39 karma · joined December 14, 2025
I probably stared at it for too long on my own monitor and got used to it. I will bump up the contrast in the next update to ensure it's readable for everyone.
My interpretation for your power supply:
Gathering (45): You need to gather some capacitors? (or maybe a heat sink for the 'massing'?)
Following (17): Clearly it's telling you to use a Voltage Follower. :D
Have you built a web app or a mobile app? I'd love to check out your work if you have a link!
You are raising a critical UX issue. Currently, I haven't implemented user accounts or a database because I wanted to keep the app simple and login-free.
However, that shouldn't mean losing your data on a refresh! I will prioritize a fix using Local Storage so your current reading persists even if you hit the back button.
I'll also make the result link much more obvious so you don't have to hunt for it. Really appreciate you trying it out despite the rough edges!
To be honest, my reason for picking this method was simple: I was reading a book about the I Ching that described the different ways to cast hexagrams.
The Yarrow Stalk method stood out to me because it felt more mysterious—in the past, it seemed like a secret method known only by a few experts.
Also, from a coding perspective, this algorithm was just much more interesting to build than a simple coin toss!
To be honest, I haven't added it because I'm hesitating. I planned to use AI to generate the interpretations, but I'm not sure if that's a good idea.
I feel like letting AI explain the I Ching might kill the 'traditional vibe'.
I'm still deciding: should I use AI, or just use the classic book text? What do you think?
You are right that humans don't split perfectly randomly (Uniform Distribution). We tend to aim for the middle but miss slightly.
In my code, I modeled this 'human splitting action' using a Gaussian (Normal) distribution centered at 50% of the pile, with a standard deviation. This simulates the user trying to split the stalks roughly in half, rather than just picking a random number from 0 to 49.
Interestingly, my Monte Carlo simulations showed that even with this human bias (splitting near the middle), the final modulo-4 probabilities remain stable. So the algorithm is robust even against our 'imperfect' hands.
I just identified the issue (it was a specific mobile browser compatibility bug). I have deployed a hotfix and it should be working now on iOS.
Could you please give it another try? Sorry for the trouble!