i am still thinking about nested noise conversation with claude and still uncertain if i fully agree with its well-cited approach at scale=0, constant signal at scale=1, midpoint, not complicated now consider scale=2, 4x4 grid what subdivision does, is it takes the scale=1 and scale=0 stuff, averages it, and nudges it now with frequency-space fourier stuff, it adds a higher frequency component the difference at scale=2 is that subdivision creates independent random numbers to nudge from, whereas a scale=2 sinusoid has equal-sized peaks aligned with both spots. the difference in heights instead could come from the phase at scale=1. that approach is kind of a hybrid of the frequency-space and subdivision/time-space interpretations -- here we're consider the time space skews to be managed by the lower frequencies, kind of intentionally rather than considering covariance. it's very normal to think of frequency space that way, i thought hummmm then scale=3 .... more complicated ... (same deal though, you could think of its dependence on scale=2 and scale=1 quirks _rather than codependence between distant points_) if you think of it that way, it seems much more natural to consider nesting data at different scales. the interpretations intermix much better.