Podwise – The premier AI powered learning app for podcast listeners

Hacker News

With an overwhelming number of podcast episodes being created and limited time available for listening, it has become a challenge for podcast enthusiasts. According to surveys, listeners typically have less than 7 hours per week to dedicate to podcasts, allowing for roughly 5 to 6 episodes. However, the global podcast landscape offers over 2 million podcasts waiting to be explored. To tackle this issue, Podwise comes to the rescue by providing a solution that allows users to make the most of their available time. With Podwise, you can learn from structured knowledge and selectively listen to chapters that pique your interest. This feature enables you to optimize your learning experience and focus on the content that matters most to you. Moreover, Podwise seamlessly integrates with popular tools such as Notion, Obsidian , Readwise , and more, streamlining your knowledge management workflow. By utilizing these integrations, you can enhance your overall learning efficiency and create a holistic approach to knowledge consumption.

Visit product View on Hacker NewsSep 26, 2023

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Actual performance

3points

Did not reach leaderboard

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

90%90% 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.

TrustMRRFits verified-revenue profile · Strong signals: users · Missing: mobile apps, ios, personal

75%75% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

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

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

9%9% 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

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

Correct prediction on native model

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