They're like riding a bike: easy, and you don't get it until you try.

Build, debug, and optimize AI agents using DSPy and MLflow.

Full guide on claude tips and tricks and how you can optimise your claude code the best & strive to find every command possible even hidden ones! - zebbern/claude-code-guide

VS Code AI Customization: Learn to use custom instructions, prompt files, and custom chat modes to personalize AI code generation, reviews, and chat responses.

Working with GitHub Copilot in VS Code amps out your efficiency as a programmer - but did you know that adding a simple markdown file can boost this efficiency even more, while *also* decreasing the size of your prompt? Custom Instructions can help you and your team do so much more with GitHub Copilot, and @rconery will show you how in this video. 🔎 Chapters: 00:12 Simple, automatic instructions 02:07 Custom Git commit messages 03:26 Customizing Copilot functionality in VS Code 05:00 Going all in with markdown files as instructions 🔗 Links: Get Copilot: https://aka.ms/get-copilot Instruction Snippets for JSONC: https://gist.github.com/robconery/f93d016ace16feb7156f9b7905f3f499 🎙️ Featuring:‪ @rconery‬ #vscode #copilot #githubcopilot

✅ Learn how to build robust and scalable software architecture: https://arjan.codes/checklist. Want your AI tools to actually *do* something? In this video, I’ll show you how to integrate external tools with language models using **MCP (Model Context Protocol)**. You’ll learn two common architecture patterns, see real code examples, and get tips on keeping your setup clean and scalable. Whether you’re building for Claude, ChatGPT, or any other LLM—this is how you connect your backend to AI. 🔥 GitHub Repository: https://git.arjan.codes/2025/mcp-server. 🎓 ArjanCodes Courses: https://www.arjancodes.com/courses. 🔖 Chapters: 0:00 Intro 0:46 What is MCP? 3:14 YouTube MCP Version 1 7:58 YouTube MCP Version 2 12:18 Final Thoughts #arjancodes #softwaredesign #python

In my previous article , we explored Model Context Protocol (MCP) — a standardized way for LLMs to invoke tools or APIs with structured…

Get the full code: https://github.com/riza-io/examples/tree/main/demos/mcp_and_pydanticai Try out Riza's MCP server: https://docs.riza.io/getting-started/mcp-servers PydanticAI is one of the most popular frameworks for building AI agents in Python, and it recently launched MCP support. If you've been wanting to learn MCP, this is a great place to start. In this tutorial you'll build a simple agent with PydanticAI, and then add an MCP server called fetch, which enables web browsing. Then we'll use the Postgres MCP server to add database querying to our agent. Then we'll use Riza's remote MCP server to add a code interpreter to our agent.

Everyone is starting to realize how big of a deal Claude’s Model Context Protocol (MCP) is - it’s the first ever “standard” for connecting LLMs with services like your database, Slack, GitHub, web search, etc. It’s VERY powerful and not well understood by many, so in this video I break down everything you need to know about MCP at a high level. I go quick here unlike my usual videos, but I call out a bunch of different resources you can use to dive into anything deeper that you’re curious about - MCP architecture, building your own MCP server, integrating your custom AI agent with MCP, etc. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Check out Stagehand, an incredible tool to crawl and scrape websites with natural language which I used in this video: https://github.com/browserbase/stagehand And here is the Stagehand MCP server that I showcased (you will need a Browserbase API key which is free to start!): https://github.com/browserbase/mcp-server-browserbase/blob/main/stagehand/README.md ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Documentation for Claude’s MCP: https://modelcontextprotocol.io/introduction List of MCP Servers on GitHub: https://github.com/modelcontextprotocol/servers Example n8n MCP Agent: https://github.com/coleam00/ottomator-agents/tree/main/n8n-mcp-agent n8n Community Node for MCP: https://github.com/nerding-io/n8n-nodes-mcp Example Pydantic AI MCP Agent: https://github.com/coleam00/ottomator-agents/tree/main/pydantic-ai-mcp-agent Dive deep into the architecture of MCP: https://modelcontextprotocol.io/docs/concepts/architecture ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 00:00 - MCP is Blowing Up 01:55 - What is MCP? 03:12 - Making MCP "Click" (Deep Dive with Diagrams) 05:33 - How Agents Work with MCP 07:16 - Word of Caution - What MCP Isn't 08:17 - Where You Can Use MCP 09:47 - MCP Servers You Can Use NOW 11:18 - How to Set Up MCP Servers 12:08 - Using MCP Servers in Claude Desktop 13:11 - MCP Demo in Claude Desktop (Brave + Stagehand) 14:09 - Building with MCP (Servers and Clients) 15:22 - Building Your Own MCP Server 18:09 - MCP with n8n AI Agents 20:10 - MCP with Python AI Agents 21:56 - The Future of MCP 23:51 - Outro ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Join me as I push the limits of what is possible with AI. I'll be uploading videos at least two times a week - Sundays and Wednesdays at 7:00 PM CDT!

In this comprehensive guide, the author discusses MCP, it's components, and takes a deep dive on how to build servers from scratch.

In this video, I take a deeper dive look at the OpenAI Agents SDK and how it can be used to build a fast food agent. Colab: https://dripl.ink/MZw2R For more tutorials on using LLMs and building agents, check out my Patreon Patreon: https://www.patreon.com/SamWitteveen Twitter: https://x.com/Sam_Witteveen 🕵️ Interested in building LLM Agents? Fill out the form below Building LLM Agents Form: https://drp.li/dIMes 👨‍💻Github: https://github.com/samwit/llm-tutorials ⏱️Time Stamps: 00:00 Intro 00:11 Creating an In-N-Out Agent (Colab Demo) 00:40 In-N-Out Burger Agent 04:35 Streaming runs 05:40 Adding Tools 08:20 Websearch Tool 09:45 Agents as Tools 12:21 Giving it a Chat Memory

Deepseek R1 - the latest and greatest open source reasoning LLM - has taken the world by storm and a lot of content creators are doing a great job covering its implications and strengths/weaknesses. What I haven’t seen a lot of though is actually using R1 in agentic workflows to truly leverage its power. So that’s what I’m showing you in this video - we’ll be using the power of R1 to make a simple but super effective agentic RAG setup. We’ll be using Smolagents by HuggingFace to create our agent - it’s the simplest agent framework out there and many of you have been asking me to try it out. This agentic RAG setup centers around the idea that reasoning LLMs like R1 are extremely powerful but quite slow. Because of this, a lot of people are starting to experiment with combining the raw power of a model like R1 with a more lightweight and fast LLM to drive the primary conversation/agent flow. Think of basically giving R1 as a tool for an agent to use when it needs more reasoning power at the cost of a slower response (and higher costs). That’s what we’ll be doing here - creating an agent that has an R1 driven RAG tool to extract in depth insights from a knowledgebase. The example in this video is meant to be an introduction to these kind of reasoning agentic flows. That’s why I keep it simple with Smolagents and a local knowledgebase. But I’m planning on expanding this much further soon with a much more robust but still similar flow built with Pydantic AI and LangGraph! ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The Community Voting period of the oTTomator Hackathon is open! Head on over to the Live Agent Studio now and test out the submissions and vote for your favorite agents. There are so many incredible projects to try out! https://studio.ottomator.ai All the code covered in this video + instructions to run it can be found here: https://github.com/coleam00/ottomator-agents/tree/main/r1-distill-rag SmolAgents: https://huggingface.co/docs/smolagents/en/index R1 on Ollama: https://ollama.com/library/deepseek-r1 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 00:00 - Why R1 for Agentic RAG? 01:56 - Overview of our Agent 03:33 - SmolAgents - Our Ticket to Fast Agents 06:07 - Building our Agentic RAG Agent with R1 14:17 - Creating our Local Knowledgebase w/ Chroma DB 15:45 - Getting our Local LLMs Set Up with Ollama 19:15 - R1 Agentic RAG Demo 21:42 - Outro ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Join me as I push the limits of what is possible with AI. I'll be uploading videos at least two times a week - Sundays and Wednesdays at 7:00 PM CDT!