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LiteLLM

LiteLLM explores lightweight large-language-model approaches focused on efficiency, deployability, and fast iteration—ideal for on-device inference, constrained environments, and rapid prototyping.

Model Context Protocol

Model Context Protocol - Notes, Best Practices, Design Patterns.

Anomaly Detection

Comprehensive Guide to mastering Anomaly Detection in Machine Learning

Roadmap - GenAI & Agentic AI Engineering

Comprehensive roadmap to mastering GenAI and Agentic AI engineering, from foundations to production-ready systems for real-world applications.

Testing LLM Outputs: Evals for Models, Agents, and Skills

LLM outputs are non-deterministic, which breaks the assumptions most of our testing tooling is built on. This document covers how to actually test model outputs, agent trajectories, and Claude Code-style skills — the layers of evaluation, the current 2026 tool landscape, and a working CI harness you can copy.

Reverse Engineering Principles

A practitioner’s guide to reverse engineering existing systems — the principles, the techniques that prove accuracy, and how AI tools are changing the game.

Claude Notes

Claude is a family of large language models developed by Anthropic, designed to be helpful, harmless, and honest. This document provides an overview of Claude’s capabilities, architecture, and applications — with all examples using LangChain (Python).

Microservice Design Patterns

Comprehensive reference on all 44+ microservice design patterns catalogued by Chris Richardson plus patterns from Microsoft.