I Built an Indoor Cycling Web App to Get in Shape Through Gaming

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I spent the past three years developing an exercise game for indoor smart trainers called Startona. The game is built from scratch in TypeScript and only uses a couple of small libraries. The result is a PWA that showcases the capabilities of a modern web browser through technologies like Web-Bluetooth and WebGL. Developed to turn turbo-trainer workouts into intense gaming sessions, Startona combines structured workouts with exercise games that boost specific aspects of the player's fitness, such as speed, power, and endurance. Although Startona is built for fun by providing new and unique indoor cycling experiences, the game also contains all the features you would expect from a modern indoor cycling app: single-player workouts, multiplayer challenges, personalized structured workouts, detailed activity logs, built-in Strava support, regular content updates, and various fitness tracking systems to help players stay motivated. All that is needed to try Startona is a smart trainer and a web browser with Bluetooth support (Chrome, Edge, Arc...). Sorry, no iOS support yet.

Visit product View on Hacker NewsApr 25, 2024

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

92%92% 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.

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

60%60% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: new, single, activity · Missing: mac, agents, macos

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

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

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

52%52% 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 · Strong signals: smart · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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