This is a $1,000 course. I'm giving it away free. I'll tell you why (because it's important.) Keep reading. A year ago, I needed an AI executive assistant for myself. Not a chatbot. Something that ran on my machine, prepped my mornings, and managed my calendar without me micromanaging it. I couldn't find one. So I built it. I called it a Daily Brain. The first version took me 4 weeks. Most of those 4 weeks went to one thing: my first prompt. Not getting it to work in a sandbox. Getting it to work on MY machine. Once I got past it, every agent I built came in days, not weeks. That's when I realised something. The steps I took to get my first prompt working are universal. Every builder needs them. So I turned the build into a 6-module course. 3.5 hours. No coding knowledge required. What you build: (Quick note: the course is built inside Claude Code. The only way you take the course is by building inside Claude Code. 100% focused on BUILDING) 𝗠𝗼𝗱𝘂𝗹𝗲 𝟭: 𝗬𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝗮 𝗺𝗲𝗲𝘁𝗶𝗻𝗴-𝗻𝗼𝘁𝗲𝘀 𝘀𝘂𝗺𝗺𝗮𝗿𝗶𝘀𝗲𝗿 You build a tool that reads your meeting notes and writes the summary. You learn the 5-part prompt shape. 99% of prompts fail because they skip 3 of the 5 parts. 𝗠𝗼𝗱𝘂𝗹𝗲 𝟮: 𝗬𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝘁𝘄𝗼 "𝗦𝗸𝗶𝗹𝗹𝘀" A summariser and one of your choice. You learn how Claude routes between Skills based on what you say. No commands. No menus. 𝗠𝗼𝗱𝘂𝗹𝗲 𝟯: 𝗬𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝗮 𝗳𝘂𝗹𝗹𝘆 𝗶𝗻𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝘁 𝗱𝗮𝗶𝗹𝘆-𝗯𝗿𝗶𝗲𝗳𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁 The agent that reads your work and tells you what to do next. You learn the four parts every working agent has: Brain, Goal, Tools, Memory. 𝗠𝗼𝗱𝘂𝗹𝗲 𝟰: 𝗬𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝗮𝗻 𝗮𝗴𝗲𝗻𝘁 𝘁𝗵𝗮𝘁 𝗺𝗮𝗻𝗮𝗴𝗲𝘀 𝘆𝗼𝘂𝗿 𝗰𝗮𝗹𝗲𝗻𝗱𝗮𝗿 You build an agent that connects to Google Calendar and books meetings for you based on the details from your emails and tasks. You learn MCP — the protocol that connects AI to any app. 𝗠𝗼𝗱𝘂𝗹𝗲 𝟱: 𝗬𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝗮𝗻 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿 𝗮𝗴𝗲𝗻𝘁 𝘁𝗵𝗮𝘁 𝗰𝗼𝗻𝘁𝗿𝗼𝗹𝘀 𝘄𝗶𝘁𝗵 𝘁𝗵𝗿𝗲𝗲 𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘀𝘁𝘀 𝗮𝗴𝗲𝗻𝘁𝘀 You build a system where one coordinator runs three specialists in parallel — notes, calendar, follow-ups. You learn the orchestration pattern every production AI system runs on. 𝗠𝗼𝗱𝘂𝗹𝗲 𝟲: 𝗬𝗼𝘂 𝗹𝗲𝗮𝗿𝗻 𝗵𝗼𝘄 𝘁𝗼 𝘁𝘂𝗻𝗲 𝘆𝗼𝘂𝗿 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺 You learn how to monitor and control quality, latency, and cost. You learn how to alter different parts of your product to control the three levers. What you keep after the course: The 5-part prompt shape. Agent anatomy. The chief-and-workers pattern. The three knobs. 𝗪𝗵𝘆 𝗶𝘀 𝗶𝘁 𝗳𝗿𝗲𝗲? The hard part of learning to build is the first hour. After that the curve gets easy. I wanted to remove the friction. (Link in comments) 90 days from now, the PMs who took it are going to look very different from the PMs who saved it for later.

Most PMs spend their careers describing software for someone else to build. A few stopped. They learned Claude Code, and now they build it themselves. One shipped a working AI second brain in a weekend. No engineer. No coding knowledge You can build yours too. In 30 mins. Learn Claude Code inside Claude Code.

Every few years, one tool separates the PMs who get promoted from the ones who get stuck. 2014 it was SQL. 2019 it was Figma. 2026 it's Claude Code. The ones who learn it early move up early. You can learn it for FREE. 6 lessons. You build your own AI second brain by the end. (link in comments)

The slowest part of being a PM was never the thinking. It was the waiting. Wait for the sprint. Wait for priority. Wait two quarters to learn the idea was wrong. The PMs moving fastest stopped waiting. They build it themselves with Claude Code. Learn Claude Code for FREE. Start building on your own today 👇

Include these 5 components in your next Claude prompt and see how your world transforms. 1. 𝗥𝗼𝗹𝗲: who is the AI acting as?    This helps set perspective. Enable the LLM know what type of language to use -- a senior PM talks differently compared to a senior sales executive 2. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁: what extra information does the model have?    The more context the model has the higher the chances it will create responses that are relevant and accurate. 3. 𝗧𝗮𝘀𝗸: what should the model do?    Give clear instructions, so the model knows exactly what it needs to do. "Write an email" is not specific. "Write a 200 word launch announcement, short sentences, bullet points, clear, 5th grade english...:" is specific. 4. 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀: what boundsaring should the model know about?    This will help the model focus on the right things (and stay away from the wrong things.) Without constraints, the model will make assumptions. Not all of them will be right. 5. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: what does good output look like?    Tell the model what good, bad, ugly looks like, so it has a benchmark. Need help getting started?Sign up for my free course that teaches Claude Code inside Claude Code. Module 1 shows you exactly how to create en effective prompt with all 5 components. (Link in comments)

You could doom scroll Youtube today. Or you could build your own AI Assistant with Claude Code (in less than 3 hours, without writing a single line of code) I made a free course that teaches you Claude Code inside Claude Code. The course includes 6 core lessons, and takes only 3 hours. By the end of it, you will have built your own Second Brain from scratch which runs on 4 independent agents. Here is what you will learn 𝗟𝗲𝘀𝘀𝗼𝗻 𝟭 - 𝟮𝟱 𝗺𝗶𝗻: 𝗟𝗲𝗮𝗿𝗻 𝗵𝗼𝘄 𝘁𝗼 𝘄𝗿𝗶𝘁𝗲 𝗽𝗿𝗼𝗺𝗽𝘁𝘀 𝘁𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸. You learn the 5 components of a prompt that gets you a good answer the first time, every time. This is the skill every other lesson is built on. 𝗟𝗲𝘀𝘀𝗼𝗻 𝟮 - 𝟯𝟬 𝗺𝗶𝗻: 𝗕𝘂𝗶𝗹𝗱 𝘆𝗼𝘂𝗿 𝗳𝗶𝗿𝘀𝘁 𝗦𝗸𝗶𝗹𝗹. Once a prompt works, you want to use it again without typing it out. You learn how to turn it into a Skill that Claude picks up on its own when you ask in plain English. Build multiple skills and then see how Claude routes your query to the right one automatically. 𝗟𝗲𝘀𝘀𝗼𝗻 𝟯 - 𝟯𝟱 𝗺𝗶𝗻: 𝗕𝘂𝗶𝗹𝗱 𝘆𝗼𝘂𝗿 𝗳𝗶𝗿𝘀𝘁 𝗮𝗴𝗲𝗻𝘁. A Skill runs when you ask. An agent runs on its own to hit a 𝘨𝘰𝘢𝘭 you set. You learn the four parts every agent is made of. Brain, goal, tools, memory. And then you build your very first AI agent end to end. 𝗟𝗲𝘀𝘀𝗼𝗻 𝟰 - 𝟰𝟱 𝗺𝗶𝗻: 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝘁𝗼 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝘄𝗼𝗿𝗹𝗱. An AI agent that is limited to a prompt can only do a few things. In this module, you learn how to give it real tools, so it can take real actions in real apps. You will connect your agent to Google Calendar, so it can create meeting invites, reminders, events for you when needed. 𝗟𝗲𝘀𝘀𝗼𝗻 𝟱 - 𝟱𝟬 𝗺𝗶𝗻: 𝗕𝘂𝗶𝗹𝗱 𝗮 𝘁𝗲𝗮𝗺 𝗼𝗳 𝗮𝗴𝗲𝗻𝘁𝘀. One agent doing everything gets slow and messy fast. You learn how to create AI agent teams that run your entier system. One coordinator/CEO gives orders. Specialists do the work in parallel. You watch four of them work at once on a live dashboard. (Exactly what you see in the visual below.) 𝗟𝗲𝘀𝘀𝗼𝗻 𝟲 - 𝟰𝟬 𝗺𝗶𝗻: 𝗠𝗮𝗸𝗲 𝗶𝘁 𝗳𝗮𝘀𝘁𝗲𝗿 𝗮𝗻𝗱/𝗼𝗿 𝗰𝗵𝗲𝗮𝗽𝗲𝗿. Every AI system has the same three constraints: quality, latency, cost. You learn which one to turn for which problem, and watch the speed and cost change on screen as you do. The course is 100% FREE. But not for long. It includes everything I know about Claude Code and you can learn it in 3 hours for Free. Start today. Link in comments

Don't pay $100/month for the Claude Max plan. Use these 7 hacks to protect your tokens (and your money): 1. When you keep 𝗺𝗮𝗻𝘆 𝘁𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗼𝗻𝗲 𝗰𝗵𝗮𝘁, Claude rereads all of them every time you send a message, even the ones you are done with. 𝗙𝗶𝘅: Start a new chat for each topic. When one gets long, ask for a short summary, copy it, and paste it into a fresh chat. 𝟮. 𝗪𝗵𝗲𝗻 𝘆𝗼𝘂 𝘂𝗽𝗹𝗼𝗮𝗱 𝗮 𝗣𝗗𝗙, Claude pays for it twice. Once to read the words, and again because it turns every page into an image and reads that too. Fix: Copy the text out, paste it into a plain document, and upload that. Same words, a fraction of the cost. 3. Claude has three models.: A fast cheap one, a balanced one, and a heavy expensive one. Most people leave it on the heavy one for everything, even fixing typos. Fix: Use the cheap one for small tasks and the heavy one for hard problems. 4. When Claude gets something wrong, you probably say "no, I meant this." That reply stacks on top of everything above it, and Claude rereads the whole pile to answer. Fix: scroll up, edit your original message, and run it again. 5. If you upload the 𝘀𝗮𝗺𝗲 𝗳𝗶𝗹𝗲 𝘁𝗼 𝗳𝗶𝘃𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗰𝗵𝗮𝘁𝘀, you pay to read it five times. Fix: Put it in a Project once instead. Every chat inside that Project can use the file without uploading it again. 6. You explain everything about you and your work repeatedly. Fix: Open Settings, then Capabilities, and tell Claude who you are, what you work on, and how you like answers. It carries that into every new chat, so you stop burning the first few messages re-explaining yourself. 7. Asking Claude to make a doc, sheet, or slide costs more than a normal answer. You build one, scrap it, and build again. And pay the cost for every iteration. Fix: Get the plan right in a plain chat first, then build it once. I changed those seven things and never hit the limit again. Same usage. Saving tokens is the easy part. The real shift is when you 𝘀𝘁𝗮𝗿𝘁 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗖𝗹𝗮𝘂𝗱𝗲. I made a free course that does exactly that. You learn Claude Code inside Claude Code. 6 Modules: 1. How to write effective prompts 2. How to build a skill 3. How to build an Agent 4. How to connect apps via MCP 5. How to build a second brain 6. How to optimize cost, latency, quality It takes 5 hours to build all of this. Completely FREE. (link in comments)

Last year I opened a folder on my laptop I had created in 2014. It's called "𝗠𝗶𝗹𝗹𝗶𝗼𝗻 $ 𝗶𝗱𝗲𝗮𝘀." It had more than twenty product ideas in it that I added over the last ~10 years. I read through them one by one, and most of them are still good ideas. But I had not built even a single one of them. I have always loved building things that could make someone's life a little easier, even if it helped only one person. But wanting to build was never my problem. Actually building was. Almost every idea in that folder needed a skill I did not have, like coding, mechanical engineering, or hardware. So I tried to learn those skills the usual way. I signed up for courses. I watched hundreds of hours of tutorials. I looked for co-founders who had the skills I was missing. Nothing worked. After a while I stopped trying, and I told myself this is just how it is. Some people have the ideas, and other people build them, and I was someone who only had ideas. Then about six months ago, almost on a whim, I took one idea off that list and decided to build it myself. And I did. The whole thing, fully working, on my own, without ever learning the skills I had failed to learn for years. I still remember the moment it worked for the first time. I just sat there looking at the screen. I realised that if I could build this one, I could build any of the others too. So I kept building. Since that first one I have built more than five real products that people use. I have started and dropped at least ten more. I am working on three others right now. I am building ideas from that list faster than I can add new ones. It has not been easy. There has been a lot of trial and error, some real wins and some real failures. The things that used to stop me have not all gone away. There are just far fewer of them now than there were five years ago. So I keep going, because I believe one thing. Right now, anyone who wants to build something can actually build it. 𝗔𝗻𝗱 𝗜 𝗮𝗺 𝗻𝗼𝘁 𝗴𝗼𝗶𝗻𝗴 𝘁𝗼 𝘀𝘁𝗼𝗽. That folder used to be a list of things I wished I could build. Now it is just a list of things I am building. And that is why I am writing this. What changed everything for me was 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝘄𝗶𝘁𝗵 𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲. It did not take years. It took weeks. So I put everything I learned into a FREE course that teaches claude code inside claude code: 1. writing prompts that Claude actually follows. 2. building a skill that really matters 3. building an agent that takes actions 4. building a second brain that keeps you on track If you have a folder of ideas you have never built, this is where you start building them. (Link in comments)

𝗔𝗜 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗶𝘀 𝗼𝘃𝗲𝗿𝗵𝘆𝗽𝗲𝗱. That is what I told myself when I first started working on AI products. Turns out, I had no idea what an AI PM really did. After spending years watching world-class AI PMs, building AI products at scale, making 100s of bad decisions, I have a much better definition of the role. But, sadly, even today, most PMs trying to work on AI are in the same boat: confused and clueless about "what does an AI PM actually do." Here's the simplest mental model to think of an AI PM's role: An AI PM is responsible for finding answers to these 7 questions. 1. What problem should we solve to maximize impact? 2. Does this need AI? 3. Do we have the right data? 4. How do we turn data into something useful? 5. How will users experience it? 6. How do we know it works before launch? 7. How do we keep making it better? Let's understand in detail: 𝗪𝗵𝗮𝘁 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝘀𝗵𝗼𝘂𝗹𝗱 𝘄𝗲 𝘀𝗼𝗹𝘃𝗲 The problem must be specific, validated with real users, and solution-agnostic. If you get this wrong, the model does not matter. 𝗗𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗿𝗲𝗮𝗹𝗹𝘆 𝗻𝗲𝗲𝗱 𝗔𝗜 This is the most important question an AI PM asks. And the answer is usually no. Saying no to AI when the situation does not call for it is not a failure. It is the job. 𝗗𝗼 𝘄𝗲 𝗵𝗮𝘃𝗲 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗱𝗮𝘁𝗮 "We have data" is not a strategy. An explicit data plan that includes what data we need, what we have, and what is missing is the right strategy. AI is only as good as the data behind it. 𝗛𝗼𝘄 𝗱𝗼 𝘄𝗲 𝘁𝘂𝗿𝗻 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝘂𝘀𝗲𝗳𝘂𝗹 Simple prompt, ML model, RAG, or agents. Each has a different use case, cost profile, and failure modes. The PM who skips this hands those decisions to engineering. 𝗛𝗼𝘄 𝘄𝗶𝗹𝗹 𝘂𝘀𝗲𝗿𝘀 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗶𝘁 Users need trust, control, and recovery. Design for when the AI fails, not only for when it works. 𝗛𝗼𝘄 𝗱𝗼 𝘄𝗲 𝗸𝗻𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝗹𝗮𝘂𝗻𝗰𝗵 There is no binary pass/fail. Build an eval framework. Define good, bad, and edge cases. Ship only when the product clears your threshold. 𝗛𝗼𝘄 𝗱𝗼 𝘄𝗲 𝗺𝗮𝗸𝗲 𝗶𝘁 𝗯𝗲𝘁𝘁𝗲𝗿 𝗮𝗳𝘁𝗲𝗿 𝗹𝗮𝘂𝗻𝗰𝗵 AI products degrade in production if you stop watching them. Sample live conversations. Add new failure modes to your test set. Never stop monitoring. -- Want to ship your first AI product? I just launched a free Claude Code course taught inside Claude Code. (Link below)