A New Flutter App for Mountain Biking

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I have been working on a new mountain biking app for the last year called Hangtime. It is a free ride computer app that uses a smartphone's GPS, accelerometer, gyroscope, and altimeter to track a mountain bike ride. After recording a ride, the user can go back and see their performance in turns, jumps, climbs, etc. Below is a video that shows off some of the app's features. https://youtu.be/TXiwzf-UJhQ I wasn't sure I could get the performance required from Flutter/Dart vs. native as a one hour ride generates well over a million data points from all of the sensor data. However, performance has been more than adequate and the single code base has streamlined development. If you want to give it a try, below are links to the app stores. https://play.google.com/store/apps/details?id=com.rlued.hangtime https://apps.apple.com/us/app/mtb-hangtime/id1516615267

Visit product View on Hacker NewsApr 25, 2021

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

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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Product HuntOn track for Day 1 leaderboard · Strong signals: apple, google, apps · Missing: mac, agents, macos

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

58%58% predicted probability of success on TrustMRR, 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

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% 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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