Hey guys 👋 A few of you reached out asking how to start from scratch and learn AI Agents. That’s a great question. If you’re asking it, it means two important things: - You genuinely want to learn and you're taking action - You’re aware of where you currently stand That mindset already puts you ahead. 👏 𝗧𝗵𝗲 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗕𝗹𝗼𝗰𝗸𝘀 𝗼𝗳 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 Before tools, frameworks, or money — there are fundamentals. 1. Learn enough about AI You don’t need a PhD, but you must understand: - What AI actually is (and isn’t) - What a neural network is (high level) - What an LLM is and what it’s capable of This gives you real intuition of these systems are capable of in real world applications. 2. Learn what an AI Agent is You should clearly understand: - The difference between an AI model and an AI Agent - Why agents exist in the first place - What problems agents solve that prompts alone can’t 3. Learn how to build This is your vehicle: - Either through programming - Or through no-code tools You’ll use this to turn ideas into real projects. 𝗜𝗳 𝗜 𝗪𝗲𝗿𝗲 𝗦𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗙𝗿𝗼𝗺 𝗦𝗰𝗿𝗮𝘁𝗰𝗵 (𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗣𝗮𝘁𝗵) 𝗧𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝗻𝗼 𝘀𝗵𝗼𝗿𝘁𝗰𝘂𝘁𝘀 You can’t jump from Z when your knowledge is X. You might feel some progress, but it’s self-deception. Real confidence comes from deep fundamentals. 𝗗𝗼𝗻’𝘁 𝗴𝗲𝘁 𝘀𝘁𝘂𝗰𝗸 𝗶𝗻 𝘁𝗵𝗲𝗼𝗿𝘆 Once you understand the basics: - Build something small - Break it - Fix it - Repeat That feedback loop is where learning accelerates. 𝗞𝗲𝗲𝗽 𝗱𝗼𝗶𝗻𝗴 𝗶𝘁 Your first project will be simple. Your second will be slightly better. Your tenth will finally make sense. Momentum > perfection. 𝗜𝗳 𝗬𝗼𝘂 𝗗𝗼𝗻’𝘁 𝗪𝗮𝗻𝘁 𝘁𝗼 𝗖𝗼𝗱𝗲 (𝗡𝗼-𝗖𝗼𝗱𝗲 𝗣𝗮𝘁𝗵) No-code is a valid path if your goal is speed. 1. Start by understanding core concepts: - What is an AI Agent - What is an LLM - What are tools - What is an API Then apply them using no-code platforms. 2. What no-code is great for: - Rapid prototyping - Validating ideas fast - Learning agent logic visually - Building MVPs - Non-technical founders

Hey everyone 👋 A few of you reached out asking about building AI agents with low or no coding skills. My honest take: the most powerful and flexible agents are still built with code.That said, no-code / low-code agents absolutely have real use cases, and they’re a totally valid path—especially if coding feels overwhelming right now (and honestly, learning to code has become way easier with today’s LLMs). To make this concrete, I put together a video where I build a fully working AI agent using n8n, with no coding required. If you’re curious about this approach or considering it as your entry point, check it out—and feel free to drop any questions below. Happy to help 👍

I just published a new video breaking down Anthropic's Agent Skills — and honestly, this solves one of the biggest pain points I see people struggling with: building agents that don't break when you try to scale them. Here's the problem: - You build an agent that works great in demos. Then you try to add more capabilities, more context, more tools... and suddenly you're hitting context limits. Or you're rebuilding the same functionality across different projects because nothing is reusable. Agent Skills fixes this. - Instead of loading everything into your agent's context upfront, you build modular "skills" that agents discover and load dynamically — only when they actually need them. It's like giving your agent a library instead of a single book. Claude Skills have 3 Levels: - Metadata Level: Just the skill name and description (Tells the Agent when to invoke a skill) - Core Level: Full instructions (Teaches the Skill to the Agent) - Supplementary tier: Extra docs and files (loaded on-demand) This means you can build effectively unlimited agent capabilities without maxing out context. Resources: • Original Article: https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills • Skills Cookbook (Practical Learning Guide): https://github.com/anthropics/claude-cookbooks/tree/main/skills

I just published a new video breaking down which AI agent frameworks actually matter in 2026 — based on what the top companies in the field are building with. If you're starting today and want to pick the framework that has demand and maturity to build production ready apps, this video is for you. Key insight: Biggest shift since last year: Visual tools are now production-viable (N8N, OpenAI Agent Builder, CrewAI, Flowise, etc...), but code-first frameworks (LangChain's LangGraph, Pydantic AI, Google ADK) are still the go-to for sophisticated applications that are meant to scale. Here's the link to the board I used in the breakdown. I hope this saves you weeks of trial and error 🙏

Hey everyone, I would love to know more about you and your learning expectations from this community, I'll be trying here to share some learnings about building AI Agents with various frameworks (LangGraph, N8N, etc), and also giving tips about selling them and built a freelancing career. So please take this opportunity to learn more about each other, and also letting me know your expectations!