Agents

AI-generated JIRA tickets and PRs have become word walls. Here's why your automation needs both a human summary and a machine-readable payload.

Compare GLM, Qwen, and Gemma local LLMs - benchmarks, context sizing, and when home GPU inference beats cloud token costs.

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.

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.

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

Model Context Protocol (MCP) and Agent2Agent (A2A) are rising stars in the world of AI. Let's see what they are and how they can work together.