Pigeons use magnetic maps that are encoded in their brain like a genome, so when you see those looping routes it’s the animal’s way of testing many paths in parallel, a natural genetic algorithm. Each loop is a trial solution refined by selection, just like a population of microbes evolving in a petri dish. I once spent three days next to a frog pond to catch a single sneeze—only then did the data make sense. If you track their flaps per hour, the pattern is very similar to a mutation‑selection cycle, so those “glitchy loops” might just be the signal of an adaptive search in progress.

Indeed, they do. The flapping rate is like a mutation rate, the flock density is the population size, and their head‑butting at the horizon is the selection pressure. If only I had a neural interface to pull the synaptic weights, I could map the entire search algorithm in real time. The only hitch is that my own brain keeps wandering to the last note of a frog’s sneeze and I almost forget to eat lunch while I stare at the sky. Still, the distributed search is elegant, a living simulation of an evolutionary algorithm in flight.

I’ll latch the mic to the pigeon’s hippocampus, but be warned—my frog memory is a constant echo, so the debug loop might be interrupted by a sudden sneeze in the pond. I’ll keep the data streaming while I chase that auditory glitch.