Study League – Gamified, AI-Driven Study Platform with Instant Rewards

Hacker News

As a student who struggled to begin studying, I built Study League to make revision more rewarding and engaging. Study League is a gamified study platform where users earn points based on study duration, not just exam outcomes. My custom algorithm, which takes into account the users study time to promote longer sessions and sessions with breaks, and the users team already to promote progress, scores each session, and users can immediately redeem points to "buy" players, creating an immediate sense of achievement. With these players, users form teams, compete in 'study matches' with friends, and join leagues to track and compare progress—transforming revision into a shared, motivational experience. If you can, I would appreciate feedback as this is one of the biggest projects I have made. Thank you.

Visit product View on Hacker NewsOct 31, 2024

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40%40% 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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

20%20% 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

15%15% 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

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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