A simple offline-first app to track your reps in the gym

Hacker News

Developer here. Thanks for checking out my app, Riker. In case anyone is curious, the iOS app is native, written in Obj-C. The Riker web app is written using React and Redux. The REST API is written in Clojure and the backend is Postgres. Although there is also a fully functional web version of Riker, the app is preferred since it supports offline mode, provides Watch App and integrates with Apple's Health app. Will be happy to answer any questions. App Store link: https://itunes.apple.com/us/app/riker/id1196920730?mt=8 Web link: https://www.rikerapp.com

Visit product View on Hacker NewsJan 2, 2018

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Launch Intel predictions

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

81%81% 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: apple, using · Missing: mac, agents, macos

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

62%62% 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

47%47% 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

42%42% 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

19%19% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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