A Podcast Q&A App Powered by GPT

Hacker News

DrPawd is a GPT powered app for answering questions from podcasts. Not only does it provide the answer but also provides the timestamps in the relevant episodes where the hosts discuss the topic. It also features a personalized episode page where the user can ask questions specifically to the episode, read summary of the episode and find related episodes which they might find interesting. Right now some of the most popular podcasts such as HubermanLab, Peter Attia's TheDrive, StarTalk and Stuff You Should Know are supported with more to come. This is still early stage and UI needs more refinements but happy to hear critical feedback.

Visit product View on Hacker NewsMar 27, 2023

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Did not reach leaderboard

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

79%79% 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

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

54%54% 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

33%33% predicted probability of success on AppSumo, 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

31%31% 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

11%11% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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