Blind Valet

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Blind Valet

Blind Valet (http://blindvalet.com) has been a hobby project of mine for a number of years. It started as a blind structure calculator for poker tournaments and is has now evolved into a full live poker tournament management software. I released a new version yesterday that is completed redesigned and rearchitected from the ground up. The old version was a stand-alone flash web application. This new version is all HTML 5/JS. It is responsive across all devices and multiple devices can connect to the same tournament with real-time data synchronization. So, in practice this allow tournament directors to hook up their computer to the big screen, yet control the entire tournament from their phone (starting/pausing the clock, eliminating players, balancing tables, etc). I built this version using AngularJS as the front-end framework and I am using Firebase on the back-end that provides the NoSQL data storage and real-time data synchronization over web sockets. The application is free to use. I am monetizing through non-intrusive advertisements on the poker clock and down the road will add some paid options above and beyond what is free today. Would love to get your feedback.

Visit product View on Hacker NewsMay 2, 2016

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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

83%83% 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.

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

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: computer, new, using · Missing: mac, agents, macos

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

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

39%39% 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 · Strong signals: paid · Missing: web3, chat, crypto

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

Incorrect prediction on native model

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