I analysed how the creator of OpenClaw uses AI coding agents. Here are top takeaways: 1. Runs 3-8 agents at the same time — each in its own terminal window. Lets them work for hours without checking on them. 2. Gives agents testing tools — so they can check their own work instead of waiting for him to verify everything. 3. Short prompts — 1-2 sentences max. The AI reads your code anyway, no need for long explanations. 4. Screenshots over descriptions — "fix the padding" + a screenshot beats writing a paragraph explaining what's wrong. 5. Command-line tools over plugins — AI already knows how to use git, database commands, etc. No need to add extra tools. 6. "Write tests right after the feature" — the agent just built it, it knows what might break. Don't start a new chat to ask for tests later. 7. Think about scope first — before starting, estimate how big the change is. If it's taking too long, stop and ask for a status update. 8. Commits straight to main — only uses branches when unsure about big architectural decisions.

Hey everyone, I built a small app that lets you chat with your favourite youtube channel, you can give it the link to the channel, and just ask any question about anything they ever said. In this video I break down how I've done it, and how you can build similar systems too.

Hey everyone! I'd like to share a mini story about an agent I built and sold online. The idea was an intake agent that handles incoming client calls — it has a conversation with them and generates a report based on the call. In this video, I walk you through the technical details behind it and share the code as well (linked below), in case you want to build something similar or create your own voice agent.

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