Simon J.K. Pedersen sjkp

Download the source files from the SQLite website: https://www.sqlite.org/download.html Get the sqlite-amalgamation ZIP and the sqlite-src ZIP, you will need both.

Extract the sqlite-src ZIP to a temporary folder. Open the sqlite-amalgamation ZIP and copy the sqlite3.h, sqlite3.c and sqlite3ext.h files to clipboard. Open to where you extracted the sqlite-src ZIP with Windows Explorer then go into the ext/misc folder. Paste the 3 files into that folder.

Next, open a visual studio developer x64 prompt and change to the sqlite-src/ext/misc folder. Enter the following command to compile the extension: cl /I . /LD spellfix.c /link /OUT:spellfix.dll

Import the spellfix.dll into your SqliteStudio using the Extension Manager.

How to use SQL server on M1 Mac

Prerequisites

Docker & docker-compose
Brew

Setup Colima VM

brew install colima

	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;

	# 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

	{
	"$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",

	<!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;

	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 lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>Modern Contact Form</title>
	<script src="https://challenges.cloudflare.com/turnstile/v0/api.js" defer></script>
	<style>
	body {
	font-family: Arial, sans-serif;