Sl

Slack app to manage messages overload

Hacker News

Slack app to manage messages overload

Many of us wake up to dozens of messages on Slack, feeling overwhelmed when we start our days. My friends and I worked on a side project to help people manage this daily Slack struggle. You can use Rewind (Slack app) to tag any message in your Slack workspace and find it easily later. Think of it like Slack’s saved items, but better. You can use Rewind to tag a message as ‘todo’, ‘readlater’, ‘feature-research’, ‘togetback’, pretty much anything you would like. The app is great if you have a lot going on in your Slack. I think many of us moved from Gmail to Slack and find it really hard to manage all the incoming messages, so I'm hoping Rewind will be useful for a lot of people. Check it out here: https://www.rewindapp.io/

Visit product View on Hacker NewsJun 15, 2022

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

79%79% 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 · Missing: supports, reddit linkedin, podcasting

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, 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

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 · Missing: plus, platform, intuitive

17%17% predicted probability of success on AppSumo, 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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