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Let it snow() The code could definitely be golfed… function snow(io = stdout) h, w = displaysize(io) iob = IOBuffer() ioc = IOContext(IOContext(iob, io), :displaysize=>(h,w)) print(io, repeat("\n", h), "\e[", h, "A\e[1G") # new lines and move back up air = ones(Int, w, h) flakes = [" ", "*", "❄︎", "❅", "❆"] scsin(t) = ((sin(t) / 2) + 0.5) * (0.1 / 3) likelihood(t) = scsin(t) + scsin(t * 1.00001) +
For those of you who aren’t aware, the Mojo SDK was recently released, so I thought I would take the opportunity to start benchmarking some Julia code against Mojo. As a first test, I am calculating the Mandelbrot set using the code provided by Modular. This is my Julia implementation: using Plots const xn = 960 const yn = 960 const xmin = -2.0 const xmax = 0.6 const ymin = -1.5 const ymax = 1.5 c
The JuliaSymbolics Organization Roadmap We need new Computer Algebra Systems (CAS) for this new era of computing. We need a CAS that dispatches in the multiple ways we think. We need a CAS that scales exponentially like our problems. We need a CAS that integrates with our package ecosystem, letting people extend parts and contribute back to the core library all in one language. We need a modern CA
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