marekyggdrasil

This repository contains my simplified AGE (Actually Good Encryption) http://age-encryption.org/v1 heavily based on pyage library.

It's purpose is to demonstrate the fact that GRIN core wallet implementation is producing incorrect HMAC signatures in its AGE-encrypted payloads.

1. slateage.py contains values implementated by the GRIN core wallet. My code correctly decrypts it only if HMAC verification is ignored. Decrypted payload contains the sender address.
2. validage.py contains official AGE test vector. My code correctly decrypts it and verifies the HMAC and matches the expected file key. Decrypted payload hash matches the expected value.

Another indicator the GRIN core wallet is producing flawed HMAC signatures is the fact that in grin++ wallet the HMAC verification also had to be commented out.

To run it, first setup

marekyggdrasil

def numberToBase(n, b):
    if n == 0:
        return [0]
    digits = []
    while n:
        digits.append(int(n % b))
        n //= b
    return digits[::-1]

marekyggdrasil

import matplotlib.pyplot as plt


def plotQubit(angles, refrel, refimag, resrel, resimag, title, x_axis, filename, url=''):
    fig, ax = plt.subplots(1, 1, constrained_layout=True)

    ax.set_title(title)
    x_axis += '\n' + url
    ax.set_xlabel(x_axis)
    ax.set_ylabel('phase')

marekyggdrasil

OSX Sierra version 10.12.6

if you are getting error like

ERROR:  Could not find a valid gem '<some package name>' (>= 0), here is why:
          Unable to download data from https://rubygems.org/ - SSL_connect retur

test your TLS v1.2 support

marekyggdrasil

from constraint import Problem

problem = Problem()

problem.addVariable('x', range(1, 10))
problem.addVariable('y', range(1, 10))
problem.addVariable('z', range(1, 10))


def constraintCheck(x, y, z):

marekyggdrasil

This is code repository for Quantum Adiabatic Optimization article.

Start by running instance.py that will build list-based definiton of the graph from its set-based definition, this will let you keep consistent indices between all the runs.

The run.py is the adiabatic optimization code.

Energy spectrum can be examined by running eigenenergies.py with single command line argument being one of HMVC, HMVC_, HMIS or HMIS_. I don't suggest running all the diagonalizations in single run, graphs are big and you are likely to get a segmentation fault error. This is why it is good to keep consistent indices and store your graph using lists and not sets.

You can plot figures using plot.py.

marekyggdrasil

#!/bin/sh
convert -background none inputs_before.png inputs_after.png +append inputs.png

marekyggdrasil

A very simple zkApp that allows user to deposit and withdraw. Run it with

node --experimental-vm-modules --experimental-wasm-modules --experimental-wasm-threads node_modules/jest/bin/jest.js -- fakemac.test.ts

marekyggdrasil

import matplotlib.pyplot as plt
import numpy as np
from qutip import basis


def _ket(n, N):
    return basis(N, n)


def _bra(n, N):

marekyggdrasil

from functools import wraps

class ForbiddenDrink:
    def __init__(self, drinks):
        self.do_not_drink_that = drinks

    def __call__(self, func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            innerself = args[0]