Balaji V (xbalajipge) xbalajipge

Azure ASP vs ASE

1. What is Azure ASP vs ASE?

ASP (App Service Plan)

An App Service Plan defines the compute resources for hosting your web apps, APIs, and functions in Azure App Service.
It determines region, VM size, scaling options, and pricing tier.
Multiple apps can share the same ASP.

xbalajipge: Describe the design of the terraform code in tfe/modules/{projects,teams,workspaces} folder; give a high level over view as bullet points, suitable for presentationn

Workspace: Collecting workspace information# High-Level Design Overview: TFE Modules

response

Based on the Terraform code in tfe/modules/{projects,teams,workspaces}, here's a presentation-ready overview:

prompt 1

what is the purpose of the terraform code in `terraformv2` folder; give a high level over view as bullet points

response 1

Collecting workspace informationThis Terraform configuration in the terraformv2 folder is designed to manage Terraform Cloud (TFC) resources programmatically. Its primary purpose is to automate the creation and management of TFC workspaces and their associated team permissions.

Here is a high-level overview:

The Architecture of Modern AI Systems: Introducing the Model Context Protocol (MCP)

1. Foundations of AI and the Large Language Model

1.1 Defining the Core Components of Modern AI

Artificial Intelligence (AI) is the broad field of computer science dedicated to building machines that can perform tasks normally requiring human intelligence. Within this landscape, Generative AI and Large Language Models (LLMs) represent a significant leap forward.

A Large Language Model (LLM), such as Claude, GPT-4, or Llama, is a type of generative model trained on vast amounts of text data. It excels at reasoning, understanding context, generating creative text, summarizing, and translating.

Write mermaid code for architecture and a ladder chart showing AI communication between:
- User
- AI Client
- LLM
- MCP Server
- MCP Registry
- Multiple MCP Agents

Include:

 python apitest.py -m "Where is PG&E" -u "https://aws-ai-gateway.nonprod.pge.com/v1"  -M "@bedrock-aigatew-02d091/us.anthropic.claude-sonnet-4-20250514-v1:0" -s "You are a helpful assistant"

python apitest.py -m "Where is PG&E" -u "https://aws-ai-gateway.nonprod.pge.com/v1"  -M "@bedrock-aigatew-02d091/us.anthropic.claude-3-7-so
nnet-20250219-v1:0"

	#! /usr/bin/env python3

	import json
	import argparse
	from pathlib import Path

	def extract_chat_to_markdown(json_file_path: str, output_file_path: str):
	"""
	Extract chat prompts and responses from a Copilot chat JSON file and save as markdown.

	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>GitHub Repositories</title>
	<style>
	/* Generated by Copilot */
	* {
	margin: 0;

	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>PagerDuty Schedule Calendar</title>
	<link rel="preconnect" href="https://fonts.googleapis.com">
	<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
	<link href="https://fonts.googleapis.com/css2?family=Roboto:wght@400;500;700&display=swap" rel="stylesheet">
	<style>

	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>Universal Stock Dashboard</title>
	<style>
	/* Generated by Copilot */
	* {
	margin: 0;