NVIDIA Gives 38 AI Models FREE (No Credit Card) + I Built a 3D Website

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

Source video:
- Title: NVIDIA Gives 38 AI Models FREE (No Credit Card) + I Built a 3D Website
- URL: https://www.youtube.com/watch?v=_mRF3FzRiJY
- Topic: Creative Automation
- My current learning frame: Create a short-lived NVIDIA API key, connect it to OpenCode, verify one free model with a harmless prompt, switch models if it stalls, and ask the working model to build a simple one-page prototype.
- Why this matters: New playlist item from Free AI Vault; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Transcript anchors from this exact video:
- 0:00 / Evidence 1: "Okay, so I didn't build this website like at all. I didn't write a single line of it. A free AI model did. And this helmet, that's real 3D. I can grab it and spin it around right..."
- 2:33 / Evidence 2: "safe. And never share this key. Not in comments, not in screenshots. That's why it's blurred here. Next up, open code. It's a free AI coding agent that runs right in your terminal. You'll need node.js on your..."
- 4:40 / Evidence 3: "real 3D models. I paused the recording while it worked because this part takes a while. The last run alone took about 5 minutes. And when it was done, it opened the page in my browser, by itself."

Video-aware target:
- Prompt lane: Design system
- Mechanism to extract: Extract how the video turns visual references or component systems into usable constraints for agents.
- Artifact to produce: A design-system adoption brief with source references, tokens/components, agent handoff rules, and visual QA checks.
- Artifact must include: references; tokens/components; handoff artifact; implementation rule; visual QA

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 how the video turns visual references or component systems into usable constraints for agents. Do not invent claims that are not supported by the title, lesson frame, or transcript anchors.
4. Build a reusable learning artifact: A design-system adoption brief with source references, tokens/components, agent handoff rules, and visual QA checks.
5. Include:
   - a plain-English definition of the core idea
   - a diagram or structured model using this sequence: Reference -> Tokens -> Components -> Usage rules -> Agent prompt context -> Implementation -> Visual QA
   - answers to these source questions: What design source is reused? | How is it translated into agent context? | What review catches generic output?
   - 3 concrete examples that apply the video idea to real agentic work, such as Figma-to-shadcn workflow; design.md brief; UI reference library remix
   - 2 failure modes the video helps prevent, chosen from the transcript evidence and these likely risks: copying visuals without rules; generic generated UI; no visual QA screenshot pass
   - a checklist for the next real workflow, focused on: references, tokens, components, handoff, QA
   - one practical exercise with a clear done signal: Turn one screen reference into five constraints a coding agent must follow.
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 "NVIDIA Gives 38 AI Models FREE (No Credit Card) + I Built a 3D Website", not a generic Creative Automation essay.
- Cite transcript anchors for every claim about design context, UI generation, preview, critique, or handoff.
- 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 design inspiration; component lists without usage rules; no screenshot review.
- 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.