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<imgsrc="docs/img/banner.png"alt="podmind — always remember everything you ever heard. Listen → Ingest → Summarize → Link → Ask, plus the AI minds it remembers ranked by how often you heard them."width="880">
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<palign="center"><em>Always remember everything you ever heard.</em></p>
A [Karpathy-style LLM-built knowledge wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) of everything you actually listen to.
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**You've listened to thousands of hours of podcasts. Quick — what did the last one actually say?**
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podmind is the memory you wish your ears had. Point it at your Pocket Casts and YouTube history and it quietly builds a [Karpathy-style LLM knowledge wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) of everything you've *actually* heard — one cross-linked markdown page per episode, plus people, topics, and show pages, with a listening-state badge on every citation so you can tell what you truly listened to from what's just been sitting in your queue.
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Pocket Casts listening history and YouTube watch history go in; an Obsidian-compatible, cross-linked markdown wiki comes out — one page per episode, plus people, topics, and show pages, listening-state badges on every citation, and an analytics layer over your listening history. The wiki compounds: every ingest and every query leaves more cross-links behind.
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Listening history goes in; an Obsidian-compatible second brain comes out. And it compounds: every ingest and every query leaves more cross-links behind.
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## Why podmind
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-**You already did the listening — keep the knowledge.** Hours of audio leave almost no durable trace. podmind turns listening you've *already done* into something searchable.
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-**Answers from your sources, not the web.**`podmind query "..."` synthesizes a cited answer from the episodes you actually heard — not a generic chatbot guess.
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-**Plain markdown you own forever.** Obsidian-compatible files on your disk. Greppable, portable, and they outlive every SaaS knowledge tool.
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-**It builds itself.** A daily job pulls new listens, transcribes, summarizes, and cross-links — hands-off while you sleep.
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-**Cheap and local-first.**~$0.015/episode for summaries; transcription runs free on Apple Silicon.
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## What it produces
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Run podmind over an episode and you get a page like this — structured takeaways, quotes with timestamps, and real cross-links, topped with a listening-state badge (`🎧` heard · `▶ 38%` in progress · `⚪` unheard):
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```markdown
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---
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show: "Signals & Noise"
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date: 2026-05-16
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guests: ["Daniel Voss"]
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---
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▶ 38% [[shows/signals-and-noise]] — Daniel Voss on cost-per-token engineering:
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your LLM bill is input tokens, and the cheapest model that emits valid JSON wins.
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## Key takeaways
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- Summarization cost is dominated by input tokens (~90%), so the bill scales
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with transcript length, not model intelligence.
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- Three levers — truncation, prompt caching, and model routing — cut costs by
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two thirds before any prompt tuning.
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## Notable quotes
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> Your bill is determined by how much you make the model read, not by how smart
Every `[[link]]` resolves to a real page. Topic and people pages gather every episode that touched them — so *"what have I actually heard about LLM costs?"* is a grep away: filter a topic page's citations for `🎧` and you get exactly the claims you've personally listened to, not just subscribed to.
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## What it looks like at scale
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One real vault, built passively over ~10 years of listening:
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|---|---|
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|**14,789** episodes tracked |**8,789** hours of audio |
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|**1,720** shows & channels |**9,376** episodes distilled into the wiki |
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|**19,467** people mapped |**30,589** topics — every one cross-linked |
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The busiest threads run from Bitcoin (562 citations) to AI agents (260) to geopolitics — the wiki maps whatever you actually feed it.
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## How it works
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Three layers, strictly separated:
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WIKI --> STATS[stats.md + charts]
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```
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Every episode citation in the wiki carries a **listening-state badge** derived from the raw metadata: `🎧` fully listened, `▶ 42%` in progress, `⚪` not yet heard. That makes *"what have I actually heard about X?"* answerable with a grep — filter topic-page citations for `🎧` and you get exactly the claims you've personally listened to, not just subscribed to.
**Default is DeepSeek — put one key in `secrets.json` and you're done.** podmind
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also speaks OpenAI, OpenRouter, Ollama, and Anthropic; see
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[docs/OPERATIONS.md](docs/OPERATIONS.md#llm-provider-configuration) for the
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provider matrix and embeddings config.
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**Default is DeepSeek — put one key in `secrets.json` and you're done.** podmind also speaks OpenAI, OpenRouter, Ollama, and Anthropic; see [docs/OPERATIONS.md](docs/OPERATIONS.md#llm-provider-configuration) for the provider matrix and embeddings config.
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### First sync
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Loads a daily 04:00 pipeline + an AC-gated whisper loop. Details in [docs/OPERATIONS.md](docs/OPERATIONS.md#automation-macos-launchd).
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## Pocket Casts disclaimer
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Pocket Casts has no public API. podmind talks to the same private endpoints the Pocket Casts web player uses — like other open-source Pocket Casts clients. It can break without notice, and you should consider whether that's acceptable use of your account.
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## Cost
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~**$0.015/episode** on the default DeepSeek path (measured); a 100-episode run ≈
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$1.50. Whisper transcription is free locally on Apple Silicon. Full breakdown in
~**$0.015/episode** on the default DeepSeek path (measured); a 100-episode run ≈ $1.50. Whisper transcription is free locally on Apple Silicon. Full breakdown in [docs/OPERATIONS.md](docs/OPERATIONS.md#cost-discipline).
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## Commands
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