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Lvlup – Habits and Journal

Hacker News

Lvlup – Habits and Journal

Hi, I am excited to share something i've been working on for a while with you! lvlup is a fun and efficient habit trainer which uses innovative science-based methods and gamification to systematically help you build life-changing habits and get you closer to your goals. It follows a gradual habit-building process accompanied by self-reflection tools and personalized habit-specific reminders to keep you motivated along the way. Main concepts: - Start with a habit so easy you can’t say ‘no’ to it and then slowly build up. Along the way, your willpower and motivation will increase, making it easier to stick to your habit for good. - If you are struggling with a habit, split it into smaller sessions to make it easier to accomplish and maintain momentum. - Journal and reflect on your past progress and mood to keep yourself motivated and figure out why you are crushing some habits, and struggling with others. - Complete your habits consistently and unlock new achievements, titles, avatars and more fun rewards. I am looking forward to getting your feedback on lvlup! Thanks, Erol

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
84%84% 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 HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos
45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
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: efficient · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, io · Missing: https docs, just released, exist
36%36% 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
19%19% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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