Checksum

John was good at math. Math was better than people, elegant and infinitely repeatable, with a sly humor that made him laugh over paradoxes and non-Euclidean geometry proofs. This meant that John was good with computers, too, since a computer was just bitwise arithmetic compounded on itself. John was good at arcade games and detecting mixed whitespace on a page. He was good at mowing the lawn, even though he found it boring after seven summers, and chopping apples into regular polyhedrons for his mom’s applesauce.

John was not good at people, the same way his body was not good with dairy. He liked people, less than ice cream but at least as well as string cheese, but that did not intersect with his ability to know what to do with them. Take, for example, the eyes. They were always moving, even during conversation. Down! Up! Check to make sure the hand was still there! Check to make sure the person was still there! Left! Make sure the ground hadn’t split open! Up again!

John knew about evolution, these savannah prey tactics, but found the body’s delayed response frustrating. Didn’t it know that there were no tigers in this suburban neighborhood? Also, it was an inefficient data-gathering strategy. Far more effective to keep the focus steady, or do a visual grid search across the person to make sure all details were captured, but it turned out that not everyone agreed with his superior technique.

Corroborating example: speech. There was no deception in binary, which meant that computers couldn’t lie. Even if there was distortion in the electrical signal, most communication protocols had a self-correcting process that used row and column sums to validate every packet of data. People did not have this. They said yes which meant no, and no which meant please ask me again. Later meant never, hate meant not-hate, love meant not-love, except sometimes when it did.

One clap, two clap, three clap, forty?

By clapping more or less, you can signal to us which stories really stand out.