Prostir: Planning doesn't have to feel like pressure

Hacker News

Create a space where your to-dos don’t create pressure. This app is made for those who want to plan without the stress. Inside, you’ll find: - To-do list with time, repetition, and importance options. - Notes with checklists and reminders. - Folders to organize everything. - A soft, supportive interface – no harsh red warnings, no pushy notifications, no guilt. - No pressure to complete everything right now – your well-being comes first. This is a space where planning adjusts to you, not the other way around. AppStore: https://apps.apple.com/us/app/daily-planner-prostir/id653569...

Visit product View on Hacker NewsMay 23, 2025

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

68%68% predicted probability of success on TrustMRR, 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, apps, notes · Missing: mac, agents, macos

67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

43%43% predicted probability of success on Indie Hackers, 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

37%37% 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

19%19% 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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