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fffocus – a simple tool that helps you clear your mind before work

Hacker News

fffocus – a simple tool that helps you clear your mind before work

The url can be found in the first comment. To scratch my own itch, I created a simple tool that should be helpful to many of us who are under constant distractions. If anyone uses this and finds it effective or ineffective, please kindly share your experience. Below are some ways I think it can be put in use: 1. Before we start a work day, we can use it to clear up random thoughts and anxiousness. 2. We can use it regularly to increase our attention span. Note: This was posted several years ago. I only made very minor changes since last time. I'm reposting it and hope to see some feedback on its usefulness. Sorry if it is a waste of your time.

Visit product View on Hacker NewsNov 30, 2019

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

63%63% 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

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

52%52% 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

47%47% 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

14%14% 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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