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CQRS, Task Based UIs, Event Sourcing agh!

Posted by gregyoung on February 16, 2010

Many people have been getting confused over what CQRS is. They look at CQRS as being an architecture; it is not. CQRS is a very simple pattern that enables many opportunities for architecture that may otherwise not exist. CQRS is not eventual consistency, it is not eventing, it is not messaging, it is not having separated models for reading and writing, nor is it using event sourcing. I want to take a few paragraphs to describe first exactly what CQRS is and then how it relates to other patterns.

CQRS Command and Query Responsibility Segregation

Starting with CQRS, CQRS is simply the creation of two objects where there was previously only one. The separation occurs based upon whether the methods are a command or a query (the same definition that is used by Meyer in Command and Query Separation, a command is any method that mutates state and a query is any method that returns a value).

Using an Access Token for the first time

Follow the instructions on Github to Create an Access Token in Github

Configure Git to use the osxkeychain

By default, git credentials are not cached so you need to tell Git if you want to avoid having to provide them each time Github requires you to authenticate. On Mac, Git comes with an “osxkeychain” mode, which caches credentials in the secure keychain that’s attached to your system account.

You can tell Git you want to store credentials in the osxkeychain by running the following:-

	#!/usr/bin/python2


	# Copyright (C) 2016 Sixten Bergman
	# License WTFPL
	#
	# This program is free software. It comes without any warranty, to the extent
	# permitted by applicable law.
	# You can redistribute it and/or modify it under the terms of the Do What The
	# Fuck You Want To Public License, Version 2, as published by Sam Hocevar. See

	<?php

	define('BOT_TOKEN', '7041512989:AAFYTQ-CI0Umr1SF2IdU2Yqzbc3McXbHu4g'); // place bot token of your bot here

	function checkTelegramAuthorization($auth_data) {
	$check_hash = $auth_data['hash'];
	unset($auth_data['hash']);
	$data_check_arr = [];
	foreach ($auth_data as $key => $value) {
	$data_check_arr[] = $key . '=' . $value;

	<!DOCTYPE html>
	<html lang="pt-br">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>Meus objetivos do ano</title>
	<link rel="stylesheet" href="style.css">
	</head>
	<body>
	<section class="conteudo-principal">

	{ config, lib, pkgs, ... }:

	with lib;

	{
	imports =
	[
	<nixpkgs/nixos/modules/installer/cd-dvd/channel.nix>
	./machine-config.nix
	];

	from flask import Flask
	from subprocess import check_output

	app = Flask(__name__)

	@app.route('/')
	def root():
	nf = '<pre>' + check_output('neofetch --stdout', shell=True).decode('utf-8') + '</pre>'
	lsblk = '<pre>' + check_output('lsblk').decode('utf-8') + '</pre>'
	return f'<h1>Server is running!</h1><br>\n{nf}\n<br>\n{lsblk}'

	#!/usr/bin/env zsh

	# Note:
	# CMake, Clang, clang-format, Ninja, git and sed are required to build
	#
	# Note that currently there is a bug (https://github.com/google/binexport/issues/117)
	# that requires applying this patch, remove when resolved
	#

	if [ -d ~/Downloads ]

	# The goal of this file is to provide a FastAPI application for handling
	# chat requests amd generation AI-powered responses using conversation chains.
	# The application uses the LangChaing library, which includes a chatOpenAI model
	# for natural language processing.

	# The `StreamingConversationChain` class is responsible for creating and storing
	# conversation memories and generating responses. It utilizes the `ChatOpenAI` model
	# and a callback handler to stream responses as they're generated.

	# The application defines a `ChatRequest` model for handling chat requests,