fffocus.com, a minimalist pomodoro timer for your browser homepage

Hacker News

Hi everybody, check out this minimalist pomodoro timer I whipped up this weekend: http://fffocus.com - click the title and identify your next action - click the circle to start the timer. The default time is 25 minutes - click again to pause the timer, or double click to reset it - if you navigate away from the page and come back, it should keep track of the elapsed time Now you can set fffocus.com as your browser homepage, and have a constant reminder to stay on task. It's all driven driven by a static html page and a bunch of object-oriented Javascript, using Knockout.js, HTML5 local store, and some other goodies. Code is up on Github: http://github.com/dylanized/fffocus Love to hear any feedback, especially bug reports (this is untested in IE!). Happy fffocusing!!

Visit product View on Hacker NewsMar 24, 2013

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, code · Missing: mac, agents, macos

56%56% 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

41%41% 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

38%38% 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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

26%26% 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

16%16% 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

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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