Authorship Note: This document was compiled during an interactive exploration session simulating a "Feynman Lab" environment. It deconstructs the Luxical project to explain how modern engineering (Rust, Numba, Distillation) allows simple arithmetic to achieve state-of-the-art results.

1. The Problem: The Efficiency Gap

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.

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.

Pristina (Kosovo):

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

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

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

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

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:

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

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

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

• Lightning fast: Provisions A100s in seconds