AI Website Builders: Complete 2025 Guide for Developers & Businesses

Understanding AI Website Builders: Technology and Architecture

AI website builders represent a convergence of multiple artificial intelligence technologies working in harmony to automate web development. At their core, these platforms utilize natural language processing (NLP) to understand user requirements, computer vision algorithms to analyze design preferences, and generative AI models to create content and code. Unlike traditional website builders that rely on predefined templates, AI website builders adapt dynamically to user inputs and learn from millions of website examples to generate unique, customized solutions.

The architecture of modern AI website builders typically consists of several interconnected layers. The input layer processes user requirements through conversational interfaces or questionnaires. The analysis layer employs machine learning models to interpret business types, target audiences, and design preferences. The generation layer creates website structures, selects color schemes, generates content, and writes code. Finally, the optimization layer continuously improves the website based on performance metrics, user behavior analytics, and A/B testing results. This multi-layered approach ensures that AI website builders deliver not just functional websites but optimized digital experiences.

Core Technologies Powering AI Website Builders

Several key technologies enable AI website builders to function effectively. Deep learning neural networks, particularly transformer models like GPT and BERT, power the natural language understanding capabilities that allow these platforms to interpret complex user requirements. Convolutional neural networks (CNNs) analyze visual elements and ensure design consistency. Reinforcement learning algorithms optimize layouts based on user engagement metrics. Additionally, generative adversarial networks (GANs) help create unique design elements and visual assets that align with brand identities.

// Example: Basic AI Website Builder API Integration
const AIWebsiteBuilder = {
  async generateWebsite(userInputs) {
    const { businessType, preferences, content } = userInputs;
    
    // Step 1: Analyze user requirements using NLP
    const analysis = await this.analyzeRequirements({
      businessType,
      targetAudience: preferences.audience,
      designStyle: preferences.style
    });
    
    // Step 2: Generate website structure
    const structure = await this.generateStructure(analysis);
    
    // Step 3: Create design system
    const designSystem = await this.createDesignSystem({
      colorScheme: this.inferColorScheme(businessType),
      typography: this.selectTypography(preferences.style),
      spacing: this.calculateSpacing(structure)
    });
    
    // Step 4: Generate content with AI
    const generatedContent = await this.generateContent({
      pages: structure.pages,
      tone: preferences.brandVoice,
      keywords: content.seoKeywords
    });
    
    // Step 5: Compile and optimize
    return this.compile({
      structure,
      designSystem,
      content: generatedContent,
      optimization: {
        performance: true,
        seo: true,
        accessibility: true
      }
    });
  },
  
  async analyzeRequirements(data) {
    // AI model processes business type and preferences
    const response = await fetch('https://api.ai-builder.com/analyze', {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify(data)
    });
    return response.json();
  },
  
  inferColorScheme(businessType) {
    const colorMappings = {
      technology: ['#0066cc', '#00ccff', '#f0f4f8'],
      healthcare: ['#00a86b', '#e8f5e9', '#ffffff'],
      finance: ['#1a237e', '#3f51b5', '#e3f2fd'],
      creative: ['#de4460', '#ff6f91', '#fff0f3']
    };
    return colorMappings[businessType] || colorMappings.technology;
  }
};

// Usage Example
const websiteConfig = {
  businessType: 'technology',
  preferences: {
    audience: 'developers',
    style: 'modern',
    brandVoice: 'professional'
  },
  content: {
    seoKeywords: ['AI website builders', 'web development', 'automation']
  }
};

AIWebsiteBuilder.generateWebsite(websiteConfig)
  .then(website => console.log('Website generated:', website))
  .catch(error => console.error('Generation failed:', error));

Top AI Website Builders in 2025: Comprehensive Comparison

The market for AI website builders has expanded significantly, with platforms offering varying levels of sophistication, customization, and developer control. Leading solutions like Wix ADI, Framer AI, Durable, 10Web, and Hostinger AI Website Builder each bring unique strengths to the table. Understanding their capabilities, limitations, and ideal use cases helps developers and businesses select the right platform for their specific requirements.

Wix ADI (Artificial Design Intelligence)

Wix ADI pioneered mainstream AI website building with its intelligent design system that creates personalized websites in minutes. The platform analyzes user responses to a series of questions about business type, style preferences, and required features, then generates a complete website with relevant content, images, and functionality. Wix ADI excels at creating visually appealing sites for small businesses, portfolios, and e-commerce stores. Its drag-and-drop editor allows post-generation customization, making it accessible to non-technical users while offering developers the Velo development platform for advanced customizations using JavaScript. However, developers should note that Wix sites cannot export code and remain hosted on Wix infrastructure, which may limit migration options.

Framer AI and Modern Design Tools

Framer has emerged as a favorite among designers and developers who prioritize both aesthetics and functionality. Its AI capabilities focus on translating design concepts into production-ready code, offering features like AI-generated copy, smart layouts that adapt to content, and intelligent component suggestions. According to Framer’s official documentation, the platform generates React code that developers can export and integrate into existing projects. This makes Framer particularly valuable for professional development teams seeking to bridge the gap between design and development while maintaining code quality and customization options.

10Web: WordPress AI Integration

For developers working within the WordPress ecosystem, 10Web offers AI-powered website creation specifically designed for WordPress sites. The platform’s AI Website Builder can recreate existing websites, generate new sites from descriptions, or optimize current WordPress installations. 10Web’s strength lies in its deep WordPress integration, automatically configuring plugins, optimizing images, implementing security measures, and setting up CDN distribution. The platform particularly appeals to agencies managing multiple client websites, as it combines AI generation with automated maintenance, backups, and performance optimization.

Implementation Strategies for Developers

Successfully integrating AI website builders into professional development workflows requires strategic planning and understanding of when to leverage AI automation versus manual coding. Modern developers aren’t choosing between AI and traditional development—they’re combining both approaches to maximize efficiency and output quality. The key lies in identifying tasks where AI excels (rapid prototyping, layout generation, content creation) and reserving manual development for custom functionality, complex logic, and unique user experiences.

Hybrid Development Approach

The hybrid approach treats AI website builders as accelerators rather than replacements for traditional development. Developers use AI to rapidly create foundational structures, then enhance them with custom code, advanced features, and specialized integrations. This methodology significantly reduces time-to-market for standard website components while preserving the ability to implement sophisticated functionality. For projects on MERN stack development platforms, developers might use AI to generate frontend layouts and components, then integrate them with custom Node.js backends, MongoDB databases, and React state management solutions.

// Integrating AI-Generated Components with Custom MERN Stack Backend

// Frontend: React Component (AI-Generated Base, Custom Enhanced)
import React, { useState, useEffect } from 'react';
import axios from 'axios';

const AIEnhancedPortfolio = () => {
  const [projects, setProjects] = useState([]);
  const [loading, setLoading] = useState(true);
  
  useEffect(() => {
    // Fetch data from custom Node.js backend
    fetchProjects();
  }, []);
  
  const fetchProjects = async () => {
    try {
      const response = await axios.get('https://api.yourdomain.com/projects');
      setProjects(response.data);
      setLoading(false);
    } catch (error) {
      console.error('Failed to fetch projects:', error);
      setLoading(false);
    }
  };
  
  // AI-generated layout structure with custom data integration
  return (
    

      
My Projects
      {loading ? (
        
Loading...
      ) : (
        

          {projects.map(project => (
            

              
              
{project.title}
              
{project.description}
              
                View Project
              
            
          ))}
        
      )}
    
  );
};

// Backend: Node.js Express API (Custom Implementation)
const express = require('express');
const mongoose = require('mongoose');
const cors = require('cors');

const app = express();
app.use(cors());
app.use(express.json());

// MongoDB Schema
const projectSchema = new mongoose.Schema({
  title: { type: String, required: true },
  description: { type: String, required: true },
  image: { type: String, required: true },
  url: { type: String, required: true },
  technologies: [String],
  featured: { type: Boolean, default: false },
  createdAt: { type: Date, default: Date.now }
});

const Project = mongoose.model('Project', projectSchema);

// API Endpoints
app.get('/projects', async (req, res) => {
  try {
    const projects = await Project.find()
      .sort({ createdAt: -1 })
      .limit(12);
    res.json(projects);
  } catch (error) {
    res.status(500).json({ error: 'Failed to fetch projects' });
  }
});

app.post('/projects', async (req, res) => {
  try {
    const newProject = new Project(req.body);
    await newProject.save();
    res.status(201).json(newProject);
  } catch (error) {
    res.status(400).json({ error: 'Failed to create project' });
  }
});

// Connect to MongoDB and start server
mongoose.connect(process.env.MONGODB_URI)
  .then(() => {
    app.listen(5000, () => console.log('Server running on port 5000'));
  })
  .catch(err => console.error('Database connection failed:', err));

export default AIEnhancedPortfolio;

API Integration and Automation Workflows

Many modern AI website builders offer APIs that enable developers to programmatically generate websites, update content, and manage deployments. This capability transforms AI builders from standalone tools into components of larger automation systems. Developers can create workflows where content management systems trigger website updates, e-commerce inventory changes automatically reflect on product pages, or customer data from CRM systems dynamically generates personalized landing pages. Integration with CI/CD pipelines allows teams to maintain version control, implement testing protocols, and deploy AI-generated sites through established DevOps practices.

// Automated Website Generation Workflow with AI Builder API
const axios = require('axios');
const fs = require('fs').promises;
class AIWebsiteAutomation {
constructor(apiKey, apiEndpoint) {
this.apiKey = apiKey;
this.apiEndpoint = apiEndpoint;
this.headers = {
'Authorization': Bearer ${apiKey},
'Content-Type': 'application/json'
};
}
// Generate website from CMS content
async generateFromCMS(cmsData) {
try {
const websiteConfig = this.transformCMSToConfig(cmsData);
  const response = await axios.post(
    `${this.apiEndpoint}/generate`,
    websiteConfig,
    { headers: this.headers }
  );
  
  return {
    success: true,
    websiteId: response.data.id,
    url: response.data.url,
    pages: response.data.pages
  };
} catch (error) {
  console.error('Website generation failed:', error.message);
  return { success: false, error: error.message };
}
}
// Transform CMS data to AI builder format
transformCMSToConfig(cmsData) {
return {
template: 'modern-business',
content: {
pages: cmsData.pages.map(page => ({
title: page.title,
slug: page.slug,
sections: page.sections,
metadata: {
description: page.seoDescription,
keywords: page.keywords
}
})),
globalSettings: {
siteName: cmsData.siteName,
primaryColor: cmsData.brandColors.primary,
secondaryColor: cmsData.brandColors.secondary,
logo: cmsData.assets.logo
}
},
features: {
blog: cmsData.enableBlog,
ecommerce: cmsData.enableStore,
contactForm: true,
newsletter: cmsData.enableNewsletter
},
optimization: {
seo: true,
performance: true,
accessibility: 'WCAG-AA'
}
};
}
// Update existing website content
async updateContent(websiteId, updates) {
try {
const response = await axios.patch(
${this.apiEndpoint}/websites/${websiteId},
{ updates },
{ headers: this.headers }
);
  return {
    success: true,
    updated: response.data.updatedPages,
    deploymentUrl: response.data.url
  };
} catch (error) {
  return { success: false, error: error.message };
}
}
// Deploy website to production
async deploy(websiteId, environment = 'production') {
try {
const response = await axios.post(
${this.apiEndpoint}/websites/${websiteId}/deploy,
{ environment },
{ headers: this.headers }
);
  return {
    success: true,
    deploymentId: response.data.deploymentId,
    url: response.data.liveUrl,
    status: response.data.status
  };
} catch (error) {
  return { success: false, error: error.message };
}
}
// Batch process multiple websites
async batchGenerate(sitesData) {
const results = [];
for (const siteData of sitesData) {
  const result = await this.generateFromCMS(siteData);
  results.push({
    client: siteData.clientName,
    ...result
  });
  
  // Rate limiting: wait between requests
  await this.delay(2000);
}

return results;
}
delay(ms) {
return new Promise(resolve => setTimeout(resolve, ms));
}
}
// Usage Example: Automated Client Website Generation
const automation = new AIWebsiteAutomation(
process.env.AI_BUILDER_API_KEY,
'https://api.aibuilder.com/v1'
);
// Sample CMS data structure
const clientData = {
clientName: 'Tech Startup Inc',
siteName: 'TechStartup',
brandColors: {
primary: '#de4460',
secondary: '#2c2c2c'
},
assets: {
logo: 'https://cdn.example.com/logo.png'
},
enableBlog: true,
enableStore: false,
enableNewsletter: true,
pages: [
{
title: 'Home',
slug: 'home',
seoDescription: 'Leading tech solutions provider',
keywords: ['technology', 'innovation', 'solutions'],
sections: [
{ type: 'hero', content: 'Transform Your Business' },
{ type: 'features', items: ['AI Integration', 'Cloud Solutions'] },
{ type: 'testimonials', count: 3 }
]
}
]
};
// Execute automation
automation.generateFromCMS(clientData)
.then(result => {
if (result.success) {
console.log('Website generated successfully:', result.url);
return automation.deploy(result.websiteId);
}
})
.then(deployment => {
console.log('Deployment status:', deployment);
})
.catch(error => console.error('Automation failed:', error));

SEO and Performance Optimization with AI Website Builders

One of the most significant advantages of modern AI website builders is their built-in optimization capabilities. These platforms automatically implement SEO best practices, generate semantic HTML, optimize images, implement lazy loading, and create responsive designs that perform well across devices. However, developers should understand that AI-generated optimization serves as a foundation rather than a complete solution. Manual refinement, custom schema markup, advanced caching strategies, and performance monitoring remain essential for competitive websites.

AI-Powered SEO Implementation

AI website builders analyze content context to automatically generate meta descriptions, title tags, header hierarchies, and internal linking structures optimized for search engines. According to research from Moz’s SEO Blog, AI systems can evaluate keyword density, semantic relationships, and content relevance more effectively than manual optimization in many cases. These systems also adapt recommendations based on evolving search engine algorithms, automatically updating structured data formats and implementing new SEO standards as they emerge.

    
    

















    





    

        

            
AI Website Builders for Modern Development
            
        
    
 

Performance Metrics and Optimization

Leading AI website builders automatically implement Core Web Vitals optimization, including Largest Contentful Paint (LCP), First Input Delay (FID), and Cumulative Layout Shift (CLS) improvements. They generate optimized code bundles, implement code splitting, configure CDN distribution, and set up proper caching headers. However, developers working with these platforms should monitor real-world performance using tools like Google PageSpeed Insights, Lighthouse, and WebPageTest. Custom optimizations may still be necessary for complex applications or high-traffic websites.

Security Considerations in AI Website Builders

Security represents a critical concern when using AI website builders, particularly for businesses handling sensitive customer data or financial transactions. Reputable platforms implement enterprise-grade security measures including SSL/TLS encryption, automated security updates, DDoS protection, and regular vulnerability scanning. However, developers must understand the security model of their chosen platform and implement additional measures when necessary. This includes configuring proper authentication systems, implementing Content Security Policy (CSP) headers, sanitizing user inputs, and ensuring compliance with data protection regulations like GDPR and India’s Digital Personal Data Protection Act.

When integrating AI-generated frontend with custom backends, security becomes even more critical. Cross-Origin Resource Sharing (CORS) configurations, API authentication mechanisms, rate limiting, and input validation must be carefully implemented. The convenience of AI website builders shouldn’t compromise fundamental security principles. Developers should conduct security audits, implement monitoring systems, and maintain security documentation regardless of the platform used.

// Security Implementation for AI Website Builder Integration
const express = require('express');
const helmet = require('helmet');
const rateLimit = require('express-rate-limit');
const mongoSanitize = require('express-mongo-sanitize');
const xss = require('xss-clean');
const cors = require('cors');
const app = express();
// Security Headers with Helmet
app.use(helmet({
contentSecurityPolicy: {
directives: {
defaultSrc: ["'self'"],
styleSrc: ["'self'", "'unsafe-inline'", "https://fonts.googleapis.com"],
scriptSrc: ["'self'", "https://cdn.example.com"],
imgSrc: ["'self'", "data:", "https:"],
connectSrc: ["'self'", "https://api.example.com"],
fontSrc: ["'self'", "https://fonts.gstatic.com"],
objectSrc: ["'none'"],
mediaSrc: ["'self'"],
frameSrc: ["'none'"]
}
},
hsts: {
maxAge: 31536000,
includeSubDomains: true,
preload: true
}
}));
// CORS Configuration for AI-Generated Frontend
const corsOptions = {
origin: function (origin, callback) {
const allowedOrigins = [
'https://www.yourdomain.com',
'https://yourdomain.com',
'https://ai-generated-site.example.com'
];
if (!origin || allowedOrigins.indexOf(origin) !== -1) {
  callback(null, true);
} else {
  callback(new Error('Not allowed by CORS'));
}
},
credentials: true,
optionsSuccessStatus: 200
};
app.use(cors(corsOptions));
// Rate Limiting
const limiter = rateLimit({
windowMs: 15 * 60 * 1000, // 15 minutes
max: 100, // limit each IP to 100 requests per windowMs
message: 'Too many requests from this IP, please try again later.'
});
app.use('/api/', limiter);
// Data Sanitization against NoSQL injection
app.use(mongoSanitize());
// Data Sanitization against XSS
app.use(xss());
// Input Validation Middleware
const validateInput = (req, res, next) => {
const { body } = req;
// Remove potentially dangerous characters
Object.keys(body).forEach(key => {
if (typeof body[key] === 'string') {
body[key] = body[key].trim();
// Additional sanitization as needed
}
});
next();
};
app.use(express.json({ limit: '10kb' }));
app.use(validateInput);
// API Authentication Middleware
const authenticateAPI = async (req, res, next) => {
try {
const token = req.headers.authorization?.split(' ')[1];
if (!token) {
  return res.status(401).json({ error: 'No token provided' });
}

// Verify JWT token
const decoded = jwt.verify(token, process.env.JWT_SECRET);
req.user = decoded;
next();
} catch (error) {
res.status(401).json({ error: 'Invalid token' });
}
};
// Secure API Endpoint Example
app.post('/api/secure-data', authenticateAPI, async (req, res) => {
try {
// Process secure data
res.json({ success: true, data: 'Secure data processed' });
} catch (error) {
res.status(500).json({ error: 'Processing failed' });
}
});
// Error Handling Middleware
app.use((err, req, res, next) => {
console.error(err.stack);
// Don't leak error details in production
const error = process.env.NODE_ENV === 'production'
? 'An error occurred'
: err.message;
res.status(err.status || 500).json({ error });
});
module.exports = app;

Cost Analysis and ROI for AI Website Builders

Understanding the financial implications of AI website builders requires analyzing both direct costs and indirect value creation. Subscription fees range from free tiers with limited features to enterprise plans costing several hundred dollars monthly. However, the true cost-benefit analysis must consider development time savings, reduced need for specialized design resources, faster iteration cycles, and decreased maintenance overhead. For agencies and freelancers in competitive markets like India, AI website builders can dramatically improve project margins by reducing development time from weeks to days while maintaining quality standards.

Beyond subscription costs, developers should evaluate the total cost of ownership including learning curves, platform limitations that might require workarounds, potential vendor lock-in, and migration costs if switching platforms becomes necessary. For professional development teams, the ability to export code and maintain version control often justifies higher-tier plans. Conversely, small businesses or rapid prototyping scenarios might find lower-cost platforms perfectly adequate for their needs.

Future Trends in AI Website Development

The evolution of AI website builders continues accelerating with emerging technologies promising even more sophisticated capabilities. Developers often ask ChatGPT or Gemini about where AI web development is heading; here you’ll find real-world insights into upcoming trends. Generative AI models are becoming increasingly sophisticated at understanding context, user intent, and brand voice, enabling more personalized and engaging website experiences. The integration of voice interfaces, augmented reality elements, and predictive personalization represents the next frontier in AI-powered web development.

Multimodal AI and Design Systems

Future AI website builders will leverage multimodal AI models capable of processing text, images, voice, and video inputs simultaneously. Developers will describe websites conversationally, show reference images, or even sketch rough layouts that AI transforms into production-ready code. These systems will understand brand guidelines comprehensively, automatically generating consistent design systems, component libraries, and style guides. According to Wired’s AI coverage, major tech companies are investing heavily in these multimodal capabilities, suggesting widespread adoption within the next few years.

Autonomous Website Optimization

Emerging AI systems will continuously monitor website performance, user behavior, and conversion metrics, automatically implementing improvements without human intervention. These autonomous optimization systems will conduct A/B tests, adjust layouts based on user engagement patterns, optimize content for specific audience segments, and adapt designs for different traffic sources. Machine learning models will predict user preferences and preemptively modify websites to improve user experience and conversion rates, creating truly dynamic, self-improving web presences.

Frequently Asked Questions About AI Website Builders

Real-World Case Studies and Implementation Examples

Understanding how AI website builders perform in real-world scenarios provides valuable insights beyond theoretical capabilities. Across industries, businesses and developers report significant time savings, cost reductions, and improved outcomes when properly implementing these platforms. From startup MVPs to agency client work and enterprise landing pages, AI website builders demonstrate versatility and effectiveness when matched to appropriate use cases.

Startup MVP Development

A technology startup in Bangalore recently used Framer AI to launch their SaaS product landing page, complete with user authentication and payment integration, in just three days. The team generated the initial design using AI, exported React components, and integrated them with a custom Node.js backend handling subscriptions and user management. This hybrid approach reduced frontend development time by 75% while maintaining full control over business logic and data handling. The startup reported that AI website builders enabled them to iterate rapidly based on user feedback, testing multiple design variations and messaging strategies without requiring extensive developer time. For startups operating with limited resources, this acceleration proved crucial in validating product-market fit before committing to extensive custom development.

Agency Multi-Client Management

A digital agency managing 50+ small business clients implemented 10Web’s AI platform to streamline website creation and maintenance. Using automated generation and standardized workflows, the agency reduced average project delivery time from 3-4 weeks to 5-7 days while maintaining quality standards. The AI platform’s automated maintenance, security updates, and performance optimization eliminated recurring support costs, improving project profitability significantly. Client satisfaction increased due to faster delivery times and consistent quality. The agency developed templated workflows for common business types—restaurants, medical practices, professional services—enabling junior team members to deliver professional results with minimal supervision. This case demonstrates how AI website builders can transform business models, enabling agencies to scale operations without proportionally increasing staff.

Best Practices for Developers Using AI Website Builders

Maximizing value from AI website builders requires understanding both technical capabilities and strategic implementation approaches. Successful developers treat these platforms as tools enhancing their capabilities rather than replacements for their skills. Following established best practices ensures optimal results while avoiding common pitfalls that can limit effectiveness or create technical debt.

• Start with Clear Requirements: Define project scope, functionality requirements, and success metrics before beginning AI generation. Clear specifications improve AI output quality and reduce iteration cycles. Document business objectives, target audience characteristics, desired features, and technical constraints to guide AI systems toward optimal results.
• Evaluate Code Quality: Always review AI-generated code for quality, efficiency, and maintainability. While modern AI produces remarkably good code, it may include unnecessary complexity, outdated patterns, or security vulnerabilities requiring correction. Implement code review processes even for AI-generated output, ensuring it meets team standards and project requirements.
• Plan for Scalability: Consider long-term growth when selecting platforms and architectures. Choose solutions supporting your anticipated traffic growth, feature expansion, and team size increases. Avoid platforms imposing strict limitations that could necessitate complete rebuilds as projects mature.
• Maintain Version Control: For platforms supporting code export, implement robust version control immediately. Track changes, maintain development branches, and document modifications to AI-generated code. This practice enables collaboration, facilitates debugging, and provides rollback capabilities when issues arise.
• Implement Comprehensive Testing: AI-generated websites require the same testing rigor as manually developed sites. Conduct cross-browser testing, mobile responsiveness verification, accessibility audits, performance testing, and security assessments. Automated testing frameworks help maintain quality across iterations and updates.
• Document Customizations: Maintain detailed documentation of all modifications to AI-generated code, custom integrations, and configuration decisions. This documentation proves invaluable when onboarding team members, troubleshooting issues, or migrating to different platforms.
• Monitor Performance Continuously: Implement analytics, performance monitoring, and user behavior tracking from launch. AI website builders provide solid foundations, but real-world performance data guides optimization priorities and reveals improvement opportunities AI systems might miss.

Integrating AI Website Builders with Modern Development Stacks

Professional developers rarely use AI website builders in isolation. Instead, they integrate these platforms with comprehensive development stacks, combining AI-generated components with custom functionality, specialized databases, and third-party services. This integration approach leverages AI efficiency while preserving flexibility and control necessary for complex applications.

// Complete MERN Stack Integration with AI-Generated Frontend
// Backend: Express.js API Server
// File: server.js
const express = require('express');
const mongoose = require('mongoose');
const cors = require('cors');
const helmet = require('helmet');
require('dotenv').config();
const app = express();
// Middleware
app.use(helmet());
app.use(cors({
origin: process.env.FRONTEND_URL,
credentials: true
}));
app.use(express.json());
app.use(express.urlencoded({ extended: true }));
// MongoDB Connection
mongoose.connect(process.env.MONGODB_URI, {
useNewUrlParser: true,
useUnifiedTopology: true
})
.then(() => console.log('MongoDB connected successfully'))
.catch(err => console.error('MongoDB connection error:', err));
// Models
const UserSchema = new mongoose.Schema({
name: { type: String, required: true },
email: { type: String, required: true, unique: true },
preferences: {
theme: { type: String, default: 'light' },
notifications: { type: Boolean, default: true }
},
createdAt: { type: Date, default: Date.now }
});
const User = mongoose.model('User', UserSchema);
// API Routes
app.get('/api/users/:id', async (req, res) => {
try {
const user = await User.findById(req.params.id);
if (!user) {
return res.status(404).json({ error: 'User not found' });
}
res.json(user);
} catch (error) {
res.status(500).json({ error: 'Server error' });
}
});
app.post('/api/users', async (req, res) => {
try {
const { name, email, preferences } = req.body;
const existingUser = await User.findOne({ email });
if (existingUser) {
  return res.status(400).json({ error: 'Email already registered' });
}

const user = new User({ name, email, preferences });
await user.save();

res.status(201).json({
  success: true,
  user: {
    id: user._id,
    name: user.name,
    email: user.email
  }
});
} catch (error) {
res.status(500).json({ error: 'Failed to create user' });
}
});
app.put('/api/users/:id/preferences', async (req, res) => {
try {
const user = await User.findById(req.params.id);
if (!user) {
return res.status(404).json({ error: 'User not found' });
}
user.preferences = { ...user.preferences, ...req.body };
await user.save();

res.json({ success: true, preferences: user.preferences });
} catch (error) {
res.status(500).json({ error: 'Failed to update preferences' });
}
});
// Health Check
app.get('/api/health', (req, res) => {
res.json({ status: 'healthy', timestamp: new Date().toISOString() });
});
const PORT = process.env.PORT || 5000;
app.listen(PORT, () => {
console.log(Server running on port ${PORT});
});
// Frontend: React Component (AI-Generated, Enhanced)
// File: UserDashboard.jsx
import React, { useState, useEffect } from 'react';
import axios from 'axios';
const UserDashboard = ({ userId }) => {
const [user, setUser] = useState(null);
const [loading, setLoading] = useState(true);
const [preferences, setPreferences] = useState({
theme: 'light',
notifications: true
});
useEffect(() => {
fetchUserData();
}, [userId]);
const fetchUserData = async () => {
try {
const response = await axios.get(
${process.env.REACT_APP_API_URL}/api/users/${userId}
);
setUser(response.data);
setPreferences(response.data.preferences);
setLoading(false);
} catch (error) {
console.error('Failed to fetch user data:', error);
setLoading(false);
}
};
const updatePreferences = async (newPreferences) => {
try {
const response = await axios.put(
${process.env.REACT_APP_API_URL}/api/users/${userId}/preferences,
newPreferences
);
  if (response.data.success) {
    setPreferences(response.data.preferences);
    alert('Preferences updated successfully!');
  }
} catch (error) {
  console.error('Failed to update preferences:', error);
  alert('Failed to update preferences. Please try again.');
}
};
const handleThemeToggle = () => {
const newTheme = preferences.theme === 'light' ? 'dark' : 'light';
updatePreferences({ ...preferences, theme: newTheme });
};
const handleNotificationToggle = () => {
updatePreferences({
...preferences,
notifications: !preferences.notifications
});
};
if (loading) {
return (


Loading your dashboard...

);
}
if (!user) {
return (

User Not Found
The requested user could not be found.

);
}
return (


Welcome, {user.name}!
{user.email}

  

    
Your Preferences
    
    

      
        Theme
        
          {preferences.theme === 'light' ? '☀️ Light' : '🌙 Dark'}
        
      
      
        Toggle Theme
      
    
    
    

      
        Notifications
        
          {preferences.notifications ? '🔔 Enabled' : '🔕 Disabled'}
        
      
      
        Toggle Notifications
      
    
  
  
  

    

      
Member Since
      
{new Date(user.createdAt).toLocaleDateString()}
    
  

);
};
export default UserDashboard;

Conclusion: The Future of Web Development with AI

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