A better way to track fitness goals (my nights and weekend project)

Hacker News

FitFrnd is a mobile app that helps you enjoy working out. - Flexible goals: Set an initial fitness goal. Adjust your goal as you make progress and learn about what you enjoy doing. - Share your success: Tell friends and family about workouts done and milestones achieved! Share on Facebook and Twitter. - Push your buddies: Encourage friends and comment on their workouts. - Smarts for fitness: Discover weekly patterns in your workout history, and get better at working out. - Be a winner: Kick ass and get into shape! FitFrnd is fun, simple and easy to use. It is your personal fitness companion and social butterfly rolled into one! Video Overview: http://youtu.be/_i3dWPdDdD4

Visit product View on Hacker NewsJun 25, 2012

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TrustMRRFits verified-revenue profile · Strong signals: personal, video, way · Missing: mobile apps, ios, entrepreneurs

66%66% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

49%49% 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: ide, io · Missing: https docs, excited, just released

40%40% 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: smart · 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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