HN Podcast Transcriber

Automatically fetch, transcribe, and archive Hacker News podcast episodes (Hacker News Morning Brief). Use when the user wants to set up a podcast transcription pipeline, archive HN podcast episodes as searchable text, transcribe podcast audio to markdown, or schedule periodic HN podcast ingestion. Also use for any podcast RSS feed transcription workflow.

Fetch new episodes from the Hacker News Morning Brief podcast RSS feed, transcribe with Whisper, and archive as searchable markdown.

Prerequisites

• whisper CLI installed (pip install openai-whisper)
• ffmpeg on PATH (required by whisper; download from https://ffmpeg.org)
• python3 with standard library (no extra deps for the fetch script)
• Disk space for audio files (~5-10 MB per episode)

Quick Start

Run the main script to fetch and transcribe all new episodes:

bash

bash scripts/fetch_and_transcribe.sh --archive ~/hn-podcast-archive

First run processes all episodes. Subsequent runs only process new ones (tracked via state.json).

Options

Flag	Default	Description
--feed URL	HN Morning Brief RSS	Podcast RSS feed URL
--archive DIR	./hn-podcast-archive	Archive root directory
--model MODEL	turbo	Whisper model (tiny/base/small/medium/large/turbo)
--limit N	0 (all)	Max new episodes to process per run

Custom Feeds

Point at any podcast RSS feed:

bash

bash scripts/fetch_and_transcribe.sh --feed "https://example.com/podcast/feed.xml" --archive ./my-podcast-archive

Scheduling

Set up an OpenClaw cron job for daily checks:

1. Create an isolated cron job that runs the script
2. Or add a heartbeat check in HEARTBEAT.md

Archive Structure

See references/archive-layout.md for directory layout and state.json schema.

Workflow Summary

1. Download RSS feed → parse <item> entries
2. Skip already-processed episodes (state.json lookup)
3. Download audio (mp3/m4a) to episode directory
4. Run whisper to produce .txt transcript
5. Generate cleaned transcript.md with title + date header
6. Update state.json with processed episode ID

Notes

• Whisper models cache to ~/.cache/whisper after first download
• Use --model tiny for speed, --model large for best accuracy
• Average episode (~6 min) takes ~1-2 min with turbo model on CPU
• For GPU acceleration, install ffmpeg with CUDA support