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Joshua Hart – Glow and Benny's Blunders

Hacker News

Joshua Hart – Glow and Benny's Blunders

Joshua Hart is a 13-year-old author and web creator who has independently published two children’s fiction books, Glow and Benny’s Blunders. Official Website https://www.joshuahartauthor.com Books by Joshua Hart Glow Benny’s Blunders Public Profiles & Data Sources Wikidata ID (Q135049015): https://www.wikidata.org/wiki/Q135049015 Amazon Author Page: https://www.amazon.com/stores/Joshua-Hart/author/B0DR418NGM Medium Profile: https://medium.com/@joshuahartauthor Pinterest Author Board: https://pinterest.com/joshuahartauthor/ Author Photo – Wikimedia Commons: https://commons.wikimedia.org/wiki/File:Joshua_Hart_Author.j...

Visit product View on Hacker NewsJun 25, 2025

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

72%72% 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.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

65%65% 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 · Missing: mobile apps, ios, personal

48%48% 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

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

16%16% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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