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.

LLMs produce non-deterministic outputs, making traditional exact-match testing ineffective. How can you verify an application response is contextually accurate when the response can vary with every request? Let's take a look at promptfoo!

A look at the recently published Aspire roadmap for 2025, focusing on its code-first DevOps evolution, polyglot aspirations, and AI orchestration, and more.

Discover how GitHub Copilot Agents can function as AI developers on your team, automating development while complementing human developers' strategic skills.