I built a service for generating summaries of any podcast

Hacker News

Hey HN! Around a year ago, I had the idea of using generative AI to automatically create notes from podcasts, so that it would be easier to resurface stuff I had listened to in the past. I spent a few weeks creating an MVP by combining OpenAI Whisper with an LLM pipeline, and it worked surprisingly well. In just a few minutes, the tool could generate written summaries of pretty much any podcast. I dubbed it Snipcast, and I've added a lot of features and improvements since then. You can now use it with any major podcast platform (Spotify, Youtube, Apple, etc.), get timestamps in the summaries, access full transcripts, and even upload your own audio files for summarization. In March this year I launched a subscription tier, which right now has 50 paying users. It has been really cool going all the way from an idea in my head to a full product that some people actually want to pay for :D If this sounds interesting, Snipcast is free to try, with no registration required. I'm looking forward to hearing your feedback!

Visit product View on Hacker NewsJul 16, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: apple, user, openai · Missing: mac, agents, macos

89%89% 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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, io · Missing: https docs, excited, just released

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

18%18% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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