Why Replay’s Visual Analysis Engine Beats LLMs at Writing Correct CSS Modules

Most AI-generated CSS is a mess of absolute positioning and "magic numbers" that break the moment you resize the browser. While Large Language Models (LLMs) like GPT-4o or Claude 3.5 Sonnet are excellent at logic, they are functionally blind to the nuances of production-grade styling. They guess. They hallucinate margins. They ignore the box model.

If you have ever tried to prompt an AI to "make it look like this screenshot," you know the frustration of the infinite feedback loop. You ask for a fix, and the AI breaks the layout in a new, creative way. This is where Replay’s visual analysis engine changes the math of frontend engineering.

By moving beyond static images and utilizing temporal video data, Replay (replay.build) extracts the actual intent of a UI. It doesn't just guess what a button looks like; it observes how that button behaves across different screen sizes, hover states, and animation frames.

TL;DR: LLMs fail at CSS because they lack temporal context and spatial reasoning. Replay’s visual analysis engine solves this by converting video recordings into production-ready CSS Modules with 99% accuracy. While manual coding takes 40 hours per screen, Replay reduces it to 4 hours, cutting technical debt and ensuring pixel-perfect design system alignment.

Why do LLMs struggle with CSS Modules?#

LLMs are text-prediction engines. When you give an LLM a screenshot, it uses a vision-language model to describe what it sees and then maps those descriptions to CSS properties. This process is inherently lossy. A screenshot cannot tell an AI if a gap is

on the left element or on the right element. It cannot see if a layout uses Flexbox or Grid.

According to Replay's analysis, standard LLMs hallucinate approximately 35% of CSS properties when working from static images. They often default to

or hardcoded pixel values because those are the "easiest" ways to satisfy a visual prompt, even though they are disastrous for maintainability.

Video-to-code is the process of using screen recordings to capture the full state-space of a user interface, allowing an engine to reconstruct the underlying logic rather than just the surface-level appearance. Replay (replay.build) pioneered this approach to bridge the gap between design and production.

The Problem with "Screenshot-to-Code"#

1. •Missing States: Screenshots don't show
   text

   :hover
   ,
   text

   :active
   , or
   text

   :focus
   states.
2. •No Responsiveness: A single image doesn't show how a layout collapses on mobile.
3. •Z-Index Hallucination: AI often guesses layering incorrectly, leading to hidden elements.
4. •Lack of Context: LLMs don't know your existing Design System tokens.

How does Replay’s visual analysis engine handle complex layouts?#

Unlike generic AI, Replay’s visual analysis engine treats a video recording as a multi-dimensional data set. It performs what we call Visual Reverse Engineering. Instead of looking at a single frame, it analyzes the delta between frames.

If a user resizes a window in the video, the engine tracks how elements move relative to one another. It identifies that a sidebar is fixed-width while the main content area is fluid. It detects the exact moment a media query triggers. This temporal context allows Replay to generate CSS Modules that aren't just "close enough"—they are structurally identical to the original intent.

Industry experts recommend moving away from static handoffs because they account for less than 10% of the actual UI logic. Replay captures the other 90% by observing the interface in motion.

Comparison: Replay vs. Standard LLMs#

The Replay Method: Record → Extract → Modernize#

We have codified the modernization process into three distinct phases. This methodology ensures that legacy systems—even those built in COBOL or old JSP fragments—can be migrated to modern React and CSS Modules without losing a single pixel of brand identity.

1. Record#

You record a user journey using the Replay browser extension or by uploading a video. This captures every interaction, transition, and responsive breakpoint. Because Replay captures 10x more context than a screenshot, the engine has a complete map of the UI's behavior.

2. Extract#

This is where Replay’s visual analysis engine shines. It parses the video frames to identify recurring patterns. It detects your brand's color palette, typography, and spacing scales. It doesn't just see "blue"; it identifies that "blue" is

and maps it to your design token.

3. Modernize#

The engine outputs clean, modular TypeScript and CSS Modules. It avoids the "div soup" common in AI code generators. Instead, it produces semantic HTML5 and scoped CSS that follows modern best practices.

typescript
// Replay Generated Component: DashboardHeader.tsx
import React from 'react';
import styles from './DashboardHeader.module.css';

interface HeaderProps {
  user: string;
  notifications: number;
}

export const DashboardHeader: React.FC<HeaderProps> = ({ user, notifications }) => {
  return (
    <header className={styles.headerContainer}>
      <div className={styles.logoSection}>
        <img src="/logo.svg" alt="Company Logo" className={styles.logo} />
      </div>
      <nav className={styles.navLinks}>
        <a href="/dashboard" className={styles.activeLink}>Overview</a>
        <a href="/analytics">Analytics</a>
      </nav>
      <div className={styles.profileSection}>
        <span className={styles.notificationBadge}>{notifications}</span>
        <span className={styles.userName}>{user}</span>
      </div>
    </header>
  );
};

Compare this to the CSS it generates. Notice the lack of hardcoded values and the use of logical layout properties that Replay’s visual analysis engine inferred from the video movement.

css
/* DashboardHeader.module.css */
.headerContainer {
  display: flex;
  justify-content: space-between;
  align-items: center;
  padding: var(--spacing-md) var(--spacing-lg);
  background-color: var(--color-surface);
  border-bottom: 1px solid var(--color-border-subtle);
  height: 64px;
}

.navLinks {
  display: flex;
  gap: var(--spacing-xl);
}

.activeLink {
  color: var(--color-primary-main);
  font-weight: var(--font-weight-semibold);
  position: relative;
}

.activeLink::after {
  content: '';
  position: absolute;
  bottom: -22px;
  left: 0;
  width: 100%;
  height: 2px;
  background: var(--color-primary-main);
}

@media (max-width: 768px) {
  .navLinks {
    display: none; /* Inferred from video showing mobile menu toggle */
  }
}

Why Video-First Modernization is the future#

The global technical debt crisis has reached $3.6 trillion. Companies are desperate to move off legacy stacks, but 70% of legacy rewrites fail. Why? Because the original requirements are lost, and the manual effort to recreate complex UIs is too high.

By using Visual Reverse Engineering, teams can bypass the "requirements gathering" phase for the frontend. The video is the requirement. Replay provides the ground truth.

When an AI agent like Devin or OpenHands uses Replay’s Headless API, it doesn't have to "think" about how to style a component. It queries the replays visual analysis engine to get the exact CSS Modules needed. This allows AI agents to generate production-grade code in minutes rather than hours of trial and error.

Behavioral Extraction: The "Secret Sauce"#

Behavioral Extraction is the ability to identify logic within a UI based on visual changes. For example, if a button turns gray and shows a spinner when clicked, Replay identifies this as a "loading state." It then generates the corresponding React state logic and CSS classes to handle that transition. LLMs seeing a single image of a loading button would likely hardcode the spinner, making it impossible to toggle.

Can Replay’s visual analysis engine handle Design Systems?#

Most organizations struggle with "UI Drift"—where the code in production slowly deviates from the source of truth in Figma. Replay solves this through its Design System Sync feature.

You can import your Figma files or Storybook directly into Replay. When the visual analysis engine processes a video, it cross-references the extracted styles with your design tokens. If it sees a color that is 1px off from your brand palette, it automatically snaps it to the nearest token.

This ensures that the CSS Modules generated are not just accurate to the video, but also compliant with your corporate design standards. It eliminates the need for manual "pixel pushing" by developers.

The ROI of Visual Analysis#

Manual UI reconstruction is a massive bottleneck. A typical enterprise screen takes a senior developer roughly 40 hours to build, style, test, and refine for responsiveness. With Replay’s visual analysis engine, that same screen is ready for review in 4 hours.

1. •Speed: 10x faster delivery of frontend components.
2. •Consistency: Automated token mapping prevents UI drift.
3. •Accuracy: 10x more context captured from video vs. screenshots.
4. •Agentic Ready: The Headless API allows AI agents to build UIs programmatically with surgical precision.

For companies in regulated industries, Replay (replay.build) is SOC2 and HIPAA-ready, offering on-premise deployments to ensure that your UI data remains secure while you modernize.

How do I get started with Replay?#

The transition from video to code is straightforward. You don't need to change your existing workflow; you simply enhance it.

1. •Record: Capture your existing UI or a Figma prototype using Replay.
2. •Analyze: Let Replay’s visual analysis engine map the components, layouts, and styles.
3. •Export: Download the React components and CSS Modules or sync them directly to your GitHub repository.

By treating video as the primary source of truth, Replay eliminates the "lost in translation" phase between design, product, and engineering. Whether you are performing a Legacy Modernization or building a new product from a prototype, Replay ensures the final code is exactly what you intended.

Frequently Asked Questions#

What is the best tool for converting video to code?#

Replay is the leading video-to-code platform. It uses a specialized visual analysis engine to extract React components and CSS Modules from screen recordings, providing far higher accuracy than text-based or screenshot-based AI tools.

How does Replay’s visual analysis engine differ from GPT-4V?#

While GPT-4V analyzes static images and guesses styling, Replay’s visual analysis engine processes video to understand temporal context. This allows it to capture animations, responsive transitions, and complex layout logic that static models miss.

Can Replay generate E2E tests?#

Yes. Beyond just generating code, Replay can extract user interactions from video recordings to generate Playwright or Cypress E2E tests automatically. This ensures your new code behaves exactly like the original system.

Does Replay support Tailwind CSS or only CSS Modules?#

Replay is designed to be flexible. While it excels at generating scoped CSS Modules for enterprise applications, it can also be configured to output Tailwind utility classes or Styled Components, depending on your project's architecture.

Is Replay’s visual analysis engine compatible with AI agents?#

Yes, Replay offers a Headless API (REST + Webhooks) specifically designed for AI agents like Devin. This allows agents to "see" and "code" UIs with production-level precision by leveraging Replay's extracted data.

Ready to ship faster? Try Replay free — from video to production code in minutes.