A crowdsourced teaching platform. May the best teachers win.

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I'm a long time lurker on HN, and among other projects, about two years ago I began creating a textbook replacement. www.forgetthetextbook.com In short, it's a free, crowdsourced, and dynamic online textbook that rewards quality instruction. It's ideally a platform anyone can learn or teach from in every subject, and I hope it will harness the Salman Khan's of the world. Reddit gave me some great feedback over a year ago, but I've been so busy with my normal life and day-job that it has fallen to the wayside. It's still rough around the edges, but I wanted HN's opinion of the concept before I put more effort towards it. Any input would be great.

Visit product View on Hacker NewsJul 11, 2012

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

68%68% 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: ide, io · Missing: https docs, excited, just released

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

28%28% predicted probability of success on Product Hunt, 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: reward · 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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