A couple of weeks ago, I launched a fully automated podcast, AI Arxiv that focuses exactly on this. I have built an automated pipeline that does the research for this podcast by sifting through various sources such as engineering blogs, research papers, youtube videos etc., extracts high signal content, categorizes it and feeds it to Google's NotebookLM for audio generation before getting published on public RSS feeds, Spotify and Apple podcast. I continue to be fascinated by how AI is being applied in novel ways beyond chatbots for solving unique use cases.

The podcast has already crossed 40 episodes, 50 subscribers and 500 listens. Some of the more popular ones include:

- How Meta uses LLMs for efficient incident response

- How Salesforce operationalizes models at scale

- How DoorDash built a high quality RAG for dasher support

- How Pinterest built text-to-sql

I was glad to hear from some folks that this was helping them stay on top of novel GenAI use cases and also to stretch their thinking beyond chatbots. The nice thing is, these podcast episodes are semi-automated with myself in the loop directing and approving workflows. As a result, it hardly takes few minutes to generate and publish new episodes.

For the tech stack, I used CrewAI for content research, DSPy for content metadata & podcast notes/sponsor message and Langtrace for experimentation & testing.

If you are someone actively tinkering with LLMs or even generally curious about the GenAI space, do check it out.

1. RSS Feed - https://anchor.fm/s/fb0d9ea8/podcast/rss

2. Apple - https://podcasts.apple.com/us/podcast/ai-arxiv/id1768464164

3. Spotify - https://open.spotify.com/show/0Toon5UiQc5P7DNDjsrr9K