oneryalcin

#!/bin/bash
# Crawl and download Claude platform docs as markdown

set -e

BASE_URL="https://platform.claude.com/docs/en"
SITEMAP_URL="https://platform.claude.com/sitemap.xml"
WORK_DIR="/tmp/claude-docs"
OUT_DIR="$WORK_DIR/docs"
URLS_FILE="$WORK_DIR/urls.txt"

oneryalcin

"""
SDK Patch with Subagent Support - Adds .raw field to SDK message types with maximum fidelity.

Provides drop-in replacements:
- ClaudeSDKClientWithRaw: replaces ClaudeSDKClient
- query_with_raw: replaces query

Data sources for .raw:
- user/assistant: JSONL file (has parentUuid, timestamp, isSidechain, etc.)
- result: Raw CLI output (has modelUsage, errors, permission_denials)

oneryalcin

Luxical: The Engineering Deep Dive

"Transformers without the Heavy Lifting"

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.

---

Table of Contents

1. The Problem: The Efficiency Gap

oneryalcin

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.

oneryalcin

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):

oneryalcin

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:

oneryalcin

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

oneryalcin

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:

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

oneryalcin

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

oneryalcin

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).