How to Build an Airdrop Monitor with AI

Here's a thinking process:

1. Analyze the Request:

   • Topic: How to Build an Airdrop Monitor with AI
   • Format: Technical article
   • Length: 400 words
   • Format: Markdown
   • Constraints:
     • Do NOT start with # or the title
     • Start directly with content
     • Include code examples
     • Include practical tips
     • Include call to action for AI API services
   • Key elements to cover: Airdrop monitoring, AI integration, code snippets, tips, CTA for AI APIs

2. Determine Structure & Word Count:

   • Target: ~400 words
   • Markdown format (no heading at start, but can have headings later? The constraint says "Do NOT start with # or the title — start directly with content." So the first character should be text, not #. I can use headings later if needed, but better to avoid # entirely or use them after content starts. I'll just write paragraphs and maybe use bold or inline code, but avoid top-level # headings to be safe.)
   • Actually, re-read: "Do NOT start with # or the title — start directly with content." Means the very first line should not be # Title or just #. I can use markdown formatting elsewhere, but the first character should be text. I'll structure it as a regular article with paragraphs, maybe use ## later or just avoid carets. Safer: no # at all, or use ### only after content starts? I'll just write without any # to be safe. Or I can use ** for emphasis, ````` for code blocks.

Let's plan the content:

• Intro: Building an airdrop monitor that uses AI to filter, analyze, and alert
• Core components: API integration, blockchain data fetching, AI analysis
• Code example: Python script using web3.py or ethers.js + an AI API (like OpenAI or a custom classifier)
• Practical tips: Rate limiting, caching, false positive reduction, security
• CTA: Prompt to try AI API services, mention benefits

Word count target: ~400 words. I'll aim for exactly or around 400.

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