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# Inspired by https://docs.microsoft.com/en-us/azure/storage/blobs/storage-quickstart-blobs-python

import os
from azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient, __version__

# Make sure you update this per server
CONTAINER_NAME = 'mcbackups1'

try:
    print("Azure Blob storage v" + __version__ + " - Python quickstart sample")

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/**
 * Add this JavaScript early in the DOM so it will execute when the page loads.
 * You can use something like the "Simple Custom CSS and JS" plugin:
 * https://wordpress.org/plugins/custom-css-js/#
 * 
 * If you want to test it, execute the code inside the "DOMContentLoaded" 
 * function in the developer console on the login|register page. 
 */

document.addEventListener("DOMContentLoaded", function (event) {

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Method	Path	Purpose
GET	/	Home page
GET	/places	Places index page
POST	/places	Create new place
GET	/places/new	Form page for creating a new place
GET	/places/:id	Details about a particular place
PUT	/places/:id	Update a particular place
GET	/places/:id/edit	Form page for editing an existing place
DELETE	/places/:id	Delete a particular place

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const express = require('express');
const app = express();

app.get('/:color?', (req, res) => {
    let myColor = req.params.color;
    console.log('myColor is: ', myColor);

    if (typeof myColor === 'undefined') {
        // Set to default when undefined.
        myColor = 'white';

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// seeders/seed-bakers.js
const Baker = require('../models/baker');

require('dotenv').config();
const mongoose = require('mongoose');
const MONGO_URI = process.env.MONGO_URI;

mongoose
    .connect(MONGO_URI)
    .then(() => {

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// scripts/seed-languages.js

// DEPENDENCIES
const mongoose = require('mongoose');

// CONFIGURATION
require('dotenv').config();
mongoose.connect(process.env.MONGO_URI, { useNewUrlParser: true, useUnifiedTopology: true }, () => {
    console.log('connected to mongo: ', process.env.MONGO_URI);
});

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// I place this in ./cli/main.go, whereas rush.json is at the root of my repo.
// Then run from within ./cli with `$ go run main.go`
package main

import (
	"encoding/json"
	"fmt"
	"os"
	"os/exec"
	"path/filepath"

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#!/bin/bash

# Usage: ./script.sh [directory]
# If no directory is provided, it uses the current directory.

dir="${1:-.}"

# Common directories to exclude (3rd party libraries and build artifacts)
exclude_dirs=( -name node_modules -o -name Pods -o -name Carthage -o -name build -o -name .build -o -name .gradle -o -name venv -o -name .venv -o -name env )

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Based on current trends in 2025, companies of all sizes—from small startups to large enterprises—are increasingly seeking AI services to streamline operations, boost revenue, and optimize resource allocation. This demand is driven by the need for cost-effective, scalable solutions amid economic pressures and rapid technological advancements. Key focus areas include automating routine tasks to improve processes, enhancing customer interactions for better sales outcomes, and leveraging data-driven insights for smarter capital decisions like inventory management or risk assessment.

These services align well with reusable solutions built on open-source models (e.g., from Hugging Face repositories like LLaMA, Mistral, or Stable Diffusion, using frameworks such as TensorFlow, PyTorch, or scikit-learn). Such approaches allow for fine-tuning on company-specific data, deployment as APIs or microservices, and broad applicability across industries like retail, finance, healthcare, and manufacturing. Below, I outline th