unixpickle

func CreateMesh() *model3d.Mesh {
	box := model3d.NewMesh()

	stepSize := 0.05
	addQuad := func(p model3d.Coord3D, normalAxis int) {
		ax1 := model3d.Coord3D{X: stepSize}
		ax2 := model3d.Coord3D{Y: stepSize}
		if normalAxis == 0 {
			ax1 = model3d.Coord3D{Z: stepSize}
		} else if normalAxis == 1 {

unixpickle

module gists/deformation

go 1.14

require (
	github.com/unixpickle/essentials v1.1.0
	github.com/unixpickle/model3d v0.2.1
	github.com/unixpickle/polish v0.0.0-20200508163631-1844661b331d
)

unixpickle

const canvas = document.createElement('canvas');
canvas.width = 400;
canvas.height = 400;
const ctx = canvas.getContext('2d');
ctx.font = '350px serif';
ctx.textBaseline = 'middle';
ctx.textAlign = 'center';
ctx.fillText('Hi', 200, 200);
const data = ctx.getImageData(0, 0, 400, 400);

unixpickle

import math


def involute_point(a, t):
    c = math.cos(t)
    s = math.sin(t)
    d = t - a
    return c + d * s, s - d * c

unixpickle

// Command extract_tar can be used to extract a large
// ImageNet tarbal on a system that doesn't have enough
// storage for both the tarbal and the untarred data.
//
// As it extracts the tarbal, it truncates the original
// tar file so that it takes up less and less space.
//
// Based on this earlier gist for processing ImageNet tars:
// https://gist.github.com/unixpickle/7304c78032c9f433e28a87409f4d5aca
package main

unixpickle

"""
Reading data produced by an external program.
"""

import math
import random

import numpy as np
import torch

unixpickle

Overview

In this document, I will describe how I managed to get unlimited stars and coins in the Android game "Lily's Garden". In short, here are the steps:

• Use the adb backup feature to extract all of the game's data
• Extract the Android backup into a tar file
• Modify the file which stores the number of coins and stars
• Re-sign the coins/stars field using a reverse-engineered HMAC key
• Convert the tar file back to an Android backup

unixpickle

package main

import (
	"compress/gzip"
	"fmt"
	"math"
	"os"

	"github.com/unixpickle/model3d"
)

unixpickle

import math

import torch
import torch.nn.functional as F


def maml_grad(model, inputs, outputs, lr, batch=1, checkpoint=False):
    """
    Update the model gradient using MAML.
    """

unixpickle

import torch
import torch.nn.functional as F


def maml_grad(model, inputs, outputs, lr, batch=1):
    """
    Update a model's gradient using MAML.

    The gradient will point in the direction that
    improves the total loss across all inner-loop