A curated literature program for K-12

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Pageturners is a new product for school-going students from Great Books Homeschool, which I started based on my experience designing a secular literature-based homeschooling curriculum for my kids. Schools typically don’t allocate enough time to simply reading great books, and the majority of students don’t read for enjoyment. As a result, kids don’t get the exposure to literature that they need to read more challenging works in college. Pageturners aims to fill in the gap with a curated reading program that gently progresses from picture books in kindergarten to adult classic literature in high school. It’s customizable but starts with sensible defaults that include the most iconic works of children’s literature and Great Books, both classic and contemporary. The kindergarten level is free. For other levels, there's a free trial and it's currently priced at $3.99/month paid annually thereafter.

Visit product View on Hacker NewsDec 30, 2023

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

89%89% 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 · Missing: https docs, excited, just released

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

44%44% 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 · Missing: mobile apps, ios, personal

39%39% 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

37%37% 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.

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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