Automating a Daily AI News Podcast with Claude Code and Three AI Agents

Pipeline Architecture and Key Design Decisions

This automated podcast system follows a four-step pipeline orchestrated by Claude Code. The most critical design decision is the source weighting system: official AI company blogs (40%), industry thought leaders (30%), and community signals like Hacker News and GitHub Trending (30%).

Three Specialized AI Agents in Sequence

• Collector Agent: Pulls and de-noises content from all weighted sources
• Editor Agent: Selects the top 5 stories and writes narration scripts using a "super prompt" with recursive summarization
• Proofreader Agent: Fact-checks every claim against original sources plus Google search. Failed checks trigger automatic rewrites

Voice Generation and Publishing

The system uses ListenHub API for Chinese TTS with cloned voice (requires ~2 minutes of sample audio). For English, ElevenLabs would work. The complete workflow is: collect → edit → proofread → TTS → combine audio segments → publish to podcast platform (RedCircle or Spotify for Podcasters).

Practical Implementation Tips

• Focus on curation rules rather than model selection—determining "what's worth listening to" is the core challenge
• Add de-duplication mechanisms for daily runs (the developer encountered repeated topics in week 2)
• Start with text-only version (skip steps 3-4) which delivers 80% of the value

The entire system runs on Claude Code with any TTS tool and preferred podcast hosting service, demonstrating how specialized AI agents can handle different aspects of content creation and verification.