4 Free Models, 1 Winner

You are helping me turn one specific YouTube video into real, durable learning.

Source video:
- Title: 4 Free Models, 1 Winner
- URL: https://www.youtube.com/watch?v=gAQasQoPuE8
- Topic: Creative Automation
- My current learning frame: Recreate the experiment: run two free models in separate OpenCode sessions on the identical LLM-refined portfolio brief, then a second round demanding multi-file structure and cited research, and write a short verdict on which small design details separated the winner.
- Why this matters: New playlist item from CodingLab; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Transcript anchors from this exact video:
- 0:42 / Evidence 1: "side, each one building the exact same thing. So, let me make the directory, move into it, and from here I'll run three separate instances of Open Code. One dedicated to each model I want to compare. So,..."
- 3:15 / Evidence 2: "anything it needs to know before it starts building. That way, the prompt that finally goes to the models is as clear and complete as it can be. All right, the prompt is ready, so let's go. All..."
- 4:58 / Evidence 3: "uses it under the hood by default. So, that's model number one locked in. I'll scroll down, paste in the prompt, "Build a website for me based on the following information." And then, just let it go and..."
- 11:17 / Evidence 4: "one that actually works, so I can slot a proper fourth contender into that empty spot. The replacement comes from the Open Code lineup, and Mimo gets brought in here, too. Mimo actually sounded really promising to me."
- 14:20 / Evidence 5: "and design guidelines to the model. If I can feed it that kind of structured design knowledge, then it's not just guessing at what looks good. It's working from actual principles, and the output should feel a lot..."
- 16:39 / Evidence 6: "open-source project. Okay, I realized Oh, this is the open-source project, so The Okay, these are the things that these two found. How on this did I not know all this earlier? This is going to save my..."
- 19:31 / Evidence 7: "available to it, I should get something that feels intentional and distinctive instead of yet another safe, generic-looking page like the ones we've been getting. And in case you don't know what sub-agents are, it's basically like taking..."

Video-aware target:
- Prompt lane: Creative automation
- Mechanism to extract: Extract the creative production loop, especially where the human keeps taste, selection, and final judgment.
- Artifact to produce: A creative production board with source inputs, prompt recipe, selection criteria, edit pass, and taste-review checkpoints.
- Artifact must include: brief; source inputs; generation recipe; selection criteria; edit/review checkpoint

Your task:
1. Use the transcript anchors above as the primary source packet. If you add outside context, label it clearly as outside context and keep it secondary.
2. Create a source-check table with columns: timestamp, claim, transcript support, what the demo proves, confidence, and what still needs verification.
3. Extract the actual teachable mechanism from the video: Extract the creative production loop, especially where the human keeps taste, selection, and final judgment. Do not invent claims that are not supported by the title, lesson frame, or transcript anchors.
4. Build a reusable learning artifact: A creative production board with source inputs, prompt recipe, selection criteria, edit pass, and taste-review checkpoints.
5. Include:
   - a plain-English definition of the core idea
   - a diagram or structured model using this sequence: Brief -> Source material -> Generation -> Selection -> Edit -> Taste review -> Reusable recipe
   - answers to these source questions: What asset is being produced? | What inputs and tools drive it? | Where does human taste intervene?
   - 3 concrete examples that apply the video idea to real agentic work, such as Claude-generated video campaign; image-to-site workflow; voice or video editing loop
   - 2 failure modes the video helps prevent, chosen from the transcript evidence and these likely risks: mistaking novelty for quality; no source/brief discipline; shipping generated media without taste review
   - a checklist for the next real workflow, focused on: brief, inputs, generation, selection, critique
   - one practical exercise with a clear done signal: Build one reusable creative recipe and define what would make the result rejectable.
6. Add a "learning transfer" section: what changes in my workflow tomorrow if I actually learned this?
7. Add a "source check" section that cites which transcript anchor supports each major takeaway.

Quality bar:
- Make this specific to "4 Free Models, 1 Winner", not a generic Creative Automation essay.
- Anchor each creative step to transcript evidence about inputs, model/tool choices, iteration, editing, or critique.
- Prefer operational examples, failure modes, and reusable artifacts over broad definitions.
- Call out uncertainty instead of smoothing over weak evidence.
- Avoid these generic drifts: generic content advice; tool hype; creative output without selection criteria.
- If evidence is weak or missing, stop and say what transcript segment or timestamp needs review instead of guessing.
- Finish with a concise artifact I could paste into my learning app.