Recreating Magazine Layouts with JavaScript

It never happened.

Safari supported it briefly. Chrome experimented with it. Then browsers killed it, citing performance concerns and implementation complexity. And we were left with the same grid-based layouts that make every modern website look nearly identical.

Meanwhile, print designers continued creating these incredible, dynamic layouts where text could snake around images, flow between scattered text boxes, and create visual hierarchy that actually guided your eye through the story. The web felt static and lifeless in comparison.

So I asked a simple question: Could we fake it with JavaScript?

The Experiment

I didn't actually know if we could do it. I'm comfortable with HTML and CSS, and I can write basic JavaScript, but building a sophisticated text-measurement and content-flow system was absolutely beyond my skill level.

But I had Claude.

The entire premise of this project was to see if AI-assisted development could lower the barrier to experimental web design. Could I conceptually understand what I wanted and have AI handle the technical complexity?

The answer turned out to be yes. But with some fascinating complications.

How It Actually Works

The core concept is deceptively simple:

1. Structure your content as semantic JSON blocks (paragraphs, headings, lists)
2. Define regions as empty HTML containers positioned anywhere on the page
3. Measure how much content fits in each region using "ghost rendering"
4. Flow content progressively through regions until everything is placed
5. Recalculate everything when the viewport changes

The devil, of course, is in the details.

You can't just count characters to know if text will fit. Different fonts, sizes, line heights, and responsive breakpoints all affect the actual space text occupies. The solution was to create invisible test containers with identical styling, render content into them, measure the pixel height, then remove them. And do it all in milliseconds.

function measureContent(block, targetRegion) {
  const testDiv = document.createElement('div');
  testDiv.className = targetRegion.className;
  testDiv.style.visibility = 'hidden';
  testDiv.style.position = 'absolute';
  testDiv.style.width = getComputedStyle(targetRegion).width;
  
  testDiv.innerHTML = renderBlock(block);
  document.body.appendChild(testDiv);
  
  const height = testDiv.scrollHeight;
  document.body.removeChild(testDiv);
  
  return height;
}
        

It sounds inefficient, but browsers are fast. The entire measurement and flow process happens in about 50-100 milliseconds, even with complex layouts. Gotta love the modern internet!

The Flow Algorithm

Once you can measure content accurately, the flow logic becomes manageable. Loop through each region, progressively add blocks until you run out of space, then move to the next region with the remaining content.

The actual implementation included dozens of edge cases—orphan protection, keeping headings with their following paragraphs, handling images and special formatting—but the fundamental concept stayed simple.

What We Built

Using this system, we created four completely different experimental layouts:

1. The Intro: Dramatic "EXPERI-MENTAL" typography with three-column responsive flow
2. Algorithm Sadness: Emotional narrative about AI and recommendation systems
3. Stoic Drama: Horizontal scrolling with Slack-style dark UI and ancient philosophy
4. How We Did It: A technical deep dive with magazine-style grid columns

Each page uses the same underlying flow system but with completely different visual treatments. The intro page has that bold, fragmented magazine aesthetic. The horizontal page feels like a physical scroll. That technical page uses a responsive CSS Grid where columns wrap to new 100vh "rows" as the viewport shrinks, just like pages in a magazine.

The AI Development Process

Here's the part that won't surprise you: I could not have built this alone.

The JavaScript measurement logic, the responsive reflow calculations, the DOM manipulation performance optimizations; these required expertise I simply don't have. But through conversation with Claude in Cursor, we:

• Conceptualized the architecture through back-and-forth discussion
• Generated complex algorithms with detailed prompts
• Debugged edge cases by describing observed behavior
• Iterated on performance through targeted refinements
• Built responsive systems I didn't fully understand at first

My process looked like this:

"I want text to flow between scattered containers, like CSS Regions would have done."

"The columns are getting too narrow on wide screens. Can we use utility classes for min/max widths?"

"The scroll snap isn't working. It's still scrolling normally."

And Claude would generate working solutions, explain the approach, and help me understand what was actually happening under the hood.

What would have been months of research and trial-and-error became hours of directed problem-solving.

What Did We Actually Learn?

1. Magazine Layouts Are Possible (But Are They Worth It?)

We proved it's technically feasible to recreate print-style layouts with JavaScript. The performance is good, the responsive behavior works, and the visual results feel genuinely different from typical websites.

But was it worth the effort? I'm gonna go with no.

For starters, it stopped making sense below roughly 900px, where it just had to revert to a traditional stacked website. This took significantly more time than building a traditional site, and it really only looks cool on really big monitors.

Secondly, the responsiveness created cascading problems we had to solve through iteration. Content authoring is more complex because of the JSON structure. And while the results might look cool, I'm not convinced they meaningfully improve the user experience.

2. AI Development Is Transformative (But Requires New Skills)

The way we work is changing forever. Complex technical challenges that would normally require specialized expertise can now be tackled through conversation and iteration with AI tools.

But this isn't "AI does everything for you." It's a different kind of skill and expertise:

• Knowing what to ask for and how to describe problems clearly
• Understanding when AI needs context and what that context should be
• Recognizing when solutions are wrong and iterating toward correctness
• Debugging AI-generated code by explaining observed behavior

My prompting skills improved drastically through this project. I learned to provide Figma links for extracting content, to specify column placement explicitly, to break complex requests into sequential steps.

3. Creative Constraints Breed Innovation (Sometimes)

Structuring everything as semantic JSON blocks actually improved our content authoring. Thinking about "regions" and "flow order" forced us to consider reading patterns more deliberately than we might have with traditional layouts.

But there's a flip side: sometimes constraints just make things harder without adding value. There were moments where I just wanted to write HTML and this system felt like fighting the platform. I can't help but think the AI struggled with it because it even it was thinking, "This doesn't make sense. Why are we doing this?"

The Honest Conclusion

Would I use this approach for a real client project? Definitely not. Unless they intentionally wanted it to be weird.

The development complexity, maintenance overhead, and edge cases don't justify the aesthetic gains for most use cases. Especially when those gains are really only noticeable above 1600px. Which rules out most site visitors. There are diminishing returns on visual distinctiveness when users just want to read your content on their phone.

But would I recommend this kind of experimental project to other developers? Absolutely.

The value wasn't in shipping a production-ready text flow system. The value was in:

• Learning how AI can accelerate experimental development
• Understanding the tradeoffs between visual creativity and practical functionality
• Pushing the boundaries of what's possible on the web
• Improving my ability to collaborate with AI tools through better prompting and iteration

This was less about "look what we built" and more about "look what we learned by building it."

Try It Yourself

The full codebase is open source on my repo. You can see all four experimental pages, read through the text flow algorithm, and adapt it for your own projects.

Will you use it? I don't know. Should you use it? That depends on whether you really enjoy being frustrated several evenings in a row.

But if you want to experiment with magazine-style layouts on the web, the code is there. And if you want to see what AI-assisted development looks like in practice, the entire conversation history is documented.

Because in the end, that might be the most interesting part of all: this entire project is a case study in human-AI collaboration. The code, the designs, the problem-solving process is all a conversation between a designer with a vision and an AI with technical implementation skills.

The web may not have gotten CSS Regions.

But with a little creativity (and a lot of JavaScript), we can fake it pretty convincingly.

Thank you for coming to my TED Talk.