The era of prompts is over. The era of Agentic AI systems has begun. I’m excited to introduce Agentic AI Mastery: Claude Code, Clawdbot & Beyond — a hands-on course designed for engineers and builders who want to move beyond chatbots and build real multi-agent, production-ready architectures. In this course, you’ll design event-driven systems, implement guardrails, build personal and enterprise agents, and ship a complete capstone project ready for your portfolio. If you want to think like an AI systems architect and build autonomous workflows that actually scale — this course is for you. The future is agentic. Let’s build it.

Join me in WhatsApp - we can communicate faster https://chat.whatsapp.com/JuqbToSN6bcJAY94jLzZEd

Over the next few weeks, we're launching a video series that takes you end-to-end from "I just installed Ollama" to "I can design and ship serious, graph-based AI agents using LangChain and LangGraph, entirely on my own machine." Who This Is For This journey is designed for: - Developers and data practitioners who want hands-on experience building agentic AI systems - Startups prototyping AI features before committing to expensive API costs - Enterprise teams with data privacy requirements who need local-first solutions - Tech leaders and researchers who want to deeply understand agent architectures without being locked into hosted models Prerequisites Before starting, you should have: - Basic Python knowledge: functions, classes, pip, virtual environments - API/JSON familiarity: understanding request/response patterns - Terminal comfort: running commands, navigating directories - Hardware: 16GB+ RAM recommended; GPU optional but helpful for larger models What This Journey Covers Across the series, we'll walk through five major phases. Each phase ends with tangible projects you can run locally and adapt to your own use cases. --------------------------------------------------------------- Phase 0 – Local Stack: Ollama + Python Estimated time: 1-2 hours We begin by setting up a local AI sandbox: - Installing and configuring Ollama - Pulling and running popular open models (e.g., Llama, Mistral) - Understanding hardware requirements and model selection for different tasks - Creating a clean Python environment and wiring up a minimal script to talk to a local LLM By the end, you'll have a lightweight local playground where you can experiment without API keys or usage limits. Troubleshooting covered: Model selection guide, memory optimization, when local models shine vs. their limitations --------------------------------------------------------------- Phase 1 – LangChain Fundamentals with Local Models Estimated time: 3-4 hours Next, we introduce LangChain as the "capabilities layer" on top of your local model:

Most professionals have a scaling problem. Your knowledge is valuable. Your time is limited. So every time you teach, present, onboard, or create content, you start from scratch. That’s exactly what this course fixes. I just launched Build Your Digital Twin — a hands-on course where you learn how to turn your expertise into an AI Digital Twin that can: • Teach • Present • Create slides • Generate videos • Scale your knowledge without scaling your time You’ll build this using ChatGPT, Gamma, and HeyGen—step by step. 💡 For a limited time, the entire course is available for $1. Yes. One dollar. 👉 Enroll here: https://maven.com/the-school-of-ai/build-your-digital-twin?promoCode=DEAL If you’ve been thinking about: • Creating courses • Scaling your content • Using AI beyond chat • Building leverage with your expertise This is the easiest place to start.

I’ve opened up my AI Engineer Complete Bootcamp to my network for a limited time. This is a 14-week, end-to-end AI engineering program where I teach how to go from Python fundamentals to building and deploying production-ready AI systems. The course is normally $199, but for my Skool network, it’s available right now for a discounted price. Why? Because too many people learn AI in theory and still struggle to build real systems. This course is designed to fix that—hands-on, practical, and project-driven. If you’ve been meaning to: - Transition into AI engineering - Build a serious AI portfolio - Learn ML, deep learning, and MLOps the right way This is the best time to start. https://maven.com/the-school-of-ai/ai-2026-engineering-bootcamp?promoCode=SKOOL If you want to get a free preview - check this out https://maven.com/p/22c19b/what-it-takes-to-become-an-ai-engineer-live-breakdown?utm_medium=ll_share_link&utm_source=instructor