Re-Imagining the College Essay

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Our goal is to make the college paper more engaging and shareable. On our site https://chalktips.com , users publish "booklets" which are counterparts to the traditional essay. Essay assignments, especially for the social-sciences, rely heavily on text documents and they are meant to be read only by the teacher. We would like to make these assignments cater to the 21st century writer while maintaining their intellectual integrity. Here are a few examples of student work. Please note that there are 2 layouts possible, booklets and slideshows: https://chalktips.com/t/view/e37b797e6f3c4f97bd088629509e5f9e (booklet) https://chalktips.com/t/view/8ce8c41333a743f696fd01984d19c5a5 (slideshow)

Visit product View on Hacker NewsFeb 11, 2013

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3points

Did not reach leaderboard

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

66%66% 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.

AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive

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

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

44%44% predicted probability of success on TrustMRR, 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

35%35% 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: user · Missing: mac, agents, macos

27%27% 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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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