AI AGENTS

MCP is the USB-C of AI agents. What the Model Context Protocol is, why Anthropic built it, and how to install your first server in Claude Code or Cursor. Fact-checked against the official MCP spec.

Claude Code is Anthropic's AI coding agent for your terminal. What it does, how it works, how it compares to Cursor and Codex, and how to ship your first feature with it. Fact-checked against official docs.

A practical security playbook for running Codex cloud tasks safely in 2026 using OpenAI docs: internet access controls, domain allowlists, HTTP method limits, and review workflows.

Hacker News keeps arguing about Claude Code, Codex, skills, MCP, and orchestration. Under the noise, the same four truths keep surfacing: workflows matter more than demos, verification is the bottleneck, skills beat prompts, and orchestration matters more than raw autonomy.

How to use AI agents to plan, scaffold, build, test, and deploy a SaaS product. Parallel development patterns, real workflow examples, and the operational details that determine whether your AI-assisted build succeeds or fails.

Context engineering is the practice of designing the persistent information that surrounds every AI interaction. CLAUDE.md files, system prompts, skill libraries, and memory systems. It is the single highest-leverage skill for developers working with AI agents in 2026.

Production-tested patterns for orchestrating AI agent teams - from fan-out parallelism to hierarchical delegation. Covers CrewAI, LangGraph, AutoGen, OpenAI Agents SDK, Google ADK, and custom approaches with real code.

AI agents that reflect on failures, accumulate skills, and get better with every session. Reflection patterns, memory architectures, skill extraction, and working code examples for building agents that actually learn.

Step-by-step guide to building an MCP server in TypeScript - from project setup to tool definitions, resource handling, testing, and deployment.

Deep comparison of the top AI agent frameworks - architecture, code examples, strengths, weaknesses, and when to use each one.

Agents forget everything between sessions. Here are the patterns that fix that: CLAUDE.md persistence, RAG retrieval, context compression, and conversation summarization.

AI agents fail in ways traditional debugging cannot catch. Here are the tools and patterns for finding and fixing broken agent loops, tool failures, and context issues.

A practical comparison of the five major AI agent frameworks in 2026 - architecture, code examples, and a decision matrix to help you pick the right one.

AI agent skills are not just for developers. Here is how 12 professions use packaged AI workflows to do better knowledge work.

A step-by-step guide to building AI agents that actually work. Choose a framework, define tools, wire up the loop, and ship something real.

How to spec agent tasks that run overnight and wake up to verified, reviewable code. The spec format, pipeline, and review workflow.

AI agents use LLMs to complete multi-step tasks autonomously. Here is how they work and how to build them in TypeScript.

A practical guide to building AI agents with TypeScript using the Vercel AI SDK. Tool use, multi-step reasoning, and real patterns you can ship today.

From swarms to pipelines - here are the patterns for coordinating multiple AI agents in TypeScript applications.

AI coding agents are submitting pull requests to open source repos - and some CONTRIBUTING.md files now contain prompt injections targeting them.