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8 articles
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What is MCP (Model Context Protocol)? The Ultimate 2026 Guide
MCP is the open standard that connects AI apps and agents to tools, data, and workflows. This 2026 guide explains how it works, what servers expose, how it fits with agents, and best practices for builders and adopters. Read more → -
Types of AI Agents Explained: Reactive, Deliberative, Hybrid & Autonomous
A practical guide to the four main types of AI agents—reactive, deliberative, hybrid, and autonomous—covering how they decide, when to use each, and how they map to modern LLM-based systems. Read more → -
How to Build an MCP Server: A Step-by-Step Tutorial (Python & TypeScript)
A step-by-step tutorial for building a Model Context Protocol (MCP) server in Python and TypeScript, covering tools, resources, prompts, local testing with the MCP Inspector, and production security practices. Read more → -
AI Agent vs Chatbot: 7 Key Differences You Need to Know
Chatbots answer questions; AI agents pursue goals. This guide explains the seven differences that matter in 2026, including autonomy, planning, tool use, memory, side effects, and risk, with practical advice on choosing the right system. Read more → -
Agent Cards: How AI Agents Discover and Trust Each Other
A practical guide to Agent Cards in the A2A Protocol, explaining how agents advertise capabilities, discover peers, verify identity, establish trust, and collaborate safely. Read more → -
What Is AI Grounding? Preventing Hallucinations in Commercial AI Applications
AI grounding anchors model outputs to verified sources instead of relying only on training data. Learn how grounding works with RAG, agents, verification, and practical commercial systems. Read more → -
Does AI Really Think? Understanding Next-Token Prediction and Its Limitations
LLMs predict the next token rather than think like humans. This article explains how next-token prediction works, what it enables, where it fails, and why useful agents need tools, memory, verification, and external state. Read more → -
OpenAI Is Moving Away from Fine-Tuning. Why Most People Shouldn't Train Their Own Model
Fine-tuning is rarely the right first step. This article breaks down its hidden data, engineering, maintenance, and opportunity costs, then compares prompting, RAG, and ready-made agents for practical AI work. Read more →