Keyboard Warrior – I built a speed typing game

Hacker News

Download: https://itunes.apple.com/ca/app/keyboard-warrior-speed-typin... Hi everyone, I built a speed typing game for iPhone inspired by the many desktop alternatives for testing your typing speeds. The concept is quite simple. You have 60 seconds to type as many words as possible. After each word, hit the spacebar or next key to type the next word. You will know your WPM (Words per minute) and KPM (Keys per minute) once the time is over. There are 7 levels in the game. The easy one consists of the top 250 most common english words. The hard level consists of 3000 most common words. The next difficult levels consist of capitalization, numbers, punctuations, random characters and so on. The game has a global leaderboard too where you can see where you rank. I was inspired by someone's side-project on reddit: https://www.reddit.com/r/SideProject/comments/9ckogk/crazy_t... and thought of building something similar for iOS. Without further ado, I would love for you to try Keyboard Warrior: https://itunes.apple.com/ca/app/keyboard-warrior-speed-typin... Android app is also in development and planning on releasing sometime this week. Any questions and feedback are welcome!

Visit product View on Hacker NewsSep 18, 2018

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

88%88% 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.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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: ide, 000, 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.

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

36%36% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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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