n8programs N8python

Working on something different than what I was working on at the time I wrote this bio. All my work is CC0 unless otherwise specified.

N8python / math-assist-mlx.py

Created November 1, 2024 01:07

	import os
	import time
	from pynput import keyboard
	from datetime import datetime
	import subprocess
	import threading
	import tkinter as tk
	import queue

	# ML imports

N8python / nano-mnist-replicate.py

Created October 3, 2024 05:11

Code to replicate score

	import torch
	from torchvision import datasets, transforms
	from tqdm import tqdm
	import numpy as np

	# Import the generated predict function
	from predict_function import predict

	# Load MNIST test dataset
	transform = transforms.Compose([transforms.ToTensor()])

N8python / nano-mnist.py

Created October 3, 2024 04:58

A python function that gets 94.65% accuracy on MNIST. The parameter `i` is a 784 array of 0s or 1s - computed by rounding the flattened MNIST image.

	def predict(i):
	a2=max(0,+i[221]+i[526]+i[565]+i[578]+i[592]+i[612]+i[619]+i[636]+i[637]+i[647])
	a5=max(0,-i[75]+i[98]-i[133]-i[161]-i[162]-i[163]-i[190]-i[191]-i[218]-i[219]-i[220]-i[247]-i[248]-i[276]-i[277]+i[283]+i[294]-i[304]-i[315]-i[332]-i[340]-i[342]-i[445]-i[485]-i[500]-i[528]-i[529]-i[556]-i[557]-i[584]-i[612]+i[676]-i[743]-i[744]-i[745])
	a7=max(0,-i[323]-i[324]-i[325]+i[329]-i[350]-i[352]+i[358]+i[388]+i[412]+i[453]+i[454]+i[456]+i[512])
	a28=max(0,-i[322]-i[348]-i[349]+i[359]+i[370]+i[371]-i[375]-i[376]+i[387]+i[397]+i[398]-i[403]-i[404]+i[427]+i[428]-i[431]-i[435]+i[442]+i[455]+i[456]-i[460]-i[464]+i[484]-i[491]+i[524]+i[748])
	a39=max(0,-i[155]-i[159]-i[377]-i[405]-i[431]-i[432]-i[538]-i[636]+i[677])
	a56=max(0,+i[277]-i[484]-i[485]-i[514]-i[515]-i[516]+i[682])
	a62=max(0,+i[104]+i[122]+i[129]+i[131]+i[132]+i[164]+i[165]+i[191]+i[192]+i[221]+i[250]-i[350]-i[368]+i[416]+i[445]+i[473]+i[528]+i[570]+i[572]-i[649]-i[677]-i[678]-i[679]-i[680]-i[707]-i[708]-i[709]-i[710]-i[711]

N8python / voidhead.txt

Created August 24, 2024 01:32

https://0fe3-128-220-159-218.ngrok-free.app/v1/chat/completions

N8python / consensusllm.js

Created July 26, 2024 03:43

N8python / simplesearch.js

Last active July 10, 2024 20:20

It's pretty simple to make a half-decent search agent.

	import googleIt from 'google-it';
	import axios from 'axios';
	import cheerio from 'cheerio';
	import OpenAI from 'openai';
	import readlineSync from 'readline-sync';

	const openai = new OpenAI({
	baseURL: "http://localhost:1234/v1",
	apiKey: 'My API Key'
	});

N8python / transmission.glsl

Created January 6, 2023 15:22

Chromatic aberration/refraction shader

	vec3 transmission = vec3(0.0);
	float transmissionR, transmissionB, transmissionG;
	float randomCoords = rand();
	float thickness_smear = thickness * max(pow(roughness, 0.33), anisotropy);
	vec3 distortionNormal = vec3(0.0);
	vec3 temporalOffset = vec3(time, -time, -time) * temporalDistortion;
	if (distortion > 0.0) {
	distortionNormal = distortion * vec3(snoiseFractal(vec3((pos * distortionScale + temporalOffset))), snoiseFractal(vec3(pos.zxy * distortionScale - temporalOffset)), snoiseFractal(vec3(pos.yxz * distortionScale + temporalOffset)));
	}
	for (float i = 0.0; i < ${samples}.0; i ++) {

N8python / complexNeuron.js

Created November 30, 2020 02:07

This neuron has a 2-layer polynomial activation function, combined with a sigmoid at the end.

N8python / xorModel.js

Last active November 30, 2020 00:45

6 parameter model. Learns xor blazing fast.

N8python / decoderMnist.js

Created October 29, 2020 17:03

	(async() => {
	const model = await tf.loadLayersModel('file://./encoder-model/model.json');
	const input = tf.input({ shape: [2] });
	const dense1 = model.layers[4].apply(input);
	const dense2 = model.layers[5].apply(dense1);
	const dense3 = model.layers[6].apply(dense2);
	const dense4 = model.layers[7].apply(dense3);
	const decoder = tf.model({ inputs: input, outputs: dense4 });
	await decoder.save(`file://./decoder-model`);
	mnistToImage(decoder.predict(tf.tensor([