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Daily AI Generated Trivia Podcast about Movies, TV Shows, and Books

Hacker News

Daily AI Generated Trivia Podcast about Movies, TV Shows, and Books

Hey HN Community, I just launched my latest project, a daily trivia podcast featuring AI-generated questions on movies, TV shows, and books - in emoji format. It's a bit of a strange project, I admit, but I thought it would be fun to try. Currently 8 episodes in. I used the Wondercraft AI platform to generate the audio, and I have to say it's been awesome. This project was actually inspired by a book I wrote, and I thought it would be a great way to promote it. You can check out the book here: https://emojipuzzlebook.com/ I know emoji's don't really work in an audio format, but I had a blast making this podcast, and I hope you'll enjoy listening to it as much as I enjoyed creating it. Link: https://www.buzzsprout.com/2185630/share I'd love to hear your feedback and thoughts. Thanks for your time and support. Happy listening!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
88%88% 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.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
43%43% predicted probability of success on AppSumo, 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
41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · 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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