Po

Podshorty – Summarize YouTube Videos into Compact Audio Digests

Hacker News

Podshorty – Summarize YouTube Videos into Compact Audio Digests

Hi all, I've been working on Podshorty for the past few months. The idea is simple: summarize and compress lengthy YouTube videos into concise audio versions using the voices of the original speakers. For those who prefer reading or need a quick overview, we also offer a text transcript of the summarized content. I'd appreciate any feedback you might have. Thanks!

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos
61%61% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
52%52% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, month · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto
33%33% predicted probability of success on BetaList, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Incorrect prediction on native model

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