Smol-podcaster, an AI intern for podcast hosts

Hacker News

I co-host the Latent Space podcast with swyx, and I've built smol-podcaster to automate a lot of the menial work that we do: - It uses Whisper to transcribe and label speakers from your audio with timestamps - Formats the transcription in Markdown for the show notes - Creates chapters with timestamps for each topic using Claude-100k - Generates title ideas as well as tweets with both Claude and GPT-3.5; I've found Claude to be better, but not everyone has access to it. You can find the code on Github [0]. We've been using this for all of our latest episodes to see a "demo", you can read the latest one with Tri Dao of FlashAttention here [1]. [0] https://github.com/FanaHOVA/podcast-summarizer [1] https://www.latent.space/p/flashattention#details

Visit product View on Hacker NewsAug 9, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, using, notes · Missing: mac, agents, macos

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

35%35% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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