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.
📖 Read the full source: r/ClaudeAI
👀 See Also
OpenClaw Agent Automates AI News Pipeline with LLM Curation
An OpenClaw agent runs a fully automated AI news pipeline that scans 25 RSS feeds, 13 Reddit subreddits, Twitter, GitHub, and web searches, then uses Gemini Flash for editorial curation and Claude Sonnet for writing. The system costs about $5/month and publishes to a Telegram channel.
Financial Analyst Uses Claude Code to Build DCF Model Without Coding Experience
A financial analyst with no terminal experience used Claude Code to build a discounted cash flow model in 20-25 minutes instead of 1-2 days. The tool read financial files and generated a fully structured Excel model with working formulas after the user typed /dcf [company name].
OpenClaw + SalesBlink: Autonomous Outreach Management Cuts Time from 10hrs to 1hr/week
Connecting OpenClaw to SalesBlink via API automated campaign monitoring, reply handling, and sequence triggers. The user reports reply rates up from 3% to 5.1%, meeting bookings doubled, and zero domains burned in 30 days.
Building a Personal AI Chief of Staff on Telegram: 7 Problems and Stack Details
A developer built a personal AI assistant on Telegram using Claude Sonnet API, Notion, Google Calendar, and Gmail. They share what works (meeting processing, morning briefings) and 7 unsolved problems like memory loss, reactivity, and stale context files.