:**\n```bash\ncd frontend\n\n# Start development server\nnpm run dev\n\n# Build for production\nnpm run build\n\n# Preview production build\nnpm run preview\n\n# Point to different API\nVITE_API_BASE=http://localhost:8000 npm run dev\n```\n\n### 🔧 Available Make Commands\n\n| Command | Description |\n|---------|-------------|\n| `make help` | Show available commands |\n| `make quality` | Run all quality checks |\n| `make lint` | Run linting with ruff |\n| `make lint-fix` | Auto-fix linting issues |\n| `make format` | Format code and organize imports |\n| `make docker-build` | Build all Docker images |\n| `make pre-commit` | Install and run pre-commit hooks |\n\n---\n\n## 🤝 Contributing\n\nWe welcome contributions! Please follow these steps:\n\n1. **Fork the repository**\n2. **Create a feature branch**: `git checkout -b feature/amazing-feature`\n3. **Install dependencies**: `uv sync --group dev`\n4. **Make your changes** and add tests\n5. **Run quality checks**: `make quality`\n6. **Commit your changes**: `git commit -m 'Add amazing feature'`\n7. **Push to your fork**: `git push origin feature/amazing-feature`\n8. **Create a Pull Request**\n\n### 📋 Development Guidelines\n\n- Follow the existing code style (enforced by `ruff`)\n- Add tests for new functionality\n- Update documentation as needed\n- Ensure all CI checks pass\n\n---\n\n## 🎓 About\n\nThis project was created as part of the **AI Product Hack track Yandex#6**, demonstrating practical application of AI in educational technology for creating personalized learning experiences.\n\n### 🏆 Key Achievements\n\n- **🎯 Intelligent Learning**: Transforms errors into learning opportunities\n- **🔒 Safe Execution**: Secure sandbox for code testing\n- **🎨 Modern UI/UX**: Beautiful, responsive interface\n- **🚀 Production Ready**: Complete deployment solution\n- **📈 Scalable Architecture**: Modular, extensible design\n\n---\n\n## 📄 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n---\n\n\u003cdiv align=\"center\"\u003e\n\n**[⬆ Back to Top](#-ai-micro-project-generator)**\n\nMade with ❤️ by the AIPG Team\n\n\u003c/div\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faalexuser%2Fai-micro-project-generator","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faalexuser%2Fai-micro-project-generator","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faalexuser%2Fai-micro-project-generator/lists"}