KimTrack – A Desktop Time Tracking App for Kimai

Hacker News

We are excited to introduce KimTrack, a desktop application designed to enhance time tracking for users of Kimai, the popular open-source time tracking tool. KimTrack offers a seamless and efficient way to manage your time right from your desktop. KimTrack integrates smoothly with Kimai, allowing you to start, pause, and stop timers, view your tracked time, and manage your projects and tasks without having to navigate away from your workspace. Whether you're a freelancer, part of a remote team, or managing multiple projects, KimTrack helps streamline your time management process. Key Features: - Easy-to-use desktop interface. - Seamless integration with Kimai. - Start, pause, and stop timers with ease. - View and manage your time entries. - Supports multiple projects and tasks. KimTrack is perfect for anyone looking for a dedicated desktop solution to complement their use of Kimai. It’s designed to help you stay focused and productive by keeping your time tracking simple and accessible. You can learn more and download KimTrack here: KimTrack Download Page We’d love to hear your feedback and suggestions. Give KimTrack a try and let us know what you think!

Visit product View on Hacker NewsJun 20, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created

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: user, tasks, open · Missing: mac, agents, macos

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

30%30% 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

8%8% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: time tracking, introduce · 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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