Scrumbuiss – Project managment made easy

Hacker News

Hello everyone, I have been hard working over a year to make project Management app called Scrumbuiss. Scrumbuiss is all in, lightweight and user-friendly software solution for your projects to make it successfully. Core features: - Kanban boards - Project tracking - Project history and activity feed - Team management - Tasks creating with collaboration with your team - File uploads - Updates and notifications about changes in your Project - Intuitive, easy to use Dashboard to view latest changes - and many more... Why I didn't choose existing one? I feel like existing Project management solutions are totally overpriced. They mostly are very overwhelming for new Users, which demotivate from the start. I build Scrumbuiss in mind to be very easy to use, free to use for hobbyists, very cost-efficient for small-big and scaling business and very fast to boost the execution of every Project. If you have any questions, feedback or feature request, leave the comment below. Thanks in advance for any Feedback.

Visit product View on Hacker NewsAug 25, 2024

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: intuitive, friendly, efficient · Missing: plus, platform, reviews

60%60% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

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

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

13%13% 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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