BLDG EVELYN – a drama, fiction storytelling podcast

Hacker News

Hello HN! I am a marketer and my side project for the last 4 months has been creating an audio drama series called BLDG EVELYN. It is a twice-monthly podcast about the crazy life and family of Evelyn Munoz in Brazil, all inspired by true events. Each chapter is written by a different writer and they put their own spin to it. I grew up in Brazil and draw from my childhood to inspire Evelyn’s story. BLDG EVELYN is both a rewarding spiritual journey and a mission to share her work with others. The episodes are narrated and the story is told much like an audio book. You can find the first four episodes at www.bldgevelyn.com If you do listen to any of the episodes, please let me know your thoughts! I am looking to spread the word and reach new listeners. Cheers! Jackie

Visit product View on Hacker NewsApr 26, 2020

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

57%57% 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 HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos

52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, monthly · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, 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.

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

20%20% 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 · Strong signals: arr · Missing: mrr, revenue, profit

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: reward, audio · 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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