An easy way to do more focused work in Slack

Hacker News

The easiest way to describe Focus Mode is, ‘It’s the Pomodoro technique — designed exclusively for Slack.’ It lets users add the task they’re working on, set how long they’d like to focus on it, and how long the break should last between each focus period. In essence, it’s a nifty way to bring more focused work to the busy workspaces of Slack. The creators of Focus Mode are my team at Geekbot: the custom Slackbot that helps remote-first companies (including the likes of Zapier, Shopify, Zendesk) better manage routine tasks, like daily standups, in Slack. So to say we’re at home in Slack is an understatement: and our Geekbot experience was invaluable in helping us see what else was needed to make Slack a more focused, less distracting workspace. We’re really proud of Focus Mode because it’s simple to use (just three fields to fill in), it keeps your teammates in-sync with what you’re working on (that’s what the Geekbot mission is all about), and it auto-enables ‘Do Not Disturb’ — meaning you can get on with important tasks and avoid notifications from dominating your day.

Visit product View on Hacker NewsJul 16, 2020

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

91%91% 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: including · Missing: supports, reddit linkedin, podcasting

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: users, way · Missing: mobile apps, ios, personal

79%79% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

48%48% 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: exclusive, users · Missing: plus, platform, intuitive

24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

19%19% 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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