How I Automated My Podcast Post-Production Without Losing Sleep

说明与用途

使用 Cloudflare Workers 和 Llama 3 8B Instruct 自动化播客后期制作，将每集准备时间从 40 分钟缩短到几秒，生成结构化的节目笔记和标题。

问题

The author spent about 40 minutes per episode writing show notes, titles, and metadata, which was more time than creating content.

结果

Show notes and titles are generated in a few seconds, with structured markdown that requires minimal reformatting, saving the author from manual transcription work.

工作流

• 1. Capture raw transcript from recording
• 2. Send transcript to Cloudflare Worker via JSON request
• 3. Worker calls Cloudflare AI inference with Llama 3 8B Instruct model and a strict system prompt to generate structured show notes (summary, key takeaways, timestamps, resources)
• 4. Worker returns JSON response
• 5. Use generated markdown directly in podcast hosting platform
• 6. Separate prompt for title generation with tone variable to produce numbered list of titles

证据

Writing good show notes is not just about summarizing what was said. It is about structuring the content for search engines, highlighting key takeaways, and providing timestamps so listeners can navigate the audio easily.

来源

I decided to build PodCrisp using Cloudflare Workers, leveraging their AI inference capabilities at the edge.

来源

The results have been genuinely helpful for my own workflow. What works exceptionally well is the consistency of the structured output.

来源