Ap

AppTimer-GUI – A Linux Open-Source Tool to Limit App Usage

Hacker News

AppTimer-GUI – A Linux Open-Source Tool to Limit App Usage

Hey HN, I’ve just released AppTimer-GUI, a productivity tool designed to help you manage your screen time more effectively. Whether you’re looking to reduce distractions, limit app usage, or maintain a better work-life balance, AppTimer-GUI provides a simple and intuitive way to control how long applications run on your system. Key Features: Graphical Interface: A user-friendly GUI built with the Fyne toolkit. Flexible Time Units: Choose between seconds, minutes, or hours for your timer. Pause and Unpause: Temporarily stop the timer and resume when you’re ready. Stop and Kill: Instantly stop the timer and close the app at any time. Use Cases: Enhance Focus: Limit access to distracting apps while working or studying. Control Screen Time: Set strict usage limits on entertainment apps to avoid overuse. Work-Life Balance: Manage work-related app usage to ensure you don’t overwork. Get Involved: I’d love to hear your feedback, suggestions, or ideas for improvement. Contributions are also welcome! You can check out the project on GitHub. Background: This tool is an evolution of a CLI-based version I previously created, which you can also find on GitHub: https://github.com/Varrow1/AppTimer

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
82%82% 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: apps, user, open · Missing: mac, agents, macos
76%76% 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: just released, ide, io · Missing: https docs, excited, exist
46%46% 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: intuitive, friendly, interface · Missing: plus, platform, reviews
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, way · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
11%11% 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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