Sweet Kudos – Employee recognition, rewards and celebrations in Slack

Hacker News

My friend and I developed a Slack Bot that brings employee recognition, rewards and celebrations to Slack. We did our best to make this app user-friendly and are looking for our first users. Main Features of SweetKudos: Employee Recognition Change the number of allowed Kudos anytime Change Kudos emoji anytime Recognize groups of people Recognition via Reactions Leaderboards Reporting Analytics User roles Features that we are proud of: Rewards System with features such as Community rewards and Hidden Rewards Celebrations bot (Birthday and Work Anniversary bot) integrated and without no additional charges Use the bot on an unlimited number of channels, including private ones. Learn more about Sweet Kudos: sweetkudos.com Add Sweet Kudos to your Slack workspace: sweetkudos.com/register Thanks

Visit product View on Hacker NewsApr 17, 2024

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

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Product HuntUnlikely to reach the leaderboard · Strong signals: slack, user · Missing: mac, agents, macos

45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

40%40% 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: friendly, 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 · Missing: arr, mrr, revenue

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: reward · Missing: web3, chat, crypto

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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