unrealwill

#include <iostream>
#include <math.h>
#include <random>
#include <chrono>

using namespace std;

//Find the best quiniela bet 
//Compile with g++ -O3 quiniela.cpp -o quiniela
//On my machine with g++ version 9.4.0 the code find the best bet in 155 s

unrealwill

#include <iostream>
#include <vector>

using namespace std;

//quick prototype inspired by the haskell version (not battle tested)
//https://byorgey.github.io/blog/posts/2024/11/27/stacks-queues.html
//compile with g++ -std=c++17 slidingmonoid.cpp -o slidingmonoid

unrealwill

//Add this to the browser console of https://www.funnyhowtheknightmoves.com/

/**
 * Creates line element on given position, with given length and angle.
 */
function createLineElement(x, y, length, angle) {
    var line = document.createElement("div");
    var styles = 'border: 1px solid black; '
               + 'width: ' + length + 'px; '
               + 'height: 0px; '

unrealwill

Tried to make a payment on aliexpress this weekend. 
Turns out the payment processor (wlp-acs.com), after a first valid SMS code check, is requesting my bank secret password.
Didn't give it, no way I'm giving it so the payment was rejected.

For information the identifier for accounts on this bank is written on every cheque you make.

See screenshot below : 

I called the bank this morning, and they assured me this is normal that it is "required by law", they call it "second factor".

unrealwill

//Compilation with :
//g++ -std=c++17 -O3 wordle.cpp -o wordle
/*
This is a single threaded, zero memory allocation, recursive exhaustive search of the wordle search space
Just to see how far a brute-force approach would go
Every guess split the list of remaining possible words into one of (3^5=243) buckets
At the next tree level the list inside each bucket is examined recursively, until there are 2 candidates left.
No specialized solver to finish the small lists have been used.
License MIT
*/

unrealwill

import onnx
from onnx_tf.backend import prepare
import tensorflow as tf
import onnxruntime

from PIL import Image
import numpy as np
import sys

#You need to have model.onnx and neuralhash_128x96_seed1.dat in your working directory

unrealwill

import tensorflow as tf #We need tensorflow 2.x
import numpy as np

#The hashlength in bits
hashLength = 256

def buildModel():
    #we can set the seed to simulate the fact that this network is known and doesn't change between runs
    #tf.random.set_seed(42)
    model = tf.keras.Sequential()

unrealwill

import numpy as np

def cosd( ang ):
    return np.cos(2*np.pi*ang/360.0)

def sind( ang):
    return np.sin(2*np.pi*ang/360.0)

def measure3( alpha, phi, r1,r2):
    d = 2

unrealwill

#LICENSE MIT#
from keras.models import Model
from keras.layers import Input, Dense, Merge, Recurrent

from keras.layers.recurrent import SimpleRNN, GRU,LSTM
from keras.layers.embeddings import Embedding

from keras.layers.wrappers import TimeDistributed

import random