Ex

Exercise snack reminders for iOS/Android, no server, one-time purchase

Hacker News

Exercise snack reminders for iOS/Android, no server, one-time purchase

I built Snack App for my wife. She works from home, and every night, she would feel stiff and creaky after sitting all day, so she decided that she had to be less sedentary. Her idea was to break up her day with "exercise snacks" - formally defined as "isolated ≤1-min bouts of vigorous exercise performed periodically throughout the day" ( https://pubmed.ncbi.nlm.nih.gov/34669625/ ) but really any kind of movement (a couple flights of stairs, a 5-minute walk, or a set of air squats) is better than nothing. So I wrote her an app that lets her set a goal and pick a reminder schedule. The app sends her notifications; she logs her snacks in the app; and every day she meets her goal extends her streak. Speaking of streaks, I forgot to do my Wordle one day and, to my horror, broke a 171-day streak, so I made Snack App's streaks flexible. You can pause your streak anytime, and it'll resume when you start logging snacks again. There's also a built-in grace period, just in case you forget. The whole point is to help you feel less stiff and creaky, not to break your heart. I had knee surgery last month, so I've been using the app too, to help me stay on top of my physical therapy. I also set an exercise-specific goal of at least 30 push-ups a day so that I don't wither away completely, since I can't go climbing for at least a couple more months. App Store: https://apps.apple.com/us/app/snack-app-exercise-reminders/i... Google Play: https://play.google.com/store/apps/details?id=com.wobblerllc... - No accounts, no ads, no subscription - 14-day free trial, then $4.99 one-time purchase if you want to keep logging snacks (you can always view your existing data) - Export your data as CSV anytime - Local-only (no server, your data lives in a SQLite database on your phone) - Optional automatic backup to / restore from your personal iCloud or Google Drive (so your data is safe if your phone dies; open the app on a new phone, and you'll be prompted to restore your data from a backup) - Manual backup to Dropbox or other cloud storage service, if you prefer - Tested with VoiceOver and TalkBack and against WCAG accessibility standards (I want the app to work as well for blind, DeafBlind, and low-vision users as it does for sighted users) The stack: Bun, Expo, expo-sqlite, Drizzle ORM, Gluestack UI, React Hook Form, Zod, Maestro for E2E testing, bun:test for unit testing Happy to answer questions about the stack, the streak design, or anything else.

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Product HuntOn track for Day 1 leaderboard · Strong signals: apple, google, apps · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: wife, ios · Missing: supports, reddit linkedin, podcasting
89%89% predicted probability of success on Indie Hackers, 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: exist, existing, ide · Missing: https docs, excited, just released
38%38% 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 · Strong signals: users · 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 · Strong signals: subscription · Missing: arr, mrr, revenue
25%25% 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.

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