Welcome to the Out of the Box series — where I explore what can be built with no-code and low-code AI tools in 30 minutes or less. No manuals. No tutorials. Just curiosity and creation in motion. This time I revisited Sora 2 a few months later to see how the experience has evolved. App: Sora by OpenAI Time: Under 30 Minutes Category: AI Video Creation / Prompt-Directed Video Video Title: Move Over Rover, The Dog Days of Coding Are Over - Claude Code is The Cats Meow 🎥 What Is Sora? Sora is an AI video generation platform that transforms a simple text prompt into lifelike, cinematic scenes — complete with motion, lighting, and visual storytelling. Think of it as having a director, camera crew, and editor… all powered by a prompt. ⚙️ Experience 1 — The First Test A few months ago, I ran an Out of the Box experiment with Sora using a simple presenter-style scene. The results were impressive for early generative video, but the workflow still felt a bit like experimentation. The outputs were interesting, but not something that added much practical value beyond demonstrating what the technology could do. If you’re curious about that original test, you can see the full post here: 👉 https://www.skool.com/ai-bits-and-pieces/out-of-the-box-in-30-sora-2?p=e63f6633 That first experiment helped show what was possible, but the bigger question was how quickly the experience would evolve. ⚙️ Experience 2 — Revisiting It Today For the second experiment, I tried something completely different — a playful, high-motion scene designed to test character behavior and storytelling. Prompt theme: A cat driving a quad runner at high speed — Fast & Furious style — with a dog riding on the back howling and clearly terrified. The twist: - The cat is labeled “Claude Code.” - The dog is labeled “ChatGPT.” Experiment 2 Video: https://sora.chatgpt.com/p/s_69b4d4703dbc819180c914a61747c81f?psh=HXVzZXItQWI5dFRpa3JRS1RTSmhwbDY3VlFYaWxv.4nGp4ZY9Gsxo

This is a follow-up to my original “Wine Glass Test” — a simple experiment that turned into something more interesting. After my first post, I received a thoughtful suggestion from @Matthew Sutherland. His advice was straightforward: Be more prescriptive. So I refined the prompt to this: “Create a glass of wine that is full, red wine. It needs to be at the brim, so not to run over, and not below the brim to show any space between the brim and the surface of the wine in the glass.” The image below is the direct result. And the result is telling. 🍷 What This Actually Proves This wasn’t about aesthetics. It was about bias and instruction. When I originally asked for a “full glass of wine,” the model produced what most restaurants would call full — but still left space at the top. That’s not an error. That’s statistical bias. The model leaned into the most common interpretation of “full.” When the instruction became extreme and structured, the behavior changed. It complied precisely. 🍷 There are two observations that I see with this test: 1️⃣ Prompting Is a Skill We often talk about model bias as if it’s a flaw. It’s not. It’s probability doing what probability does. My first prompt allowed the model to default to “standard pour.” The refined prompt removed ambiguity. By defining the boundary conditions — no gap, no overflow — the model had to break from its average tendency and execute exactly. That’s not luck. That’s instruction design. Prompting isn’t just writing a sentence. It’s mapping expectation into structure. And as Matthew pointed out, that skill develops iteratively. 2️⃣ Natural Language Still Has Friction The deeper takeaway isn’t that the model can create a perfectly full glass. It’s that everyday language is still ambiguous to it. When a human says “full glass of wine,” we infer intent through context. The model infers through probability. Those are not the same. For AI to feel seamless in daily life, we shouldn’t need to mathematically define “full.”

And as it is in the beginning, you cannot afford employees. So you do everything yourself. But we are lucky. Today we have AI. And AI can really help if we use it the right way. Right now I am thinking about building a small “virtual team” with Claude AI and Cowork. Maybe a CEO assistant to help me structure decisions.A strategist for positioning and planning.Someone for marketing ideas and content. There are so many possibilities. Maybe you can give me some more hints I do not want to reinvent the wheel. I am sure there are already good skills, prompts, or setups out there that I can use. My question to you: Where do you find good and useful resources?GitHub? Specific websites?Or is there something already inside this community? I would really appreciate your tips. In the beginning, this can make a big difference. Thank you 🙌

🌀 You don’t need to binge AI — you need to practice it. ✨ Why This Quip Matters Many people treat AI like a crash diet or bootcamp — intense for a day or two, then gone. The real power of AI isn’t in the big sprint; it’s in consistent, daily use. Small experiments, brief daily reps, and intentional practice build fluency over time. One better prompt. One practical use case. One improvement a day. That’s what turns AI from a tool into a capability. Consistent habits beat frantic pushes every time. 📹 Real Story of the Week — Video Insight 👉 https://youtu.be/c7zp3w-QX9U Advice for Programming Beginners: How to Get Started with AI Agents — a video by Lex Freidman and Peter Steinberger (creator of OpenClaw) on YouTube. In this video, Peter breaks down how beginners should approach AI: not by trying to master everything fast, but by building foundational habits first — start small, learn a bit each day, and integrate AI agents gradually into your workflows. Rather than aiming for perfection, the video emphasizes experimentation, iteration, and practical application as the keys to progress. Pro Tip: Consistent daily practice matters more than occasional deep dives.

We just crossed the 500-member mark here at AI Bits & Pieces. Wow! When I started this community, I simply felt that AI was becoming something bigger than tools or trends. It felt like true a shift in the way we would interact with technology — and I wanted to create a place where people could learn, explore, and apply it in a thoughtful way. What makes this milestone meaningful isn’t just the number. It’s the people. We have members who are: - Just beginning their AI journey - Deepening their prompting fluency - Building real systems and automations - Applying AI inside established businesses That range matters. It creates perspective. It creates better conversations. It creates learning in both directions. To everyone who has contributed, asked questions, shared insight, encouraged others, or quietly followed along — thank you. Your presence shapes this space. We’re going to continue refining the classroom, adding live sessions, and building clearer paths for each stage of the AI journey. I’m grateful you’re here. Thank you, @Michael Wacht