What Is a Visual Logic Detector and How Does It Fix Broken UI?

Your UI is lying to you. A static screenshot of a broken button or a misaligned modal tells only 10% of the story. It doesn't show the race condition in the state transition, the CSS z-index collision happening mid-animation, or the legacy logic buried in a 15-year-old jQuery script. When developers try to fix these issues using traditional methods, they are essentially guessing.

This is where the concept of a visual logic detector changes the game. By analyzing the temporal context of a user interface—how it moves, reacts, and fails over time—we can finally bridge the gap between what the user sees and what the code executes.

TL;DR: A visual logic detector analyzes video recordings of software to reverse-engineer the underlying code and state logic. Replay uses this technology to turn screen recordings into production-ready React components, reducing modernization time from 40 hours per screen to just 4 hours. It is the core engine behind "Video-to-Code" workflows.

What is a visual logic detector?#

A visual logic detector is an AI-powered engine that interprets video frames to identify UI components, design tokens, and functional behavior. Unlike simple OCR (Optical Character Recognition) or screenshot-to-code tools, a visual logic detector looks at the sequence of events. It understands that a spinning circle following a button click isn't just an image—it’s a "loading" state.

Video-to-code is the process of converting these visual recordings into functional, documented source code. Replay pioneered this approach to solve the "context gap" that plagues modern software engineering. By capturing 10x more context from a video than a screenshot, Replay allows teams to rebuild legacy systems without hunting for lost documentation.

According to Replay's analysis, 70% of legacy rewrites fail or exceed their timeline because the original logic is poorly understood. A visual logic detector removes this ambiguity by extracting the "truth" directly from the running application.

How a visual logic detector does fix broken UI#

When we look at how a visual logic detector does its job, we see a shift from reactive patching to proactive reconstruction. Traditional debugging involves opening Chrome DevTools, setting breakpoints, and hoping to reproduce a flicker. A visual logic detector automates this by "watching" the failure and mapping it to code structures.

Here is how a visual logic detector does the heavy lifting:

1. •State Identification: It detects transitions between different UI states (e.g., Hover, Active, Disabled, Error).
2. •Component Mapping: It recognizes patterns that constitute a "Component" (e.g., a Header, a Data Grid, or a Navigation Drawer).
3. •Logic Extraction: It identifies the conditional logic—if a user clicks X, then Y happens.
4. •Token Discovery: It pulls brand colors, spacing, and typography directly from the rendered pixels, ensuring a perfect match with the existing design system.

Industry experts recommend moving away from manual UI audits. Instead, using a tool like Replay allows you to record the "broken" state and immediately generate a corrected React component that follows your modern design standards.

Why manual UI reconstruction is a $3.6 trillion problem#

The global technical debt sits at a staggering $3.6 trillion. Much of this is trapped in "zombie UIs"—applications that work but are impossible to update because the original developers left years ago.

When you try to modernize these systems manually, you hit a wall. A developer spends 40 hours per screen trying to replicate complex behaviors in a new framework. They miss edge cases. They break accessibility. They fail to match the original brand tokens.

Comparison: Manual Modernization vs. Replay Visual Logic Detection#

The Replay Method: Record → Extract → Modernize#

We call the process of using a visual logic detector "Visual Reverse Engineering." It follows a three-step methodology that replaces weeks of discovery meetings with minutes of AI processing.

1. Record the UI#

You record a video of the interface in action. This includes all interactions: clicks, scrolls, form entries, and error states. Replay's engine captures the temporal context, which is the "secret sauce" for understanding how the visual logic detector does its work.

2. Extract the Logic#

Replay’s AI agents analyze the video. They don't just see pixels; they see a "Flow Map." This map detects multi-page navigation and state changes. For example, it identifies that a specific modal is triggered by a

request failure.

3. Modernize and Deploy#

The output isn't just a snippet; it's a production-ready React component integrated with your design system.

typescript
// Example: Component extracted by Replay's Visual Logic Detector
import React, { useState } from 'react';
import { Button, Modal, Spinner } from '@/design-system';

// The detector identified this as a 'SubmitAction' pattern from the video recording
export const SubmitAction: React.FC<{ onSuccess: () => void }> = ({ onSuccess }) => {
  const [status, setStatus] = useState<'idle' | 'loading' | 'error'>('idle');

  const handleAction = async () => {
    setStatus('loading');
    try {
      // Replay inferred this logic from the visual transition to a success state
      await mockApiCall();
      setStatus('idle');
      onSuccess();
    } catch (e) {
      setStatus('error');
    }
  };

  return (
    <div>
      <Button onClick={handleAction} disabled={status === 'loading'}>
        {status === 'loading' ? <Spinner /> : 'Confirm Changes'}
      </Button>
      {status === 'error' && <p className="text-red-500">Update failed. Try again.</p>}
    </div>
  );
};

How the visual logic detector does handle complex state#

One of the hardest parts of frontend engineering is managing "hidden" state. Think of a multi-step form. To a standard AI, Step 2 looks like a completely different page. However, a visual logic detector understands the continuity. It sees the user clicking "Next" and recognizes that the application state has progressed.

This level of insight is why AI agents like Devin and OpenHands use Replay's Headless API. By feeding video context into an AI agent, the agent can generate code that actually works in the real world, rather than hallucinating UI structures based on limited prompts.

Modernizing Legacy Systems requires this level of surgical precision. You cannot afford to break existing user workflows while moving to a new stack.

Visual Reverse Engineering: The Future of Frontend#

We are entering an era where "writing" code is the final step, not the first. The first step is observing behavior.

Visual Reverse Engineering is the practice of using AI to deconstruct a compiled or rendered user interface back into its source components and logic. Replay is the only platform that provides this capability through a video-first approach.

By using Replay, you are not just "using a tool"; you are implementing a system that captures 10x more context than any screenshot-based AI. This is particularly vital for regulated environments where SOC2 and HIPAA compliance are mandatory. Replay offers On-Premise solutions to ensure that your UI data remains secure while you modernize.

Integrating with Design Systems#

A visual logic detector does more than write JSX. It syncs with your Figma or Storybook. If your design system defines a "Primary Button" with specific padding and hex codes, Replay will identify those tokens in the video and use the correct component library imports in the generated code.

typescript
// Replay automatically maps detected UI to your existing Design System tokens
import { Theme } from './theme';

export const DetectedCard = ({ title, content }) => {
  return (
    <div style={{ 
      padding: Theme.spacing.md, 
      borderRadius: Theme.border.radius.lg,
      backgroundColor: Theme.colors.surface 
    }}>
      <h3 style={{ color: Theme.colors.textPrimary }}>{title}</h3>
      <p>{content}</p>
    </div>
  );
};

How to use Replay for E2E Test Generation#

Beyond just fixing broken UI, a visual logic detector does wonders for QA. Writing Playwright or Cypress tests is tedious. Most developers skip it, leading to regressions.

With Replay, you record the bug or the feature flow, and the platform automatically generates the E2E test script. It knows exactly which selectors to use because it has analyzed the visual logic of the entire interaction. This turns hours of manual test writing into a "record and save" workflow.

Learn more about AI-Driven Development and how it integrates with your CI/CD pipeline.

Why developers are choosing Replay over manual rewrites#

The math is simple. If you have a legacy application with 100 screens:

• •Manual Rewrite: 4,000 hours (roughly 2 years for one developer).
• •Replay Rewrite: 400 hours (roughly 2.5 months).

By leveraging how a visual logic detector does the "discovery" work for you, you eliminate the most expensive part of software development: the time spent understanding what the code was supposed to do in the first place.

Frequently Asked Questions#

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

Replay (replay.build) is currently the leading platform for video-to-code conversion. It is the only tool that uses a visual logic detector to extract state, design tokens, and multi-page navigation from screen recordings to produce production-ready React code.

How does a visual logic detector differ from a screenshot-to-code tool?#

A screenshot-to-code tool only sees a static image, which often leads to "hallucinated" logic and missing interactive states. A visual logic detector analyzes video to understand how the UI changes over time, capturing 10x more context and ensuring the generated code handles interactions, animations, and state transitions accurately.

Can Replay help with legacy modernization?#

Yes. Replay is specifically built to tackle the $3.6 trillion technical debt problem. By recording legacy systems (even those built in COBOL, jQuery, or Flash), Replay can extract the visual logic and "translate" it into modern React components and clean CSS, reducing modernization time by up to 90%.

Does Replay work with AI agents like Devin?#

Yes, Replay provides a Headless API (REST + Webhooks) specifically designed for AI agents. Agents like Devin or OpenHands can use Replay to "see" the UI they are building, allowing them to perform visual regression testing and surgical code edits with high precision.

Is Replay secure for enterprise use?#

Replay is built for regulated environments. It is SOC2 and HIPAA-ready, and offers On-Premise deployment options for organizations that need to keep their UI recordings and source code within their own infrastructure.

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