Typing Cyclist

Hacker News

Typing Cyclist

The app started from the idea to see if we can make a developer friendly type training app. One thing this app should accomplish is being fast and intuitive with minimal distractions. Another requirement was that it should help with training your typing speed when writing code. Currently the demo app comes with a couple of modes and settings. Most importantly the are three difficulty levels (basic, intermediated and advanced) as well as a code mode (for training your type speed when writing code). Furthermore we have some rudimentary statistics (like avg. typing speed over time and error rate on a per key basis). Finally as the name implies, you should be able able to cycle through the training via shortcuts, being able to navigate from one course to the next, without having to leave the keyboard avoiding context switches. There are more features like focus mode (larger text, zero distractions) and even the possibility to add your own custom texts and try with them. Last but not least, maybe the most important feature: dark mode. You can try the demo over at http://typing-cyclist.vercel.app , let us know what you think.

Visit product View on Hacker NewsSep 11, 2022

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Actual performance

2points

Did not reach leaderboard

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

82%82% 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: context, code · Missing: mac, agents, macos

71%71% 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: ide, io · Missing: https docs, excited, just released

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive, friendly · Missing: plus, platform, reviews

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

28%28% 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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