An MCP Multimodal AI Agent with eyes and ears!

💻 vibe coding 101｜The first course for AI-native product builders.

A comprehensive set of samples of creating and using MCP servers and clients with .NET

Build the system that prompts your agents. A teaching repo for loop engineering: chapters, an annotated reading list, copy-paste prompts, a runnable example, and a portable agent skill

This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable, and secure AI workflows from session setup to service orchestration.

Master OpenCode, the open-source AI coding agent — setup, agents, skills, plugins, MCP, Zen & headless CI.

Example implementation of MCP Streamable HTTP client/server in Python and TypeScript.

Find · Leverage · Optimize · Win — an evidence-led SEO playbook for the AI-search era. 72 docs · 42 AI prompts · 27 diagrams · 15 sourced 2026 stats. CC BY 4.0.

DIO PRO Vitalício Week: Agentes de IA

Learn it. Build it. Ship it for others.

Learn what AI skills are and how to design, structure, and use them in real-world agent systems.

The definitive OpenAI, Claude, MCP, Harness, Evals, and Production Agent Systems learning roadmap.

Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) Design Pattern and Agent-Native Cloud Technologies: OpenAI Agents SDK, Memory, MCP, A2A, Knowledge Graphs, Dapr, Rancher Desktop, and Kubernetes.

This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, AutoGen, CrewAI, RAG, MCP, automation with n8n, and scalable agent deployment using Docker, AWS, and BentoML.

A practical governance framework for organizations adopting the Model Context Protocol (MCP), the open standard that lets AI agents connect to external tools, data sources, and systems.

One-stop handbook for building, deploying, and understanding LLM agents with 60+ skeletons, tutorials, ecosystem guides, and evaluation tools.

End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.

Quarkus LangChain4J Workshop that demonstrates both single AI service capabilities and Agentic AI orchestration

🏰 An exciting game of programming and Artificial Intelligence

📚 Two books on harness engineering — the design philosophies behind Claude Code & Codex: constraints, query loops, context governance, multi-agent verification. harness-books.agentway.dev

Build a Claude-Code-shaped agent harness from scratch. 7-week course, 20 chapters, ~5,000 lines of Python, 42 tests, 3 LLM providers, no frameworks.

Vlad's Playbook — a 48-chapter operator field manual where every artifact is live, clickable, and forwardable. 31 interactive widgets, a self-updating AI radar, hand-verified model leaderboards, embedded case studies — and the repo runs the agent workflow the book teaches.

Bilingual (中文+EN) ML / LLM / diffusion / agent interview cheat sheets for AI 秋招 — generated by ARIS /interview-cheatsheet, rendered by /render-html into single-file HTML, reads anywhere — plus a CV→DBLP-fact-checked academic homepage generator and hand-authored long-form blogs 🌱

Hands On Workshop for GitHub Agentic Workflows