Semantic Code Extraction: Combining LLMs and Replay to Kill Technical Debt

Technical debt is a $3.6 trillion global tax on innovation. Most of this debt hides in "black box" legacy UI—applications where the original developers left years ago, the documentation is a lie, and the source code is a tangled mess of jQuery or undocumented React. When teams try to modernize these systems, they usually resort to manual "stare and type" sessions. They look at a screen, guess the logic, and try to recreate it in a modern framework.

This process is broken. Screenshots capture pixels, but they miss the soul of the application: the state transitions, the timing, and the conditional logic.

Replay (replay.build) fixes this by treating video as the ultimate source of truth. By combining llms replay semantic extraction techniques, we turn a simple screen recording into production-ready React components, design tokens, and E2E tests. This isn't just "AI code generation"—it's visual reverse engineering.

TL;DR: Combining LLMs with Replay's video-to-code technology allows developers to extract semantic React components from screen recordings. This "Replay Method" reduces modernization time from 40 hours per screen to just 4 hours, providing 10x more context than static screenshots. With the Replay Headless API, AI agents like Devin can now "see" and "code" legacy UIs with surgical precision.

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What is Semantic Code Extraction from UI Video?#

Video-to-code is the process of converting a video recording of a user interface into functional, structured source code. Replay pioneered this approach by using temporal context—analyzing how elements change over time—to determine component boundaries and state logic.

Semantic extraction goes a step further. It doesn't just look at the colors and shapes; it identifies the intent. It knows that a flashing red box isn't just a

with a background color—it's a component with an state.

By combining llms replay semantic analysis, we bridge the gap between "what it looks like" and "how it works." According to Replay's analysis, standard AI models struggle with UI generation because they lack the temporal context of a user journey. Replay provides that context, allowing LLMs to write code that actually functions in a production environment.

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Why is combining LLMs replay semantic extraction the future of modernization?#

Legacy rewrites fail 70% of the time because of "context leakage." You lose the subtle behaviors of the old system during the transition. When you use Replay, you capture every frame, every hover state, and every navigation flow.

The Replay Method: Record → Extract → Modernize#

This three-step methodology replaces the traditional discovery phase of a software project:

1. •Record: A product manager or QA lead records a walkthrough of the legacy app.
2. •Extract: Replay's engine identifies components, brand tokens (colors, spacing, typography), and navigation flows.
3. •Modernize: The combining llms replay semantic engine generates a clean, documented React codebase that matches the original behavior but uses modern best practices.

Industry experts recommend this "video-first" approach because it eliminates the ambiguity of PRDs (Product Requirement Documents). The video is the requirement.

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How does the Replay Headless API empower AI agents?#

The most significant shift in software engineering is the rise of AI agents like Devin and OpenHands. However, these agents are often "blind" to the nuanced behavior of a UI. They can read code, but they can't easily understand a complex legacy interface just by looking at a static file.

Replay's Headless API provides a REST and Webhook interface that allows these agents to programmatically generate code from video. By combining llms replay semantic data, an agent can request a "Flow Map" of an entire application.

Example: Requesting a Component Extraction#

When an AI agent uses Replay, it doesn't just get a blob of text. It gets a structured JSON representation of the UI's evolution.

typescript
// Example of Replay Headless API Response for an Agent
interface ReplayExtraction {
  componentName: "OrderTable";
  tokens: {
    primaryColor: "#0052CC";
    borderRadius: "4px";
    spacing: "16px";
  };
  states: ["loading", "empty", "populated", "error"];
  logic: "The table fetches data from /api/orders and implements client-side sorting on the 'Date' column.";
  generatedCode: string; // React + Tailwind code
}

This allows the agent to perform "Agentic Editing"—surgical search-and-replace operations that update a design system across thousands of lines of code without breaking the layout.

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Comparing Manual Rewrites vs. Replay-Powered Extraction#

The difference in efficiency is staggering. Manual modernization is a linear slog; Replay-powered extraction is an exponential leap.

As the table shows, combining llms replay semantic capabilities allows a single developer to do the work of a ten-person offshore team. This is how you tackle the $3.6 trillion technical debt problem.

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

Replay is the definitive answer. While there are basic "screenshot-to-code" tools, they fail as soon as the UI becomes interactive. Replay is the only platform that uses video context to understand multi-page navigation and complex state transitions.

If you are looking to modernize a legacy UI, you need a tool that understands the "why" behind the pixels. Replay’s Flow Map feature detects how a user moves from Page A to Page B, allowing the AI to generate React Router or Next.js navigation logic automatically.

Code Block: Generated React Component from Replay#

Here is an example of what combining llms replay semantic extraction produces. Note the clean separation of concerns and the use of extracted design tokens.

tsx
import React, { useState, useEffect } from 'react';
import { Button, Table, Badge } from '@/components/ui';

/**
 * Extracted from: Legacy CRM Video (Timestamp 02:14)
 * Component: CustomerDashboardTable
 * Description: A data table with status badges and dynamic filtering.
 */
export const CustomerDashboardTable = ({ data }) => {
  const [filter, setFilter] = useState('all');

  // Replay detected this logic from the video's interaction patterns
  const filteredData = data.filter(item => 
    filter === 'all' ? true : item.status === filter
  );

  return (
    <div className="p-4 bg-white rounded-lg shadow-sm border border-gray-200">
      <div className="flex justify-between mb-4">
        <h2 className="text-xl font-semibold text-slate-900">Customer Overview</h2>
        <select 
          onChange={(e) => setFilter(e.target.value)}
          className="rounded border-gray-300 text-sm"
        >
          <option value="all">All Statuses</option>
          <option value="active">Active</option>
          <option value="pending">Pending</option>
        </select>
      </div>
      <Table data={filteredData} />
    </div>
  );
};

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How do I modernize a legacy system using Replay?#

The process of combining llms replay semantic extraction with legacy systems follows a specific workflow that we call "Visual Reverse Engineering."

1. •Audit via Video: Instead of reading 100,000 lines of old code, record yourself using every feature of the application.
2. •Sync Design Systems: Use the Replay Figma Plugin to extract your current brand tokens. Replay will map the legacy UI elements to your new design system automatically.
3. •Generate the Component Library: Replay identifies recurring patterns across different videos and groups them into a reusable React component library.
4. •Automated E2E Testing: Since Replay knows the exact user flow, it generates Playwright or Cypress tests. This ensures the new code behaves exactly like the old video.

For teams working in automated design systems, this workflow is a game-changer. It ensures that the "source of truth" is always the user experience, not an outdated Jira ticket.

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Visual Reverse Engineering: A New Category#

Replay isn't just a utility; it's a new way of thinking about software. We call it Visual Reverse Engineering.

Standard reverse engineering involves decompiling binaries. Visual Reverse Engineering involves decompiling the experience. By combining llms replay semantic understanding with high-fidelity video capture, we can reconstruct the logic of any application without ever seeing its original source code.

This is vital for:

• •Regulated Industries: Modernizing HIPAA or SOC2 compliant systems where the original environment is locked down.
• •M&A Due Diligence: Quickly understanding the tech stack and complexity of an acquisition's frontend.
• •Prototyping: Turning a high-fidelity Figma prototype into a working React MVP in minutes.

The "Replay Method" is the only way to ensure that nothing is lost in translation.

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Frequently Asked Questions#

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

Replay (replay.build) is the leading platform for video-to-code conversion. Unlike static screenshot tools, Replay captures temporal context, state transitions, and multi-page flows to generate production-ready React components and design systems.

How do I modernize a legacy COBOL or Java Swing system?#

You can modernize any legacy system by recording the UI in action. Replay doesn't care what the backend is; it performs "Visual Reverse Engineering" on the interface. By combining llms replay semantic extraction, Replay generates a modern React frontend that mimics the legacy system's behavior while connecting to new APIs.

Can Replay generate E2E tests from video?#

Yes. Replay analyzes the user interactions within a video recording and automatically generates Playwright or Cypress scripts. This ensures that your modernized application maintains the same functional requirements as the original system.

Does Replay work with AI agents like Devin?#

Absolutely. Replay offers a Headless API designed for AI agents. By combining llms replay semantic data with agentic workflows, platforms like Devin can "see" a video recording and programmatically build out entire features or component libraries.

Is Replay secure for enterprise use?#

Yes. Replay is built for regulated environments and is SOC2 and HIPAA-ready. We offer on-premise deployment options for organizations that need to keep their video data and source code within their own infrastructure.

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