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NoZeroDay – strictly-enforced daily streak challenges

Hacker News

NoZeroDay – strictly-enforced daily streak challenges

I got fed up with habit apps that let you "make up" for missed days or give you endless second chances. What's the point of a streak if you can just fudge it when life gets messy? So I built NoZeroDay around one brutal rule: miss a single day, and your challenge dies. No exceptions, no mercy streaks, no "oops I forgot" buttons. Here's how it actually works: You pick a challenge length (7, 14, 30, or 66 days) and commit to showing up every single day with proof — could be a quick text update, a photo, even a voice note about what you did. The format doesn't matter, but the consistency does. Miss one day? Game over. Hit every single day? You actually earned that streak. Why I made it this way: I was tired of apps that felt like participation trophies. The whole point is that it should be hard to maintain a real streak. When there's no way to cheat the system, you stop trying to cheat yourself. The psychology is pretty simple when failure is immediate and final, you find a way to show up. Even if it's just 5 minutes before midnight logging "did 10 pushups in my pajamas." Want to try it? Jump in and start a challenge right now no signup fees or trial periods. I'm really curious to hear what you think. Does the zero-tolerance thing actually motivate you, or does it just stress you out? And how's the overall experience feel? Honestly, I built this for people like me who need that extra kick in the pants, but I'm wondering if I'm the only weirdo who finds the harshness motivating.

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Actual performance

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
84%84% 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: apps, single · Missing: mac, agents, macos
64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: apps, way · Missing: mobile apps, ios, personal
60%60% 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
42%42% 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
34%34% 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
21%21% 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
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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