PodSnap.AI – automatic AI summaries for your favorite podcasts

Hacker News

Hi HN, A couple months ago, a friend mentioned that there are too many great podcasts to keep up with. We searched for a solution but didn't find one and realized that many other listeners likely face the same problem. So, I built PodSnap.AI, a service that keeps track of new podcast episodes and automatically sends AI-generated text and audio summaries to your inbox as soon as they are published. This way, users can quickly get the key insights and decide whether to watch the full episode. The service supports podcasts on Apple, Spotify, and YouTube. It's still beta, so I'd highly appreciate any feedback or suggestions for improvement. Thank you!

Visit product View on Hacker NewsAug 31, 2024

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Actual performance

9points

6comments

Made the leaderboard

Launch Intel predictions

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

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: soon, users · Missing: plus, platform, intuitive

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

47%47% predicted probability of success on TrustMRR, 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

40%40% 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

19%19% 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.

Incorrect prediction on native model

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