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Smart Silence – Remind your iPhone to stay quiet in quiet places

Hacker News

Smart Silence – Remind your iPhone to stay quiet in quiet places

Hi HN, I built an iPhone app called *Smart Silence* after sitting in a quiet setting (a service, actually) where someone’s phone rang loudly and interrupted everything. It wasn’t intentional — just one of those forgetful moments. I started wondering: what if our phones could gently remind us to stay silent when it matters? *Smart Silence* helps iPhone users do just that. It lets you: - Mark places where silence is expected (like libraries, meetings, classes, or houses of worship) - Get a reminder when you enter, with an easy Shortcut to enable Do Not Disturb - Schedule quiet times (e.g. “Mondays 9–11am at this place”) - Share Silent Places with others — so your community, school, or workspace can use the same setup - Stay fully in control — no auto-silencing, and no location tracking outside of defined zones It’s currently available via TestFlight: https://testflight.apple.com/join/47CJ31VK I’d love feedback — especially around usability, edge cases, or features you’d expect. If you run or attend a place where silence matters, I’d love to hear your thoughts.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
75%75% 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, user · 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: users · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
49%49% predicted probability of success on AppSumo, 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
21%21% 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
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · 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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