LookAway – My first native macOS app to combat digital eye strain

Hacker News

Hi HN! I'm Kushagra. I created LookAway after suffering from eye strain due to working for long hours staring at my screen. The app nudges you to take regular breaks by blurring the screen to help reduce eye strain. It gives a heads-up before each break so you can wrap up tasks, and you can adjust the break intervals and durations to fit your routine. LookAway is smart - it works when you work, pauses when you don't, and handles idle time intelligently. I’d love to hear your feedback to improve it. Check it out at https://lookaway.app . Thank you for your time!

Visit product View on Hacker NewsOct 18, 2023

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Actual performance

22points

14comments

Made the leaderboard

Launch Intel predictions

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

73%73% 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 HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

41%41% 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: way · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% 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.

Incorrect prediction on native model

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