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How I AIHow to digest 36 weekly podcasts without spending 36 hours listening | Tomasz Tunguz
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At a glance
WHAT IT’S REALLY ABOUT
Tomasz Tunguz’s terminal pipeline transcribes podcasts and drafts blog posts fast
- Tomasz Tunguz built a “podcast ripper” to keep up with 36 podcasts without spending 36 hours listening, converting daily episodes into cleaned transcripts and structured summaries.
- His pipeline downloads audio from podcast feeds, converts files with FFmpeg, transcribes locally (Whisper initially; now NVIDIA Parakeet), cleans transcripts with an LLM (Gemma 3), and tracks processing in DuckDB.
- From each day’s transcripts, he generates summaries, key topics/themes, quotable highlights, startup/company mentions for potential CRM enrichment, draft tweets, and venture “investment theses.”
- He also experiments with a blog-post generator that uses his archive (~2,000 posts) as style context, then iteratively grades and revises drafts using an “AP English teacher” rubric to reach an A-/~91 score before manual final editing/publishing.
IDEAS WORTH REMEMBERING
5 ideasTurn listening into a searchable reading workflow.
Tunguz prefers reading to listening because he can skim and jump ahead; converting podcasts to text makes high-volume audio content quickly digestible and easier to reuse.
A simple pipeline beats a “perfect” app for personal fit.
Instead of waiting for an off-the-shelf product, he built a terminal-based system that matches his exact workflow, then iterates quickly when requirements change.
Local-first can work—until a task needs bigger “brains.”
He aimed to run everything locally (Ollama, Parakeet, libraries), but found named-entity extraction improved dramatically when using more powerful LLMs rather than classic ML packages.
Transcript cleanup mattered more early than it does now.
Cleaning transcripts helped traditional NER tools recognize companies/proper nouns; once he switched to stronger LLM extraction, cleanup became less critical.
Quotes are the highest-leverage output for decision-making.
Among summaries, themes, and topics, he values quotable passages most because they’re fast to scan and often spark concrete next steps like market maps or thesis work.
WORDS WORTH SAVING
5 quotes“I have a list of 36 podcasts, but I don't have 36 hours every week to listen to 36 podcasts.”
— Tomasz Tunguz
“The part that's most valuable for me are these quotes.”
— Tomasz Tunguz
“Everything that I can easily replace with a single prompt is not going to have any value.”
— Tomasz Tunguz
“One of the techniques that I found the most effective… is to ask it to grade it like an AP English teacher.”
— Tomasz Tunguz
“An AI will only deliver you a grammatically perfect specimen.”
— Tomasz Tunguz
High quality AI-generated summary created from speaker-labeled transcript.
