Group accountability for atomic habits (iOS)

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I started this project by running 10-day group challenges on Whatsapp. The group was gathered via a Webflow landing page, Google forms, Zapier and a PayPal button (here’s what I posted on HN at that time - https://news.ycombinator.com/item?id=32363731 ). The WhatsApp challenges turned out to be a hit with over 85% checking in daily and 45% repaying to join multiple challenges. After having tried Streaks, Habitify, GTD, reminder apps, google calendar, Notion...etc—I was still having trouble staying consistent with my personal/wellness goals and habits. Methods from Atomic Habits by James Clear—are what worked best for me—but sticking to it alone was hard. For example, I had a daily push-up reminder on my google calendar that I ignored for almost a year. Fast forward, I’m now on day 120+ doing push-ups, meditation and waking up before 6am—after participating in group accountability challenges :) To see if we can scale it to help more people and potentially build a community-driven business—we just released an iOS app. Recap of how it works: - Join 10-30 day accountability challenges in small groups of 3-12 people - Commit a daily goal and check-in with the group on whether you didn’t or didn’t do it - Daily visual progress reports, reward credits/penalties and support from others helps everyone stay on track - Create and join private challenges with friends Would love to have you try it by joining a challenge—and see if could help you become the person you want be for 2023!

Visit product View on Hacker NewsJan 15, 2023

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

91%91% 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: google, apps, new · Missing: mac, agents, macos

70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

68%68% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: just released, io · Missing: https docs, excited, exist

43%43% 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

30%30% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: reward · 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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