AI News Analyzer

Here’s a complete example of an AI agent that analyzes news articles using CodecFlow’s cloud desktops:

TypeScript

import { CodecFlow } from '@codecflow/sdk';

async function startNewsAnalyzer() {
  // Initialize CodecFlow
  const codecflow = new CodecFlow({
    apiKey: 'your-api-key'
  });

  // Create cloud desktop
  const desktop = await codecflow.createDesktop({
    os: 'linux',
    version: 'ubuntu-22.04',
    security: {
      teeEnabled: true,
      isolationLevel: 'medium'
    }
  });

  // Create browser session
  const browser = await desktop.createBrowser();

  // Connect AI agent using MCP
  const agent = await desktop.connectAgent({
    model: 'gpt-4',
    protocol: 'mcp',
    config: {
      capabilities: ['browser', 'filesystem', 'network'],
      contextProviders: [
        {
          type: 'news-sources',
          sources: ['reuters', 'ap', 'bloomberg'],
          categories: ['technology', 'finance', 'politics']
        }
      ],
      prompt: `You are a news analyst. Read articles, extract key information,
              and provide insightful analysis on technology trends.`
    }
  });

  // Set up news analysis workflow
  await agent.executeAction({
    type: 'workflow',
    action: 'configure',
    params: {
      schedule: 'hourly',
      outputFormat: 'markdown',
      storageLocation: '/reports',
      alertThreshold: 'high'
    }
  });

  // Start the desktop
  await desktop.start();

  // Monitor analysis results
  desktop.on('analysisComplete', (result) => {
    console.log(`Analysis complete: ${result.title}`);
    console.log(`Key insights: ${result.insights.join(', ')}`);
    console.log(`Sentiment: ${result.sentiment}`);
  });

  // Handle errors
  desktop.on('error', (error) => {
    console.error('Analysis error:', error);
  });

  // Graceful shutdown
  process.on('SIGINT', async () => {
    await desktop.shutdown();
    process.exit(0);
  });
}

startNewsAnalyzer().catch(console.error);

Python

from codecflow import CodecFlow

async def start_news_analyzer():
    # Initialize CodecFlow
    codecflow = CodecFlow(api_key='your-api-key')

    # Create cloud desktop
    desktop = await codecflow.create_desktop(
        os='linux',
        version='ubuntu-22.04',
        security={
            'tee_enabled': True,
            'isolation_level': 'medium'
        }
    )

    # Create browser session
    browser = await desktop.create_browser()

    # Connect AI agent using MCP
    agent = await desktop.connect_agent(
        model='gpt-4',
        protocol='mcp',
        config={
            'capabilities': ['browser', 'filesystem', 'network'],
            'context_providers': [
                {
                    'type': 'news-sources',
                    'sources': ['reuters', 'ap', 'bloomberg'],
                    'categories': ['technology', 'finance', 'politics']
                }
            ],
            'prompt': 'You are a news analyst. Read articles, extract key information, and provide insightful analysis on technology trends.'
        }
    )

    # Set up news analysis workflow
    await agent.execute_action({
        'type': 'workflow',
        'action': 'configure',
        'params': {
            'schedule': 'hourly',
            'output_format': 'markdown',
            'storage_location': '/reports',
            'alert_threshold': 'high'
        }
    })

    # Start the desktop
    await desktop.start()

    # Monitor analysis results
    def on_analysis_complete(result):
        print(f"Analysis complete: {result['title']}")
        print(f"Key insights: {', '.join(result['insights'])}")
        print(f"Sentiment: {result['sentiment']}")

    desktop.on('analysisComplete', on_analysis_complete)

    # Handle errors
    def on_error(error):
        print(f"Analysis error: {error}")

    desktop.on('error', on_error)

    # Graceful shutdown
    def handle_shutdown(sig, frame):
        await desktop.shutdown()
        exit(0)

    import signal
    signal.signal(signal.SIGINT, handle_shutdown)

# Run the news analyzer
await start_news_analyzer()

Key Features

• Secure News Analysis: Uses TEE to protect sensitive data and operations
• AI-Powered Analysis: Leverages MCP to connect AI models with news sources
• Automated Workflows: Configures scheduled analysis of news content
• Multi-Source Integration: Analyzes content from multiple news providers
• Sentiment Analysis: Extracts key insights and sentiment from articles
• Secure Storage: Stores analysis results in a protected environment

Note: This example demonstrates how CodecFlow’s cloud desktops can be used for automated content analysis with AI integration.