Here is why. Princeton’s PEAR lab ran RNG work for about 28 years and shut down in February 2007. People in the lab tried to shift random event generator output, and they reported small deviations after tens of millions of events. https://www.pear-lab.com/

The Global Consciousness Project took a similar idea outside the lab. It has run a distributed network of hardware RNGs since 1998 and looks for correlated deviations around major world events.

Most people looking at hardware entropy want true randomness for crypto. I want to treat entropy like a sensor. I want to see what might perturb the underlying noise, not just consume a final stream.

So I built OpenEntropy. It samples 47 physical-ish sources on Apple Silicon, like clock jitter, thermal beats, DRAM timing conflicts, cache contention, and speculation timing. Raw mode gives you unprocessed, per-source bytes so you can run your own stats on each channel.

The PEAR-style question is: does output shift when “intention” is the experimental condition? With 47 sources, I can run intention vs control sessions and ask if multiple unrelated channels drift the same way at the same time. If thermal and DRAM timing both shift during intention blocks, that’s the kind of pattern I want to measure.