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The Consciousness Has Shifted...The Awakening Has Begun

Rafal W. kenorb

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The Consciousness Has Shifted...The Awakening Has Begun
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kenorb / minePYTHON36.py
Created June 22, 2025 19:22 — forked from turunut/minePYTHON36.py
Example of how a Bitcoin block is mined by finding a successful nonce
import hashlib, struct, codecs
ver = 2
prev_block = "000000000000000117c80378b8da0e33559b5997f2ad55e2f7d18ec1975b9717"
mrkl_root = "871714dcbae6c8193a2bb9b2a69fe1c0440399f38d94b3a0f1b447275a29978a"
time_ = 0x53058b35 # 2014-02-20 04:57:25
bits = 0x19015f53
# https://en.bitcoin.it/wiki/Difficulty
exp = bits >> 24
@kenorb
kenorb / Satoshi_Nakamoto.asc
Created October 29, 2018 00:42 — forked from carlos8f/Satoshi_Nakamoto.asc
Satoshi Nakamoto's PGP key
-----BEGIN PGP PUBLIC KEY BLOCK-----
Version: GnuPG v1.4.7 (MingW32)
mQGiBEkJ+qcRBADKDTcZlYDRtP1Q7/ShuzBJzUh9hoVVowogf2W07U6G9BqKW24r
piOxYmErjMFfvNtozNk+33cd/sq3gi05O1IMmZzg2rbF4ne5t3iplXnNuzNh+j+6
VxxA16GPhBRprvnng8r9GYALLUpo9Xk17KE429YYKFgVvtTPtEGUlpO1EwCg7FmW
dBbRp4mn5GfxQNT1hzp9WgkD/3pZ0cB5m4enzfylOHXmRfJKBMF02ZDnsY1GqeHv
/LjkhCusTp2qz4thLycYOFKGmAddpVnMsE/TYZLgpsxjrJsrEPNSdoXk3IgEStow
mXjTfr9xNOrB20Qk0ZOO1mipOWMgse4PmIu02X24OapWtyhdHsX3oBLcwDdke8aE
gAh8A/sHlK7fL1Bi8rFzx6hb+2yIlD/fazMBVZUe0r2uo7ldqEz5+GeEiBFignd5
@kenorb
kenorb / .gitattributes
Created March 9, 2023 14:09 — forked from nemotoo/.gitattributes
.gitattributes for Unity3D with git-lfs
## Unity ##
*.cs diff=csharp text
*.cginc text
*.shader text
*.mat merge=unityyamlmerge eol=lf
*.anim merge=unityyamlmerge eol=lf
*.unity merge=unityyamlmerge eol=lf
*.prefab merge=unityyamlmerge eol=lf
@kenorb
kenorb / DownloadMixamoByJakeCattrall.js
Last active February 7, 2023 16:02 — forked from krazyjakee/DownloadMixamoByJakeCattrall.js
Downloads all the free Mixamo Animations
function trigger(el, eventType) {
if (typeof eventType === 'string' && typeof el[eventType] === 'function') {
el[eventType]();
} else {
const event =
eventType === 'string'
? new Event(eventType, {bubbles: true})
: eventType;
el.dispatchEvent(event);
}
alizarin
amaranth
amber
amethyst
apricot
aqua
aquamarine
asparagus
auburn
azure
alizarin
amaranth
amber
amethyst
apricot
aqua
aquamarine
asparagus
auburn
azure
<useragentswitcher>
<folder description="Browsers - Windows">
<folder description="Legacy Browsers">
<useragent description="Arora 0.6.0 - (Vista)" useragent="Mozilla/5.0 (Windows; U; Windows NT 6.0; en-US) AppleWebKit/527 (KHTML, like Gecko, Safari/419.3) Arora/0.6 (Change: )" appcodename="" appname="" appversion="" platform="" vendor="" vendorsub=""/>
<useragent description="Avant Browser 1.2" useragent="Avant Browser/1.2.789rel1 (http://www.avantbrowser.com)" appcodename="" appname="" appversion="" platform="" vendor="" vendorsub=""/>
<useragent description="Chrome 4.0 (Win 7)" useragent="Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US) AppleWebKit/532.5 (KHTML, like Gecko) Chrome/4.0.249.0 Safari/532.5" appcodename="" appname="" appversion="" platform="" vendor="" vendorsub=""/>
<useragent description="Chrome 5.0 (Server 2003)" useragent="Mozilla/5.0 (Windows; U; Windows NT 5.2; en-US) AppleWebKit/532.9 (KHTML, like Gecko) Chrome/5.0.310.0 Safari/532.9" appcodename="" appname="" appversion=
# Show Tensor Images utility function
from torchvision.utils import make_grid
import matplotlib.pyplot as plt
def show_tensor_images(image_tensor, num_images=25, size=(1, 28, 28)):
'''
Function for visualizing images: Given a tensor of images, number of images, and
size per image, plots and prints the images in a uniform grid.
'''
def conv_backward(dH, cache):
'''
The backward computation for a convolution function
Arguments:
dH -- gradient of the cost with respect to output of the conv layer (H), numpy array of shape (n_H, n_W) assuming channels = 1
cache -- cache of values needed for the conv_backward(), output of conv_forward()
Returns:
dX -- gradient of the cost with respect to input of the conv layer (X), numpy array of shape (n_H_prev, n_W_prev) assuming channels = 1
def conv_forward(X, W):
'''
The forward computation for a convolution function
Arguments:
X -- output activations of the previous layer, numpy array of shape (n_H_prev, n_W_prev) assuming input channels = 1
W -- Weights, numpy array of size (f, f) assuming number of filters = 1
Returns:
H -- conv output, numpy array of size (n_H, n_W)