Interfaces + Open Design / Foundation

Design a Fully Animated Website with AI — Claude Code

This video shows how the creator built a fully animated portfolio website in about 2 hours by sketching states in Figma, generating warm-toned model photos with Nano Banana/Hexfield, and prompting Claude Code section-by-section to produce a flip-to-ticker intro, project pages, and an info page.

Ruben Stom8 minTranscript found

Quick learning frame

Read this before watching.

AI-native interfaces are control surfaces for intent, artifacts, context, preview, inspection, and iteration.

New playlist item from Ruben Stom; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Skill you build: Art-directing an AI coding tool to produce sequenced web animations by decomposing each interaction into opening, transition, and resting states and prompting them one at a time.

Watch for the shift from claim to mechanism. The learning value is the point where the transcript reveals a repeatable action, tool boundary, context move, review habit, or artifact.

Concept diagram

Where this video fits.

01Intent
02Canvas
03Artifact
04Preview
05Feedback
06Iteration

Deep lesson

Turn this video into working knowledge.

1,621 cleaned transcript words reviewed across 475 timed caption segments.

Thesis

Design a Fully Animated Website with AI — Claude Code teaches a practical interfaces + open design move: This video shows how the creator built a fully animated portfolio website in about 2 hours by sketching states in Figma, generating warm-toned model photos with Nano Banana/Hexfield, and prompting Claude Code section-by-section to produce a flip-to-ticker intro, project pages, and an info page.

The goal is not to remember the video. The goal is to extract the operating principle, tie it to timestamped evidence, test how far the claim transfers, and make something reusable.

0:51

Sketch as backup

“because I prepared a small free gift to you as a thank you for watching. Before I start prompting in Claude code, I sketched the concept for the website in Figma. And the purpose of these sketches is...”

Figma sketches aren't meant to be replicated pixel-for-pixel; they serve as a visual fallback Claude consults only when a text prompt is ambiguous, since text prompts are prioritized over the reference images. Sketch rough state references for a small layout, then write text prompts that carry the precise details and note which details you deliberately left to the prompt rather than the sketch.

1:54

Build animations in states

“these sections. This is the last design that I created for the about me page and I inverted the colors here just to keep things interesting and clearly distinguish it from the project pages. And I went for...”

Advanced transitions are prompted by describing a discrete opening state, a transition, and a resting state, so Claude can interpolate a smooth motion between two clearly defined endpoints (e.g. flip wordmark to spread-out ticker row). Pick one animation idea and rewrite your prompt as three labeled sections (opening state, transition, resting state) before generating any code.

5:31

Confirm before fixing

“please make the adjustments." And then it worked perfectly. And this is just one of those small techniques that can really improve your prompting by asking the model if it understands you before it starts making changes. Once...”

When a result is wrong and the cause is unclear, explain what you think went wrong and ask Claude to restate the issue back before requesting changes; its restatement often clarifies the problem better than the original description and leads to a correct fix. On your next broken AI output, withhold the fix instruction and first ask the model to explain the issue in its own words, then approve only once it matches your intent.

01

Intent

Start with this video's job: This video shows how the creator built a fully animated portfolio website in about 2 hours by sketching states in Figma, generating warm-toned model photos with Nano Banana/Hexfield, and prompting Claude Code section-by-section to produce a flip-to-ticker intro, project pages, and an info page. Treat "Intent" as the outcome you are trying to make visible, not a topic label. Anchor it to 0:51, where the video says: “because I prepared a small free gift to you as a thank you for watching. Before I start prompting in Claude code, I sketched the concept for the website in Figma. And the purpose of these sketches is...”

02

Canvas

Use "Canvas" to locate the part of the interfaces + open design workflow the video is demonstrating. Ask what changes in your real setup if this claim is true. Anchor it to 1:54, where the video says: “these sections. This is the last design that I created for the about me page and I inverted the colors here just to keep things interesting and clearly distinguish it from the project pages. And I went for...”

03

Artifact

Turn "Artifact" into the reusable artifact for this lesson: A UI critique sheet for judging whether an AI interface improves control. This is where watching becomes something you can inspect and reuse.

04

Preview

Use "Preview" as the application surface. Decide whether the idea touches a browser flow, a local file, a model choice, a source document, a UI, or a review step.

05

Feedback

Use "Feedback" to prove the lesson. The evidence should connect back to the video title, transcript anchors, and a concrete output, not a generic best-practice claim.

06

Iteration

Use "Iteration" to carry the idea forward: save the prompt, checklist, diagram, or operating rule that would make the next agent run better.

Example

Source-backed work packet

Convert the video into a scoped task that includes the transcript claim, target workflow, acceptance criteria, and proof. The output should be a ui critique sheet for judging whether an ai interface improves control..

Example

Claim vs. demo brief

Separate what the speaker claims, what the demo actually proves, and what still needs outside verification before you adopt the workflow.

Example

Teach-back module

Transform the lesson into a definition, a mechanism diagram, one misconception, one practice exercise, and a check-for-understanding question.

Do not learn it wrong
  • Treating the title as the lesson without checking what the transcript actually says.
  • Letting the prompt drift into generic advice that could apply to any video in the playlist.
  • Copying the tool setup without identifying the operating principle that transfers to your own stack.
  • Skipping the artifact, which means the learning never becomes operational or inspectable.

Transcript-derived moments

Use timestamps to study the actual video.

Quality check

Do not count this as learned until these are true.

01

State the transcript-backed claim in your own words: This video shows how the creator built a fully animated portfolio website in about 2 hours by sketching states in Figma, generating warm-toned model photos with Nano Banana/Hexfield, and prompting Claude Code section-by-section to produce a flip-to-ticker intro, project pages, and an info page.

02

Explain the practical stakes without hype: New playlist item from Ruben Stom; queued for transcript-backed review, topic mapping, and a practical learning artifact.

03

Map the idea onto the Intent -> Canvas -> Artifact -> Preview -> Feedback -> Iteration sequence and name the weakest link.

04

Produce the artifact and include the evidence that proves it: A UI critique sheet for judging whether an AI interface improves control.

Put it into practice

Give this grounded prompt to Codex or Claude after watching.

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

Source video:
- Title: Design a Fully Animated Website with AI — Claude Code
- URL: https://www.youtube.com/watch?v=47geVNoSrz4
- Topic: Interfaces + Open Design
- My current learning frame: Recreate the intro by prompting Claude Code for a flip animation that ends on the center image so it stays in place as the images spread into a left-drifting ticker row, writing the prompt as explicit opening, transition, and resting states.
- Why this matters: New playlist item from Ruben Stom; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Transcript anchors from this exact video:
- 0:51 / Evidence 1: "because I prepared a small free gift to you as a thank you for watching. Before I start prompting in Claude code, I sketched the concept for the website in Figma. And the purpose of these sketches is..."
- 1:54 / Evidence 2: "these sections. This is the last design that I created for the about me page and I inverted the colors here just to keep things interesting and clearly distinguish it from the project pages. And I went for..."
- 3:45 / Evidence 3: "uh wordmark with the flipping images and the resting state is the second section, the horizontal row with the seven projects. And then by showing and describing both states, I can create a smooth transition from the first..."
- 5:31 / Evidence 4: "please make the adjustments." And then it worked perfectly. And this is just one of those small techniques that can really improve your prompting by asking the model if it understands you before it starts making changes. Once..."
- 7:31 / Evidence 5: "prompts and the actual website files as a free download in the description, so you can use these animations in your own website designs. Thank you for watching, and I hope to see you in the next one."

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, what the demo proves, confidence, and what still needs verification.
3. Extract the actual teachable claims from the video. Do not invent claims that are not supported by the title, lesson frame, or transcript anchors.
4. Build a reusable learning artifact: A UI critique sheet for judging whether an AI interface improves control.
5. Include:
   - a plain-English definition of the core idea
   - a diagram or structured model using this sequence: Intent -> Canvas -> Artifact -> Preview -> Feedback -> Iteration
   - 3 concrete examples that apply the video idea to real agentic work
   - 2 failure modes the video helps prevent
   - a checklist I can use the next time I run Codex or Claude
   - one practical exercise with a clear done signal
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 "Design a Fully Animated Website with AI — Claude Code", not a generic Interfaces + Open Design essay.
- Prefer operational examples, failure modes, and reusable artifacts over broad definitions.
- Call out uncertainty instead of smoothing over weak evidence.
- If evidence is weak, say what transcript segment or timestamp needs review instead of guessing.
- Finish with a concise artifact I could paste into my learning app.

Misconceptions

What to stop believing.

A beautiful page is automatically a good learning tool.

Learning requires sequence, active recall, feedback, and application.

Generated UI should be accepted as-is.

Generated UI needs critique, revision, and browser verification.

Practice studio

Learning only counts when you make something.

01

Transcript evidence map

Separate what the video actually says from what you already believe about the topic.

3 source-backed takeaways with timestamps, confidence, and a transfer note.
02

One useful artifact

Apply the video to a real workflow and produce a ui critique sheet for judging whether an ai interface improves control..

A reusable artifact with a done signal and one verification step.
03

Teach-back card

Explain the lesson to someone who has not watched the video yet.

A 90-second explanation, one diagram, one example, and one misconception to avoid.

Recall check

Answer first, then reveal — without rewatching.

Ruben sketches the site in Figma before prompting, but says Claude won't replicate the sketches exactly. What specific role do the sketches play, and what takes priority?

What three-part structure does Ruben use in his prompt to get Claude to produce a smooth advanced animation, and why does that structure work?

When a result was wrong and Ruben wasn't sure of the cause, what did he do BEFORE giving Claude the fix instruction, and what benefit did that produce?

Source shelf

Use the video as a doorway, then verify with primary sources.

ReadingOpen Design Repogithub.com/open-design-dev/open-designReadingReact Docsreact.dev/