Paul Follows these loops intersect with the viewer as a spiral because even though this may repeat on the machine, the viewers time is progressing forward — as is the machines which will eventually run down. but because the mental state of the viewer is in a different space than it was 6.4 seconds ago, it only seems to be the same loop.

Paul Follows
these loops … [read full article]


I like watching computers figuring things out. For example, the Travelling Salesman Problem (TSP) has fascinated me since I was a teenager and first heard about it. At the time, massively parallel computing was going to be the solution. But they fell apart (late 1980s) and gave way to connectionism / neural networking – many of which can’t be parallelized (some can, “deep learning”), which then transformed into genetic algorithms, many of which can be parallelized. [in short] “Genetic algorithms usually perform well on discrete data, whereas neural networks usually perform efficiently on continuous data.” So, I wanted to find a visual travelling salesman solver that _could_ run in parallel which means a genetic algorithm and I found one here: – and deep in the src data is a compiled Windows version under debug, which I’m running here using 125 cities I made by doing a loose spiral that bounces off the walls. The music is copyright-free from Scott Buckley, who I just discovered when looking for a 6 minute piece to fit the length of my run here, and has a copyright-free music channel on Youtube, this piece is called Signal to Noise and captures the feeling.

I like watching computers
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the notion of objects was always that of a transient, collective quality, alike-and-dissimilar-things momentarily cooperating in a grand set of dances by affinity — a deeper affinity which includes a notion of momentary repulsion for the sake of the dance – and then, without prior notice or with, disperses

You’re good in this … [read full article]