TheBojda

$(document).ready(function() {

  $("#blockchain button.set").click(function() {
    var value = parseInt($("#blockchain input.text").val(), 10);
    SimpleStorage.set(value);
    addToLog("#blockchain", "SimpleStorage.set(" + value + ")");
  });

  $("#blockchain button.get").click(function() {
    SimpleStorage.get().then(function(value) {

TheBojda

<template>
  <div>
    <p>Your peerId: {{ myPeerId }}</p>
    <p>
      Other peerId:
      <input type="text" style="width: 420px" v-model="otherPeerId" /><button
        @click="findOtherPeer"
      >
        Find
      </button>

TheBojda

<template>
  <main role="main" class="container">
    <div class="card">
      <div class="card-body">
        <h5 class="card-title">Todo example</h5>
        <p class="card-text">Simple TODO app using Bootstrap and Vue.</p>
      </div>
      <ul class="list-group list-group-flush">
        <li class="list-group-item" v-for="(todo, idx) in todos" :key="idx">
          {{ todo }}

TheBojda

# Docker compose file for a simple WordPress with MySQL
# source: https://docs.docker.com/samples/wordpress/

version: "3.3"
    
services:
  db:
    image: mysql:5.7
    volumes:
      - db_data:/var/lib/mysql

TheBojda

import React, { useState, useEffect } from 'react';
import { StyleSheet, Text, View, Platform, Keyboard, Button, FlatList, TextInput } from 'react-native';

const isAndroid = Platform.OS == "android";

export default function App() {

  const [text, setText] = useState('');
  const [tasks, setTasks] = useState([{ key: '0', text: "First task" }]);
  const [viewPadding, setViewPadding] = useState(0);

TheBojda

// SPDX-License-Identifier: Apache-2.0

pragma solidity ^0.8.4;

contract NFTToken {
    event Mint(address indexed _to, uint256 indexed _tokenId, bytes32 _ipfsHash);
    event Transfer(address indexed _from, address indexed _to, uint256 indexed _tokenId);

    uint256 tokenCounter = 1;
    mapping(uint256 => address) internal idToOwner;

TheBojda

# Linear regression using GradientTape
# based on https://sanjayasubedi.com.np/deeplearning/tensorflow-2-linear-regression-from-scratch/

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

class Model:
    def __init__(self):
        self.W = tf.Variable(16.0)

TheBojda

import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
import torch.optim as optim

transform = transforms.Compose([

TheBojda

import torch
import numpy as np
import matplotlib.pyplot as plt

TRUE_W = 3.0
TRUE_b = 0.5

NUM_EXAMPLES = 1000

x = torch.empty(NUM_EXAMPLES).normal_(mean=0,std=1.0)

TheBojda

<template>
  <nb-container>
    <nb-header></nb-header>
    <nb-grid>
      <nb-row :style="{ height: 60 }" v-for="(todo, idx) in todos" :key="idx">
        <nb-col :size="85" :style="{ justifyContent: 'center' }">
          <text :style="{marginLeft: 10}">{{todo}}</text>
        </nb-col>
        <nb-col :size="15" :style="{ justifyContent: 'center'  }">
          <nb-button iconCenter transparent :on-press="() => removeItem(idx)">