new KF release (highlights: NanoMB1 algorithm, Mothbrot formulas, and a bugfix to incremental drawing)

I saw a deadline extended to 30th January

> The World is a Hologram

I may have some (more) to submit....

button mashing Show more

negative, complaining Show more

Ported my to empd, not published it formally yet...

With patched to generate mipmaps, may become unnecessary with upstream changes (so I can do it on the Pd patch level instead of in the C++).

Struggled with getting pix_snap2tex working, so ported it to gemframebuffer, but that looked bad ( does antialiasing only for the main output, not FBO). Turns out it was something unrelated (bad define in my GLEW hack), and pix_snap2tex works great in the end.

WIll work on the audio today, needs more variation.

negative, complaining Show more

Updated the Gem example with a spinning [cube] with desktop OpenGL 1.x Fixed Function Pipeline lighting emulated in WebGL by Regal.

Other primitives like [sphere] and [torus] fail due to a bug with GL_QUAD_STRIP, have reported it to the Emscripten port.

[teapot] fails for other reasons (incomplete GLU? something else?), seems to be an infinite loop rather than a simple failure...

claude boosted

Scala Native processing function plugged into web audio script processor node:

(AudioWorklet still to do, have too old Firefox version)

#Scala #WebAudio added some scripts for building in a chroot, so you don't risk potential weird interactions with your main dev system victory!

working on cleaning up / committing my changes to the Gem sources now, will push as soon as I've got a reproducible build script

fighting may have got easier with this

currently requires emsdk "incoming" tag, afaict

fighting with Emscripten, Regal, GL, GLU, SDL, Gem

link to stroboscopic video Show more

Today I'm mostly working on 'nnirror', my art project about training neural networks to recognize themselves.

The ego network is trained using a generative adversarial network against the id network. Ego aims to recognize its own weights (output 1) vs everything else (output 0 for id's attempts to fool ego, output 0 for random input too).

The network weights are visualized at the top left of the first image, below is the normalized change since the previous epoch.

The second image plots the parameters (learning rates, momementa, etc), on the left if the ego network failed to achieve enlightenment after 1000 epochs, on the right if it managed to score above 4.5 in that time. The total score is twice the top graph minus the two lower graphs.

implemented common subexpression elimination using the uniplate package, not yet integrated into the formula compiler proper, but seems promising in ghci exploration...

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