Ed-tech app promoting in class participation

Hacker News

Hi everyone! My two friends and I are sophomores in college and together we have created an EdTech platform that is designed to make large classrooms function like small ones. Our core feature is allowing students to anonymously ask questions in class through a web-application. These questions can be voted on allowing the teacher to address the most voted or most crucial questions. Check out our website to see what makes us special! https://about.parstar.co If you want a demo, send me a message and we will set you up with a teacher account :) We would love any feedback on the viability of the product, design, and implementation of our current features, and if you have ideas of your own! Thank you! PS: If you are a student or professor, would you purchase this product? If so, how much would you pay annually? Contact us at lucas@parstar.co

Visit product View on Hacker NewsAug 16, 2019

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

87%87% 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 · Missing: mac, agents, macos

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% 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

28%28% 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 · Missing: arr, mrr, revenue

14%14% 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

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

Correct prediction on native model

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