Fi

Fitjoy – A more efficient keyboard layout I came up with

Hacker News

Fitjoy – A more efficient keyboard layout I came up with

Hi HN, I've been trying to improve my typing efficiency for a while and tried out various keyboard layouts like Dvorak, Colemak, Workman etc., but none really hit the spot. All the existing layouts assume the home/middle row is the best/easiest to reach. But, this is not necessarily true. The home row assumes all the fingers are of almost equal length, but thats not the case. In the standard placement above, the middle and ring fingers need to curve up more. When the palm is resting on the keyboard at an angle from the shoulder, the middle finger and ring fingers should naturally rest higher than the other fingers. This radically shifts the finger movement effort calculation. My hypothesis is that if we arrange the keys in such a way as to follow the natural length of different fingers, we can achieve a greater degree of efficiency. This is how I came up with FITJOY. As a programmer, I also realized that I tend to use the special characters a lot more than I use the numbers. So, the layout also changes the numbers row to print the special character (Eg. !#%^ etc.) by default, and have to press Shift to enter the number. * Layout file : https://github.com/aj0y/fitjoy * Related blog post: https://ajoys.medium.com/keyboard-layouts-brief-history-and-a-new-choice-8707b720d01f

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
89%89% 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: exist, existing, io · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos
51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
11%11% 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.

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