Zesfy – Organize your daily tasks in under 30 seconds

Hacker News

Hi everyone, I’m the founder of Zesfy, a productivity app that I've been building over the past few years. I build Zesfy to help you quickly organize your daily tasks by showing your weekly to-do and letting you pick the task you want to tackle today with just one tap which cut daily planning time to just under 30 seconds. Here are some of its key features: - Automatic task progress - Group and schedule multiple tasks together to you calendar - Quickly filter events from specific set of calendars - Multi-level subtasks - Due date App Store: https://apps.apple.com/id/app/id6479947874 Let me know what you guys think.

Visit product View on Hacker NewsJun 25, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: apple, apps, tasks · Missing: mac, agents, macos

77%77% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

42%42% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released

40%40% 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

26%26% 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

22%22% 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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