Simon J.K. Pedersen sjkp

## gist:ad6fbf0709dca7bb69e02f5aab5e0199
New Microsoft Azure reference architectures and implementation guidance are now generally available for customers to confidently design and deploy intelligent apps. Customers can easily leverage patterns and practices to create private chatbots that are reliable, cost-efficient and compliant — adhering to both the functional and nonfunctional requirements of an organization.

The new guidance helps customers adopt well-architected best practices and includes:

A reference architecture and reference implementation for Microsoft Azure OpenAI Service based on Azure landing zones, which helps jumpstart and scale app deployment.
Service guides for machine learning that gives precise configuration instructions for Azure services used to deliver intelligent apps.
Patterns for designing and developing a RAG solution: While the architecture is straightforward, designing, experimenting with, and evaluating RAG solutions that fit into this architecture involves many complex considerations that benefit from a rigorous, s

## index.html
  <!DOCTYPE html>
<html>
<body>
<canvas id="mandelbrotCanvas" width="800" height="800"></canvas>
<script>
    const canvas = document.getElementById('mandelbrotCanvas');
    const ctx = canvas.getContext('2d');
    const imgData = ctx.createImageData(canvas.width, canvas.height);

    const MAX_ITERATION = 1000;

## both.json
{
    "$schema": "https://schema.management.azure.com/schemas/2019-08-01/deploymentTemplate.json#",
    "contentVersion": "1.0.0.0",
    "parameters": {
        "location": {
            "defaultValue": "canadacentral",
            "type": "String"
        },
        "environment_name": {
            "defaultValue": "containerapps-env",

## ffmpeg-common.sh
# Decode a video to yuv420 raw format
ffmpeg -vsync 0 -c:v h264_cuvid -i input.mp4 -c:v rawvideo -pix_fmt yuv420p -f null /dev/null
ffmpeg -vsync 0 -c:v h264_cuvid -i input.mp4 -c:v rawvideo -pix_fmt yuv420p output.yuv

# Resize videos to 640x480
ffmpeg -c:v h264_cuvid -i input.mp4 -vf scale=640:480 -c:v h264_nvenc output.mp4

# Downsampling frame rate to 30fps
ffmpeg -i input.mp4 -r 30 -c:v h264_nvenc output.mp4

## LuceneNetExample.cs
using System;
using System.Globalization;
using Lucene.Net.Analysis.Standard;
using Lucene.Net.Documents;
using Lucene.Net.Index;
using Lucene.Net.QueryParsers;
using Lucene.Net.Search;
using Lucene.Net.Store;
using Version = Lucene.Net.Util.Version;

## openapi-spec
{
    "swagger": "2.0",
    "info": {
        "version": "1.0.0.0",
        "title": "REMOVED",
        "description": "Azure Logic App."
    },
    "host": "prod-02.northeurope.logic.azure.com",
    "basePath": "/workflows/931ae63fd6b6459e88c2cf43ed366b20/triggers",
    "schemes": [

## certificate.bicep
param domainNames array
param appServicePlanName string
param location string = resourceGroup().location


resource certificates 'Microsoft.Web/certificates@2020-06-01' = [for domainName in domainNames: {
  name: domainName
  location: location
  properties: {
    canonicalName: domainName

## continents.geojson

      
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## proxies.json
{
    "proxies": {
        "ListAll": {
            "desc": [
                "This is the index of examples."
            ],
            "matchCondition": {
                "methods": [
                    "GET"
                ],

## Get-FileEncoding
function Get-FileEncoding
{
    [CmdletBinding()] Param (
     [Parameter(Mandatory = $True, ValueFromPipelineByPropertyName = $True)] [string]$Path
    )


    [byte[]]$byte = get-content -Encoding byte -ReadCount 4 -TotalCount 4 -Path $Path
	New Microsoft Azure reference architectures and implementation guidance are now generally available for customers to confidently design and deploy intelligent apps. Customers can easily leverage patterns and practices to create private chatbots that are reliable, cost-efficient and compliant — adhering to both the functional and nonfunctional requirements of an organization.

	The new guidance helps customers adopt well-architected best practices and includes:

	A reference architecture and reference implementation for Microsoft Azure OpenAI Service based on Azure landing zones, which helps jumpstart and scale app deployment.
	Service guides for machine learning that gives precise configuration instructions for Azure services used to deliver intelligent apps.
	Patterns for designing and developing a RAG solution: While the architecture is straightforward, designing, experimenting with, and evaluating RAG solutions that fit into this architecture involves many complex considerations that benefit from a rigorous, s
	<!DOCTYPE html>
	<html>
	<body>
	<canvas id="mandelbrotCanvas" width="800" height="800"></canvas>
	<script>
	const canvas = document.getElementById('mandelbrotCanvas');
	const ctx = canvas.getContext('2d');
	const imgData = ctx.createImageData(canvas.width, canvas.height);

	const MAX_ITERATION = 1000;
	{
	"$schema": "https://schema.management.azure.com/schemas/2019-08-01/deploymentTemplate.json#",
	"contentVersion": "1.0.0.0",
	"parameters": {
	"location": {
	"defaultValue": "canadacentral",
	"type": "String"
	},
	"environment_name": {
	"defaultValue": "containerapps-env",
	# Decode a video to yuv420 raw format
	ffmpeg -vsync 0 -c:v h264_cuvid -i input.mp4 -c:v rawvideo -pix_fmt yuv420p -f null /dev/null
	ffmpeg -vsync 0 -c:v h264_cuvid -i input.mp4 -c:v rawvideo -pix_fmt yuv420p output.yuv

	# Resize videos to 640x480
	ffmpeg -c:v h264_cuvid -i input.mp4 -vf scale=640:480 -c:v h264_nvenc output.mp4

	# Downsampling frame rate to 30fps
	ffmpeg -i input.mp4 -r 30 -c:v h264_nvenc output.mp4
	using System;
	using System.Globalization;
	using Lucene.Net.Analysis.Standard;
	using Lucene.Net.Documents;
	using Lucene.Net.Index;
	using Lucene.Net.QueryParsers;
	using Lucene.Net.Search;
	using Lucene.Net.Store;
	using Version = Lucene.Net.Util.Version;
	{
	"swagger": "2.0",
	"info": {
	"version": "1.0.0.0",
	"title": "REMOVED",
	"description": "Azure Logic App."
	},
	"host": "prod-02.northeurope.logic.azure.com",
	"basePath": "/workflows/931ae63fd6b6459e88c2cf43ed366b20/triggers",
	"schemes": [
	param domainNames array
	param appServicePlanName string
	param location string = resourceGroup().location


	resource certificates 'Microsoft.Web/certificates@2020-06-01' = [for domainName in domainNames: {
	name: domainName
	location: location
	properties: {
	canonicalName: domainName
	{
	"proxies": {
	"ListAll": {
	"desc": [
	"This is the index of examples."
	],
	"matchCondition": {
	"methods": [
	"GET"
	],
	function Get-FileEncoding
	{
	[CmdletBinding()] Param (
	[Parameter(Mandatory = $True, ValueFromPipelineByPropertyName = $True)] [string]$Path
	)


	[byte[]]$byte = get-content -Encoding byte -ReadCount 4 -TotalCount 4 -Path $Path