A practical breakdown of AI agents — how they plan, use tools, manage memory, and orchestrate multi-agent workflows to solve complex tasks autonomously.
A practical comparison of the leading AI coding assistants — GitHub Copilot, Cursor, and Windsurf — covering features, agent capabilities, model access, and how to choose.
A developer's guide to Azure AI Foundry — the model catalog, deployments, prompt engineering playground, agent framework, evaluation tools, and building production AI applications.
An introduction to the Model Context Protocol (MCP), how MCP servers work, and why they are a game-changer for AI-powered development workflows.
A developer's guide to Retrieval-Augmented Generation (RAG) — the architecture, chunking strategies, vector databases, and when to use RAG over fine-tuning.
A practical guide to running large language models locally on your own hardware — covering Ollama, LM Studio, hardware requirements, and when local beats cloud.
Practical architecture patterns for agent handoffs, contracts, retries, and safety checks in multi-agent systems.
A practical approach to continuously red team AI agents against injection, abuse, and data exfiltration risks.
Why context shape has become the primary determinant of quality in modern LLM products.
How teams can keep AI-assisted development fast while introducing quality gates that prevent regressions.
How AI gateways help LLM apps control cost, latency, routing, caching, and policy enforcement in production.
A practical framework for selecting the right large language model — covering use case mapping, cost vs capability tradeoffs, latency, context windows, and deployment constraints.