On-Demand Podcast Notes and Takeaways

Hacker News

I have plenty of podcast episodes in my queue and I don't have time to listen to them. In addition, some of those episodes require taking notes, especially if they mention books/numbers/supplements/other podcasts. So, I decided to build this web app where I can request the podcast episode, and within a few minutes, I get the notes and takeaways. Two features that I absolutely adore are "highlights" and "episodes digest." If your memory is as fickle as mine, the "highlights" feature will be your savior. You can emphasize any part of the episode notes or takeaways, adding them to your highlights. Choose when you'd like to receive reminder emails, and you'll receive random snippets from your highlights. It's like having a personalized memory assistant at your fingertips! The "Episodes Digest" is another indispensable tool for the dedicated podcast enthusiast. Receive a comprehensive digest email containing all the episode summaries that have been released by the podcasts you follow within the system. Please share your feedback when you give it a try.

Visit product View on Hacker NewsAug 11, 2023

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: email, notes · 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.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

44%44% 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

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

34%34% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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 · 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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