How Claude Code hooks create mechanical enforcement at commit time — blocking violations, injecting warnings, and coaching better habits before code ever reaches review.

How engineering leaders adopt AI coding tools—from completion to agents—without trading speed for quality.

How to architect, build, and evaluate AI agent systems using orchestration frameworks, protocols like MCP and A2A, and RAG pipelines.

Essential resources for mastering agentic AI development — core concepts and guides for GitHub Copilot, Claude Code, Cursor, and OpenAI Codex.

How treating AI tooling like a distracted junior developer resulted in production-quality software

What if your AI agents could learn from their mistakes and get better over time? Check out a sample architecture that does just that.

A blog post exploring my two-week experiment using GitHub Copilot CLI for all coding tasks as a generative AI skeptic.

Discover 16 practical tips for writing AI-ready C# code, making your .NET codebase easier for AI agents and tools to understand, maintain, and extend.

Learn how to build interactive AI evaluation reports in .NET to communicate model quality, boost transparency, and engage your team.

When building AI applications with LLMs, many teams are still "vibe testing" their way to production. This guide shows how file-based prompts and comprehensive test scenarios build reliable review analysis systems that handle real-world complexity, allowing you to build a solid set of repeatable automated regression tests.