This is the website I created for them — pikolhaus.com. Small start, but officially my first real client project. More to come.

Apparently “I’ll clean up a few lessons” turned into rebuilding a whole module 😂 The classroom is now called AI Animations & Workflows, and Module 1 has seven new videos, eight connected lessons, and a clearer route from your first idea to a finished animation. I made this because I want you to be able to build a workflow you can keep using. Take an idea, give it a home in a folder, and have your AI help write the script, make the voice, plan the visuals, build the scene and export the video. The AI can do a lot of that work. Understanding what each stage produces helps you ask for useful changes and check whether the result actually explains your idea, right? We follow one small example through the lessons, then you use the same process for something of your own. The videos also use a new style: more visual explanations, app views, moving diagrams and examples that connect directly to what’s being said. Start with 1.1: Welcome and Your Route Through This Module, then work through: - 1.2 How This Video Was Made — see the whole process before setting things up. - 1.3 Get the Workspace Ready with Your AI — choose your starting route and prepare your project. - 1.4 Ask for a Script, Then Make the Voice — turn the idea into a spoken explanation. - 1.5 Turn the Voice into a Visual Plan — decide what someone should see as they listen. - 1.6 Let AI Build the Scene — build the pictures and review whether they help. - 1.7 Fix What You See, Then Export — make a specific correction and check the finished video. - 1.8 Make the Next Video with Your Own Idea — carry the useful tools and instructions into your own project. -

Everything in this course has been really good, and @Bas Rosario context engineering series is right up there with it! The quick card is practical. It gives you seven lines to write before you ask an AI for anything, a way to check what you're feeding it, and a way to test it. One line from it has stuck with me: lucky is context you never wrote down. So I went back through a weekend of my own prompts to see which lines I actually use. What I was doing before. I always said what I wanted. Most of the time I said why, and where the files were. Sometimes I said what was off limits. What good looks like, I gave as adjectives. I told it to make things "awesome" or "bulletproof," and the AI can't see an adjective. What bad looks like, I never gave up front. I gave it three times in one weekend, but only as corrections after a draft came back wrong: don't name where I work, don't introduce me to people who already know me, and don't make it sound like I walk into a client with a solution already picked. Bas bet that line seven is the one most people skip. He was right about me. What I did about it. I wrote one room for the prompt I use most. Those repeat corrections are now line seven, each with one line on why. Then I tested it the way the card says, in fresh windows. Without the room, the answer sounded good but filled the gaps with things that weren't true. With the room, it left blanks where only I knew the answer. The test also showed the room wasn't perfect. One of my rules was too strict and blocked a check I needed, so I fixed it and saved a second version. What I'm going to do next. Write line seven before the first draft, not after. Replace my adjectives with a real example or a check you can pass or fail. Build a room for the next prompt I use a lot, and keep adding the corrections to it so I stop repeating them. This is the kind of stuff I can really dig into. Thanks, @Bas Rosario

🎁 $1,000 IN EDUBAWARE CREDITS 🎁 One winner takes it. 📋 THE CHALLENGE Build a folder-based AI translator that takes one kind of work and turns it into another kind of work. Same shape in, same shape out, every time. Not a summarizer. Not a writer. A converter with a contract. This week's deliverable is one translator folder that someone could drop into a Claude project, feed it the input it expects, and get back the output it promises. Every time. Without surprises. 🎯 PICK YOUR CONVERSION The conversion is yours. Pick one you do by hand right now and hate. A few sparks to get you thinking: - 📞 Sales call transcript → CRM notes in your team's exact fields - 📝 Long-form essay → LinkedIn carousel, slide by slide - 🎙️ Meeting recording → product requirements doc - 🔬 Research paper → investor one-pager - 🐛 Bug report thread → Jira ticket with repro steps - 📧 Customer email → support ticket with severity and category - 📊 Spreadsheet export → weekly status update - 📖 Interview transcript → case study draft - 🧾 Receipt photos described in text → expense report line items - 📋 Discovery call notes → SOW first draft The more locked-down the output, the better. "Turns notes into a doc" is not a contract. "Turns discovery call notes into a five-section SOW where section 3 is always scope exclusions" is. 🔥 THE ANGLE THIS WEEK Last comp was The Auditor. Every finding cited a provision so a reader could open the standard and check. The comp before that was The Cartographer. Every card cited a file and a line so a reader could open the source and check. The Translator is the same discipline, one more time. Every line in the output traces to a line in the input. ↔️ A translator has three properties that a summarizer does not: 1. The output has a fixed shape. Same fields, same order, same format, regardless of what the input looked like. If the input was messy, the output is still clean. If the input was short, the output still has every field, marked empty where there was nothing to fill it.

A quick follow-up from Weekly Comp #11: The Cartographer — I’ve been using the idea across my projects, and it has become more valuable as the systems grow. It gives Codex a clear map of the modules, dependencies, flows, and possible blast radius before making changes. In practice, it saves me time and tokens because the agent doesn’t need to repeatedly inspect the whole codebase just to understand how things connect. Even when I use ASTRA for deeper planning and audits, having the system map already available makes the process more focused and efficient. Definitely one of the competition ideas that proved genuinely useful in real project work. Thank you @Jake Van Clief and team