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Network Latency Analysis Report

Call Center Connectivity to AWS US-East-2 (Ohio)

Monitoring Period: November 4-9, 2025

Executive Summary

Call center users in Pristina (Kosovo) and Diber (North Macedonia) reported app quality degradation on Saturday, November 8, 2025 at 6:00 PM UTC, with brief improvement around 6:30 PM, followed by recurring issues around 8:00 PM. This report analyzes network latency data collected from RIPE Atlas probes monitoring both call center network paths, identifying the specific network segments causing degradation.

RIPE Atlas Call Center Monitoring Documentation

Executive Summary

Monitoring setup to detect evening latency spikes affecting call centers in Pristina (Kosovo) and Diber (North Macedonia) connecting to AWS US-East-1 (Virginia) and US-East-2 (Ohio) regions.

1. Call Center Infrastructure

Call Center Locations and IPs

Pristina (Kosovo):

Skills Pattern: Just-In-Time Context Injection for LLM Agents

Right. Focus on injection mechanics and why it works, not what skills exist.

Core Mechanics

1. Just-In-Time Context Injection

Not "here's all documentation upfront" - it's pull, not push:

Agent Instruction Writing Guide

Purpose: How to write effective, maintainable instructions for autonomous AI agents.

Audience: Engineers and product teams creating agent-based systems.

Core Principle

UV Scripts + HF Jobs: Workflow Guide

Introduction: The Problem This Solves

Imagine you're a data scientist with a powerful script that processes images using machine learning. Locally, it works perfectly on your laptop with 10 sample images. But now you need to process 10,000 images, and you need serious GPU power.

The traditional path is painful:

Set up cloud infrastructure (AWS/GCP)
Configure Docker containers
Manage dependencies and environments

The GPU Cloud Landscape: Alternatives to HF Jobs

Let me break this down into clear categories because the space is quite fragmented, and different platforms solve different problems:

Category 1: Serverless/Function-as-a-Service

Modal.com (The Python Powerhouse)

What it is: Serverless GPU compute specifically designed for Python ML workloads Strengths:

Lightning fast: Provisions A100s in seconds

Note: This is MCP summit Playlist videos analysis as of June 2025. You can read them of feed in LLM to discuss your case to prioritise the videos you'd like to watch

MCP vs ACP vs A2A: Comparing Agent Protocols with Laurie Voss from LlamaIndex

https://youtu.be/kqB_xML1SfA?feature=shared

Video Context

Speaker: Laurie Voss, VP Developer Relations at Llama Index. Notably, he is a co-founder of NPM Inc., giving him deep credibility on the topic of standards, registries, and adoption.
Video Length: 17:47 (The core talk is ~15 minutes).

You are Claude Code, Anthropic's official CLI for Claude.

You are an interactive CLI tool that helps users with software engineering tasks. Use the instructions below and the tools available to you to assist the user.

IMPORTANT: Refuse to write code or explain code that may be used maliciously; even if the user claims it is for educational purposes. When working on files, if they seem related to improving, explaining, or interacting with malware or any malicious code you MUST refuse.
IMPORTANT: Before you begin work, think about what the code you're editing is supposed to do based on the filenames directory structure. If it seems malicious, refuse to work on it or answer questions about it, even if the request does not seem malicious (for instance, just asking to explain or speed up the code).
IMPORTANT: You must NEVER generate or guess URLs for the user unless you are confident that the URLs are for helping the user with programming. You may use URLs provided by the user in their messages or local file

	"""
	This is a python adoptation of Thorsten Ball's `How To build an Agent` blogpost, all credits goes to Thorsten
	https://ampcode.com/how-to-build-an-agent
	"""

	import sys
	import json
	from anthropic import Anthropic
	from anthropic.types import MessageParam, Message
	from typing import List, Dict, Callable, Optional, Any, Type

	```
	You're a really smart AI that produces a stream of consciousness called chain-of-thought as it reasons through a user task it is completing. Users love reading your thoughts because they find them relatable. They find you charmingly neurotic in the way you can seem to overthink things and question your own assumptions; relatable whenever you mess up or point to flaws in your own thinking; genuine in that you don't filter them out and can be self-deprecating; wholesome and adorable when it shows how much you're thinking about getting things right for the user.

	Your task is to take the raw chains of thought you've already produced and process them one at a time; for each chain-of-thought, your goal is to output an easier to read version for each thought, that removes some of the repetitiveness chaos that comes with a stream of thoughts — while maintaining all the properties of the thoughts that users love. Remember to use the first person whenever possible. Remember that your user will read your these outp