Workout Quest – Gamified Workout Tracking App on iOS and Android

Hacker News

Hey all! I'm working on an app called Workout Quest and I'm at a point where getting some feedback would be really helpful. The app is all about making fitness tracking fun and effective and does this by adding experience points, levels, and achievements to the workout tracking process. As a backend developer, this is my first foray into React Native, I've used a few libraries like React Query and Zustand to handle a lot of the brunt of the frontend logic. I'm really interested in getting thoughts on the UX/UI, though. How can it be more user-friendly and engaging? What do you usually find missing or annoying in fitness apps? I'm a developer first and have really only learned a small bit about UI and UX, it's a real weak point of mine. As a product, I've offered AI-powered workout generation as a premium feature, however I'm not sure how to fairly monetize the app. The app is still in development, so your feedback could really make a difference in how it shapes up. If you're interested in checking it out, you can find it here: https://www.workoutquestapp.com/ Thanks so much for any advice or insights you can share!

Visit product View on Hacker NewsNov 28, 2023

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

89%89% 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 · Strong signals: apps, user · Missing: mac, agents, macos

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% predicted probability of success on AppSumo, 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

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

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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