I built a daily mission app to become 1% harder every day

Hacker News

Hey HN. I wanted an app that would push me to get uncomfortable every day—cold showers, early wake-ups, running, and deep focus. But nothing felt right… so I built it myself. StayHard is a web app that gives you one mission per day per habit track (running, cold exposure, focus, etc.), and makes each one about 1% harder than the last. Think Duolingo meets David Goggins. - Choose your tracks: Run, Cold Shower, Wake-Up, Focus, Study, Strength - One-tap mission tracking with XP and streaks - Weekly leaderboard, public profiles, motivational layer - AI coach chat for those tough days - Built in 12 days using Next.js, MongoDB, and Stripe Would love your feedback. Bart

Visit product View on Hacker NewsJun 19, 2025

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2points

Did not reach leaderboard

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68%68% 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: stripe, using · Missing: mac, agents, macos

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

56%56% predicted probability of success on TrustMRR, 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

41%41% 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

36%36% 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

26%26% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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