
# Modern E-commerce Platform

A comprehensive e-commerce solution built with Next.js and TypeScript, featuring real-time inventory management, dynamic pricing, and a seamless checkout experience. The platform is designed to be scalable, performant, and easy to customize.

## Key Features

- Real-time inventory tracking
- Dynamic pricing system
- Secure payment processing
- Order management
- User authentication
- Product search and filtering
- Shopping cart functionality
- Wishlist management
- Admin dashboard
- Analytics integration

## Technical Implementation

The platform is built using modern technologies:

- **Next.js & TypeScript**: For type-safe, server-side rendered pages
- **PostgreSQL**: For reliable data storage
- **Redis**: For caching and session management
- **Stripe**: For secure payment processing
- **TailwindCSS**: For responsive design
- **Vercel**: For deployment and edge functions

## Architecture

The application follows a modular architecture:

1. **Frontend Layer**
   - Server-side rendered pages
   - Client-side state management
   - Responsive components

2. **Backend Services**
   - Product management
   - Order processing
   - User authentication
   - Payment integration
   - Analytics tracking

3. **Data Layer**
   - PostgreSQL for persistent storage
   - Redis for caching
   - Search indexing

## Performance

- 100/100 Lighthouse score
- < 1s page load time
- Optimized images and assets
- Efficient caching strategy
- Global CDN distribution

## Security Features

- HTTPS everywhere
- CSRF protection
- Rate limiting
- Input validation
- Secure payment handling
- Data encryption

## Future Enhancements

- Mobile app development
- AI-powered recommendations
- Advanced analytics
- Multi-vendor support
- International shipping
- AR product preview 

<h1 id="modern-e-commerce-platform">Modern E-commerce Platform</h1>
<p>A comprehensive e-commerce solution built with Next.js and TypeScript, featuring real-time inventory management, dynamic pricing, and a seamless checkout experience. The platform is designed to be scalable, performant, and easy to customize.</p>
<h2 id="key-features">Key Features</h2>
<ul>
<li>Real-time inventory tracking</li>
<li>Dynamic pricing system</li>
<li>Secure payment processing</li>
<li>Order management</li>
<li>User authentication</li>
<li>Product search and filtering</li>
<li>Shopping cart functionality</li>
<li>Wishlist management</li>
<li>Admin dashboard</li>
<li>Analytics integration</li>
</ul>
<h2 id="technical-implementation">Technical Implementation</h2>
<p>The platform is built using modern technologies:</p>
<ul>
<li><strong>Next.js &#x26; TypeScript</strong>: For type-safe, server-side rendered pages</li>
<li><strong>PostgreSQL</strong>: For reliable data storage</li>
<li><strong>Redis</strong>: For caching and session management</li>
<li><strong>Stripe</strong>: For secure payment processing</li>
<li><strong>TailwindCSS</strong>: For responsive design</li>
<li><strong>Vercel</strong>: For deployment and edge functions</li>
</ul>
<h2 id="architecture">Architecture</h2>
<p>The application follows a modular architecture:</p>
<ol>
<li>
<p><strong>Frontend Layer</strong></p>
<ul>
<li>Server-side rendered pages</li>
<li>Client-side state management</li>
<li>Responsive components</li>
</ul>
</li>
<li>
<p><strong>Backend Services</strong></p>
<ul>
<li>Product management</li>
<li>Order processing</li>
<li>User authentication</li>
<li>Payment integration</li>
<li>Analytics tracking</li>
</ul>
</li>
<li>
<p><strong>Data Layer</strong></p>
<ul>
<li>PostgreSQL for persistent storage</li>
<li>Redis for caching</li>
<li>Search indexing</li>
</ul>
</li>
</ol>
<h2 id="performance">Performance</h2>
<ul>
<li>100/100 Lighthouse score</li>
<li>&#x3C; 1s page load time</li>
<li>Optimized images and assets</li>
<li>Efficient caching strategy</li>
<li>Global CDN distribution</li>
</ul>
<h2 id="security-features">Security Features</h2>
<ul>
<li>HTTPS everywhere</li>
<li>CSRF protection</li>
<li>Rate limiting</li>
<li>Input validation</li>
<li>Secure payment handling</li>
<li>Data encryption</li>
</ul>
<h2 id="future-enhancements">Future Enhancements</h2>
<ul>
<li>Mobile app development</li>
<li>AI-powered recommendations</li>
<li>Advanced analytics</li>
<li>Multi-vendor support</li>
<li>International shipping</li>
<li>AR product preview</li>
</ul>

Modern E-commerce Platform

Untitled Project


# Real-Time Analytics Dashboard

A sophisticated analytics platform that provides real-time insights through interactive visualizations. Built with modern web technologies and optimized for handling large-scale data streams.

## Key Features

- Real-time data streaming via WebSocket
- Interactive data visualizations using D3.js
- Custom chart components
- Data filtering and aggregation
- Export capabilities
- User-customizable dashboards
- Responsive design

## Technical Stack

The dashboard leverages cutting-edge technologies:

- **React & TypeScript**: For a robust frontend
- **D3.js**: For powerful data visualizations
- **WebSocket**: For real-time data streaming
- **ClickHouse**: For high-performance analytics
- **Node.js**: For the backend server
- **Redis**: For caching and pub/sub

## Data Visualization

The platform includes various visualization types:

- Line charts with real-time updates
- Heat maps for pattern detection
- Scatter plots with zoom capabilities
- Bar charts with animations
- Area charts for trend analysis
- Custom visualization components

## Performance

- Sub-second data updates
- Smooth animations at 60fps
- Efficient data aggregation
- Optimized rendering
- Minimal memory footprint

## Architecture

The system is built on a modern event-driven architecture:

1. **Data Ingestion Layer**: Handles incoming data streams
2. **Processing Layer**: Aggregates and transforms data
3. **Storage Layer**: Manages data persistence
4. **API Layer**: Provides data access
5. **Visualization Layer**: Renders the UI

## Future Plans

- Machine learning integration
- More visualization types
- Advanced filtering options
- Collaboration features
- Mobile app version 

<h1 id="real-time-analytics-dashboard">Real-Time Analytics Dashboard</h1>
<p>A sophisticated analytics platform that provides real-time insights through interactive visualizations. Built with modern web technologies and optimized for handling large-scale data streams.</p>
<h2 id="key-features">Key Features</h2>
<ul>
<li>Real-time data streaming via WebSocket</li>
<li>Interactive data visualizations using D3.js</li>
<li>Custom chart components</li>
<li>Data filtering and aggregation</li>
<li>Export capabilities</li>
<li>User-customizable dashboards</li>
<li>Responsive design</li>
</ul>
<h2 id="technical-stack">Technical Stack</h2>
<p>The dashboard leverages cutting-edge technologies:</p>
<ul>
<li><strong>React &#x26; TypeScript</strong>: For a robust frontend</li>
<li><strong>D3.js</strong>: For powerful data visualizations</li>
<li><strong>WebSocket</strong>: For real-time data streaming</li>
<li><strong>ClickHouse</strong>: For high-performance analytics</li>
<li><strong>Node.js</strong>: For the backend server</li>
<li><strong>Redis</strong>: For caching and pub/sub</li>
</ul>
<h2 id="data-visualization">Data Visualization</h2>
<p>The platform includes various visualization types:</p>
<ul>
<li>Line charts with real-time updates</li>
<li>Heat maps for pattern detection</li>
<li>Scatter plots with zoom capabilities</li>
<li>Bar charts with animations</li>
<li>Area charts for trend analysis</li>
<li>Custom visualization components</li>
</ul>
<h2 id="performance">Performance</h2>
<ul>
<li>Sub-second data updates</li>
<li>Smooth animations at 60fps</li>
<li>Efficient data aggregation</li>
<li>Optimized rendering</li>
<li>Minimal memory footprint</li>
</ul>
<h2 id="architecture">Architecture</h2>
<p>The system is built on a modern event-driven architecture:</p>
<ol>
<li><strong>Data Ingestion Layer</strong>: Handles incoming data streams</li>
<li><strong>Processing Layer</strong>: Aggregates and transforms data</li>
<li><strong>Storage Layer</strong>: Manages data persistence</li>
<li><strong>API Layer</strong>: Provides data access</li>
<li><strong>Visualization Layer</strong>: Renders the UI</li>
</ol>
<h2 id="future-plans">Future Plans</h2>
<ul>
<li>Machine learning integration</li>
<li>More visualization types</li>
<li>Advanced filtering options</li>
<li>Collaboration features</li>
<li>Mobile app version</li>
</ul>

Real-Time Analytics Dashboard


# Collaborative Task Management App

A modern task management application that enables real-time collaboration between team members. Built with Next.js and WebSocket technology, it provides a seamless experience for managing projects and tasks.

## Key Features

- Real-time updates via WebSocket
- Drag-and-drop task organization
- Team collaboration tools
- Project management
- Task dependencies
- Time tracking
- File attachments
- Comments and discussions
- Custom workflows
- Progress tracking

## Technical Stack

The application is built using modern technologies:

- **Next.js & TypeScript**: For a type-safe, server-rendered frontend
- **WebSocket**: For real-time updates
- **PostgreSQL**: For data persistence
- **Redis**: For caching and pub/sub
- **TailwindCSS**: For responsive design
- **Vercel**: For deployment and scaling

## Architecture

The system follows a modular architecture:

1. **Frontend**
   - Server-side rendered pages
   - Real-time state management
   - Responsive components
   - Offline support

2. **Backend Services**
   - Authentication service
   - Task management service
   - Notification service
   - File storage service
   - Analytics service

3. **Data Layer**
   - PostgreSQL for persistent storage
   - Redis for caching
   - Search indexing

## Performance

- Instant updates
- Offline capability
- Fast page loads
- Efficient data sync
- Mobile optimized

## Collaboration Features

- Real-time editing
- Team chat
- File sharing
- Task assignment
- Progress tracking
- Activity timeline

## Future Plans

- Mobile apps
- Calendar integration
- Advanced reporting
- API access
- Automation tools
- Custom plugins 

<h1 id="collaborative-task-management-app">Collaborative Task Management App</h1>
<p>A modern task management application that enables real-time collaboration between team members. Built with Next.js and WebSocket technology, it provides a seamless experience for managing projects and tasks.</p>
<h2 id="key-features">Key Features</h2>
<ul>
<li>Real-time updates via WebSocket</li>
<li>Drag-and-drop task organization</li>
<li>Team collaboration tools</li>
<li>Project management</li>
<li>Task dependencies</li>
<li>Time tracking</li>
<li>File attachments</li>
<li>Comments and discussions</li>
<li>Custom workflows</li>
<li>Progress tracking</li>
</ul>
<h2 id="technical-stack">Technical Stack</h2>
<p>The application is built using modern technologies:</p>
<ul>
<li><strong>Next.js &#x26; TypeScript</strong>: For a type-safe, server-rendered frontend</li>
<li><strong>WebSocket</strong>: For real-time updates</li>
<li><strong>PostgreSQL</strong>: For data persistence</li>
<li><strong>Redis</strong>: For caching and pub/sub</li>
<li><strong>TailwindCSS</strong>: For responsive design</li>
<li><strong>Vercel</strong>: For deployment and scaling</li>
</ul>
<h2 id="architecture">Architecture</h2>
<p>The system follows a modular architecture:</p>
<ol>
<li>
<p><strong>Frontend</strong></p>
<ul>
<li>Server-side rendered pages</li>
<li>Real-time state management</li>
<li>Responsive components</li>
<li>Offline support</li>
</ul>
</li>
<li>
<p><strong>Backend Services</strong></p>
<ul>
<li>Authentication service</li>
<li>Task management service</li>
<li>Notification service</li>
<li>File storage service</li>
<li>Analytics service</li>
</ul>
</li>
<li>
<p><strong>Data Layer</strong></p>
<ul>
<li>PostgreSQL for persistent storage</li>
<li>Redis for caching</li>
<li>Search indexing</li>
</ul>
</li>
</ol>
<h2 id="performance">Performance</h2>
<ul>
<li>Instant updates</li>
<li>Offline capability</li>
<li>Fast page loads</li>
<li>Efficient data sync</li>
<li>Mobile optimized</li>
</ul>
<h2 id="collaboration-features">Collaboration Features</h2>
<ul>
<li>Real-time editing</li>
<li>Team chat</li>
<li>File sharing</li>
<li>Task assignment</li>
<li>Progress tracking</li>
<li>Activity timeline</li>
</ul>
<h2 id="future-plans">Future Plans</h2>
<ul>
<li>Mobile apps</li>
<li>Calendar integration</li>
<li>Advanced reporting</li>
<li>API access</li>
<li>Automation tools</li>
<li>Custom plugins</li>
</ul>

Collaborative Task Management App


# AI-Powered Customer Service Chatbot

An intelligent customer service chatbot that uses OpenAI's GPT models to provide human-like responses to customer inquiries. The chatbot can handle multiple conversation contexts, understand user intent, and provide relevant information from a knowledge base.

## Key Features

- Natural language understanding using GPT models
- Context-aware conversations
- Knowledge base integration
- Real-time response streaming
- Conversation history management
- Admin dashboard for analytics
- Multi-language support

## Technical Implementation

The chatbot is built using a modern tech stack:

- **Next.js & TypeScript**: For a robust and type-safe frontend
- **OpenAI API**: Powers the natural language understanding
- **Redis**: Manages conversation state and caching
- **TailwindCSS**: For a beautiful and responsive UI
- **Vercel**: For seamless deployment and scaling

## Architecture

The system follows a microservices architecture:

1. **Frontend Service**: Handles user interactions and UI rendering
2. **Chat Service**: Manages conversation flow and context
3. **AI Service**: Interfaces with OpenAI API
4. **Knowledge Base**: Stores and retrieves relevant information
5. **Analytics Service**: Tracks usage and generates insights

## Performance & Scalability

- Average response time < 1 second
- Can handle 1000+ concurrent users
- 99.9% uptime
- Automatic scaling based on load
- Global CDN distribution

## Future Enhancements

- Voice interface integration
- Custom model fine-tuning
- Enhanced analytics dashboard
- Integration with more platforms
- Automated training data generation 

<h1 id="ai-powered-customer-service-chatbot">AI-Powered Customer Service Chatbot</h1>
<p>An intelligent customer service chatbot that uses OpenAI's GPT models to provide human-like responses to customer inquiries. The chatbot can handle multiple conversation contexts, understand user intent, and provide relevant information from a knowledge base.</p>
<h2 id="key-features">Key Features</h2>
<ul>
<li>Natural language understanding using GPT models</li>
<li>Context-aware conversations</li>
<li>Knowledge base integration</li>
<li>Real-time response streaming</li>
<li>Conversation history management</li>
<li>Admin dashboard for analytics</li>
<li>Multi-language support</li>
</ul>
<h2 id="technical-implementation">Technical Implementation</h2>
<p>The chatbot is built using a modern tech stack:</p>
<ul>
<li><strong>Next.js &#x26; TypeScript</strong>: For a robust and type-safe frontend</li>
<li><strong>OpenAI API</strong>: Powers the natural language understanding</li>
<li><strong>Redis</strong>: Manages conversation state and caching</li>
<li><strong>TailwindCSS</strong>: For a beautiful and responsive UI</li>
<li><strong>Vercel</strong>: For seamless deployment and scaling</li>
</ul>
<h2 id="architecture">Architecture</h2>
<p>The system follows a microservices architecture:</p>
<ol>
<li><strong>Frontend Service</strong>: Handles user interactions and UI rendering</li>
<li><strong>Chat Service</strong>: Manages conversation flow and context</li>
<li><strong>AI Service</strong>: Interfaces with OpenAI API</li>
<li><strong>Knowledge Base</strong>: Stores and retrieves relevant information</li>
<li><strong>Analytics Service</strong>: Tracks usage and generates insights</li>
</ol>
<h2 id="performance--scalability">Performance &#x26; Scalability</h2>
<ul>
<li>Average response time &#x3C; 1 second</li>
<li>Can handle 1000+ concurrent users</li>
<li>99.9% uptime</li>
<li>Automatic scaling based on load</li>
<li>Global CDN distribution</li>
</ul>
<h2 id="future-enhancements">Future Enhancements</h2>
<ul>
<li>Voice interface integration</li>
<li>Custom model fine-tuning</li>
<li>Enhanced analytics dashboard</li>
<li>Integration with more platforms</li>
<li>Automated training data generation</li>
</ul>

AI-Powered Customer Service Chatbot


# Image Based Classifier

A real-time image classification system that runs entirely in the browser using TensorFlow.js and MobileNet pre-trained model.

## Features

- Real-time image classification from webcam
- Support for image upload and classification
- Mobile-friendly interface
- No server-side processing required
- Custom model training capabilities
- Performance optimization for mobile devices

## Technologies Used

- TensorFlow.js
- React
- TypeScript
- WebGL acceleration
- Progressive Web App (PWA)

## Implementation Details

The application uses TensorFlow.js to load a pre-trained MobileNet model and perform real-time image classification. The model is optimized for mobile devices and can run efficiently in the browser.

### Key Components

- Model loading and initialization
- Real-time video processing
- Custom model training interface
- Results visualization
- Performance monitoring

## Live Demo

[View Live Demo](https://ml-image-classifier.example.com)

## GitHub Repository

[View Source Code](https://github.com/username/ml-image-classifier) 

<h1 id="image-based-classifier">Image Based Classifier</h1>
<p>A real-time image classification system that runs entirely in the browser using TensorFlow.js and MobileNet pre-trained model.</p>
<h2 id="features">Features</h2>
<ul>
<li>Real-time image classification from webcam</li>
<li>Support for image upload and classification</li>
<li>Mobile-friendly interface</li>
<li>No server-side processing required</li>
<li>Custom model training capabilities</li>
<li>Performance optimization for mobile devices</li>
</ul>
<h2 id="technologies-used">Technologies Used</h2>
<ul>
<li>TensorFlow.js</li>
<li>React</li>
<li>TypeScript</li>
<li>WebGL acceleration</li>
<li>Progressive Web App (PWA)</li>
</ul>
<h2 id="implementation-details">Implementation Details</h2>
<p>The application uses TensorFlow.js to load a pre-trained MobileNet model and perform real-time image classification. The model is optimized for mobile devices and can run efficiently in the browser.</p>
<h3 id="key-components">Key Components</h3>
<ul>
<li>Model loading and initialization</li>
<li>Real-time video processing</li>
<li>Custom model training interface</li>
<li>Results visualization</li>
<li>Performance monitoring</li>
</ul>
<h2 id="live-demo">Live Demo</h2>
<p><a href="https://ml-image-classifier.example.com">View Live Demo</a></p>
<h2 id="github-repository">GitHub Repository</h2>
<p><a href="https://github.com/username/ml-image-classifier">View Source Code</a></p>

ML Image Classifier


# Secure Authentication System

A comprehensive authentication system implementing modern security best practices, including multi-factor authentication, JWT handling, and secure password management.

## Features

- Multi-factor authentication (MFA)
- JWT-based session management
- Password strength enforcement
- Rate limiting and brute force protection
- Secure password reset flow
- OAuth2 integration (Google, GitHub, etc.)
- Audit logging
- Admin dashboard for user management

## Technologies Used

- Node.js & Express
- TypeScript
- PostgreSQL with TypeORM
- Redis for session management
- Docker & Docker Compose
- Jest for testing
- GitHub Actions for CI/CD

## Security Features

- Argon2 password hashing
- CSRF protection
- XSS prevention
- Content Security Policy (CSP)
- Rate limiting
- IP-based blocking
- Security headers (using Helmet)
- Input validation and sanitization

## Implementation Details

The system is built with security as the primary focus, implementing multiple layers of protection:

```typescript
// Example of the authentication flow
interface AuthResponse {
  token: string;
  refreshToken: string;
  mfaRequired: boolean;
}

async function authenticate(
  email: string,
  password: string
): Promise<AuthResponse> {
  // Rate limiting check
  await checkRateLimit(email);

  // Verify credentials
  const user = await verifyCredentials(email, password);

  // Generate tokens
  const token = generateJWT(user);
  const refreshToken = generateRefreshToken(user);

  // Check MFA requirement
  const mfaRequired = await checkMFARequired(user);

  return { token, refreshToken, mfaRequired };
}
```

## Live Demo

[View Live Demo](https://secure-auth-demo.example.com)

## GitHub Repository

[View Source Code](https://github.com/username/secure-auth-system) 

<h1 id="secure-authentication-system">Secure Authentication System</h1>
<p>A comprehensive authentication system implementing modern security best practices, including multi-factor authentication, JWT handling, and secure password management.</p>
<h2 id="features">Features</h2>
<ul>
<li>Multi-factor authentication (MFA)</li>
<li>JWT-based session management</li>
<li>Password strength enforcement</li>
<li>Rate limiting and brute force protection</li>
<li>Secure password reset flow</li>
<li>OAuth2 integration (Google, GitHub, etc.)</li>
<li>Audit logging</li>
<li>Admin dashboard for user management</li>
</ul>
<h2 id="technologies-used">Technologies Used</h2>
<ul>
<li>Node.js &#x26; Express</li>
<li>TypeScript</li>
<li>PostgreSQL with TypeORM</li>
<li>Redis for session management</li>
<li>Docker &#x26; Docker Compose</li>
<li>Jest for testing</li>
<li>GitHub Actions for CI/CD</li>
</ul>
<h2 id="security-features">Security Features</h2>
<ul>
<li>Argon2 password hashing</li>
<li>CSRF protection</li>
<li>XSS prevention</li>
<li>Content Security Policy (CSP)</li>
<li>Rate limiting</li>
<li>IP-based blocking</li>
<li>Security headers (using Helmet)</li>
<li>Input validation and sanitization</li>
</ul>
<h2 id="implementation-details">Implementation Details</h2>
<p>The system is built with security as the primary focus, implementing multiple layers of protection:</p>
<pre><code class="hljs language-typescript"><span class="hljs-comment">// Example of the authentication flow</span>
<span class="hljs-keyword">interface</span> <span class="hljs-title class_">AuthResponse</span> {
  <span class="hljs-attr">token</span>: <span class="hljs-built_in">string</span>;
  <span class="hljs-attr">refreshToken</span>: <span class="hljs-built_in">string</span>;
  <span class="hljs-attr">mfaRequired</span>: <span class="hljs-built_in">boolean</span>;
}

<span class="hljs-keyword">async</span> <span class="hljs-keyword">function</span> <span class="hljs-title function_">authenticate</span>(<span class="hljs-params">
  <span class="hljs-attr">email</span>: <span class="hljs-built_in">string</span>,
  <span class="hljs-attr">password</span>: <span class="hljs-built_in">string</span>
</span>): <span class="hljs-title class_">Promise</span>&#x3C;<span class="hljs-title class_">AuthResponse</span>> {
  <span class="hljs-comment">// Rate limiting check</span>
  <span class="hljs-keyword">await</span> <span class="hljs-title function_">checkRateLimit</span>(email);

  <span class="hljs-comment">// Verify credentials</span>
  <span class="hljs-keyword">const</span> user = <span class="hljs-keyword">await</span> <span class="hljs-title function_">verifyCredentials</span>(email, password);

  <span class="hljs-comment">// Generate tokens</span>
  <span class="hljs-keyword">const</span> token = <span class="hljs-title function_">generateJWT</span>(user);
  <span class="hljs-keyword">const</span> refreshToken = <span class="hljs-title function_">generateRefreshToken</span>(user);

  <span class="hljs-comment">// Check MFA requirement</span>
  <span class="hljs-keyword">const</span> mfaRequired = <span class="hljs-keyword">await</span> <span class="hljs-title function_">checkMFARequired</span>(user);

  <span class="hljs-keyword">return</span> { token, refreshToken, mfaRequired };
}
</code></pre>
<h2 id="live-demo">Live Demo</h2>
<p><a href="https://secure-auth-demo.example.com">View Live Demo</a></p>
<h2 id="github-repository">GitHub Repository</h2>
<p><a href="https://github.com/username/secure-auth-system">View Source Code</a></p>

Secure Authentication System


# GraphQL Blog Platform

A full-featured blog platform demonstrating GraphQL best practices with Next.js and PostgreSQL.

## Features

- GraphQL API with Apollo Server
- Next.js frontend with SSR
- PostgreSQL database with Prisma
- Real-time comments with subscriptions
- Rich text editor integration
- Image upload with cloud storage
- Authentication and authorization
- Admin dashboard

## Technologies Used

- Next.js
- GraphQL (Apollo Server/Client)
- PostgreSQL
- Prisma ORM
- TypeScript
- TailwindCSS
- Jest for testing
- Docker & Docker Compose

## Implementation Details

The platform uses a modern stack with GraphQL at its core, implementing features like:

- Efficient data loading with DataLoader
- Real-time updates with subscriptions
- Proper error handling and validation
- File uploads with cloud storage
- SEO optimization
- Performance monitoring

## Live Demo

[View Live Demo](https://graphql-blog-platform.example.com)

## GitHub Repository

[View Source Code](https://github.com/username/graphql-blog-platform) 

<h1 id="graphql-blog-platform">GraphQL Blog Platform</h1>
<p>A full-featured blog platform demonstrating GraphQL best practices with Next.js and PostgreSQL.</p>
<h2 id="features">Features</h2>
<ul>
<li>GraphQL API with Apollo Server</li>
<li>Next.js frontend with SSR</li>
<li>PostgreSQL database with Prisma</li>
<li>Real-time comments with subscriptions</li>
<li>Rich text editor integration</li>
<li>Image upload with cloud storage</li>
<li>Authentication and authorization</li>
<li>Admin dashboard</li>
</ul>
<h2 id="technologies-used">Technologies Used</h2>
<ul>
<li>Next.js</li>
<li>GraphQL (Apollo Server/Client)</li>
<li>PostgreSQL</li>
<li>Prisma ORM</li>
<li>TypeScript</li>
<li>TailwindCSS</li>
<li>Jest for testing</li>
<li>Docker &#x26; Docker Compose</li>
</ul>
<h2 id="implementation-details">Implementation Details</h2>
<p>The platform uses a modern stack with GraphQL at its core, implementing features like:</p>
<ul>
<li>Efficient data loading with DataLoader</li>
<li>Real-time updates with subscriptions</li>
<li>Proper error handling and validation</li>
<li>File uploads with cloud storage</li>
<li>SEO optimization</li>
<li>Performance monitoring</li>
</ul>
<h2 id="live-demo">Live Demo</h2>
<p><a href="https://graphql-blog-platform.example.com">View Live Demo</a></p>
<h2 id="github-repository">GitHub Repository</h2>
<p><a href="https://github.com/username/graphql-blog-platform">View Source Code</a></p>

GraphQL Blog Platform


# Serverless Media Processor

A cloud-native platform for processing and transforming media files using AWS serverless technologies.

## Features

- Automatic video transcoding
- Image optimization and resizing
- Audio format conversion
- Thumbnail generation
- Metadata extraction
- Progress tracking
- Webhook notifications
- Usage analytics

## Technologies Used

- AWS Lambda
- Amazon S3
- Amazon SQS
- AWS MediaConvert
- DynamoDB
- API Gateway
- CloudWatch
- AWS CDK

## Implementation Details

The platform leverages AWS serverless services to provide scalable media processing:

### Architecture

- S3 for file storage
- Lambda for processing
- SQS for job queuing
- DynamoDB for metadata
- MediaConvert for transcoding
- CloudWatch for monitoring

### Processing Pipeline

1. File upload to S3
2. Trigger Lambda function
3. Queue processing job
4. Process media
5. Store results
6. Send notifications

## Live Demo

[View Live Demo](https://media-processor.example.com)

## GitHub Repository

[View Source Code](https://github.com/username/serverless-media-processor) 

<h1 id="serverless-media-processor">Serverless Media Processor</h1>
<p>A cloud-native platform for processing and transforming media files using AWS serverless technologies.</p>
<h2 id="features">Features</h2>
<ul>
<li>Automatic video transcoding</li>
<li>Image optimization and resizing</li>
<li>Audio format conversion</li>
<li>Thumbnail generation</li>
<li>Metadata extraction</li>
<li>Progress tracking</li>
<li>Webhook notifications</li>
<li>Usage analytics</li>
</ul>
<h2 id="technologies-used">Technologies Used</h2>
<ul>
<li>AWS Lambda</li>
<li>Amazon S3</li>
<li>Amazon SQS</li>
<li>AWS MediaConvert</li>
<li>DynamoDB</li>
<li>API Gateway</li>
<li>CloudWatch</li>
<li>AWS CDK</li>
</ul>
<h2 id="implementation-details">Implementation Details</h2>
<p>The platform leverages AWS serverless services to provide scalable media processing:</p>
<h3 id="architecture">Architecture</h3>
<ul>
<li>S3 for file storage</li>
<li>Lambda for processing</li>
<li>SQS for job queuing</li>
<li>DynamoDB for metadata</li>
<li>MediaConvert for transcoding</li>
<li>CloudWatch for monitoring</li>
</ul>
<h3 id="processing-pipeline">Processing Pipeline</h3>
<ol>
<li>File upload to S3</li>
<li>Trigger Lambda function</li>
<li>Queue processing job</li>
<li>Process media</li>
<li>Store results</li>
<li>Send notifications</li>
</ol>
<h2 id="live-demo">Live Demo</h2>
<p><a href="https://media-processor.example.com">View Live Demo</a></p>
<h2 id="github-repository">GitHub Repository</h2>
<p><a href="https://github.com/username/serverless-media-processor">View Source Code</a></p>

Projects

Featured Projects

AI-Powered Customer Service Chatbot

All Projects

Modern E-commerce Platform

Untitled Project

Real-Time Analytics Dashboard

Collaborative Task Management App

AI-Powered Customer Service Chatbot

ML Image Classifier

Secure Authentication System

GraphQL Blog Platform

Serverless Media Processor

Filter by Tags