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Scribbler – Podcast Summaries Using GPT
Hacker NewsScribbler – Podcast Summaries Using GPT
Hey, we're Phil and Ian, the founders of Scribbler. We're huge podcast fans, but found we never had enough time to soak it all in. So, we built Scribbler - a tool that leverages GPT to condense podcast episodes into bite-sized summaries for when life's too busy. Now, we can catch the best bits from any episode, discover new shows, and best of all, stop wasting valuable time figuring out what's worth listening to and what's not. We hope you'll find it useful!
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91points
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Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Strong signals: new, using · Missing: mac, agents, macos80%80% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua · Missing: https docs, excited, just released39%39% 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, revenue15%15% 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, crypto3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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