Replay vs Cursor: Why Surgical Code Edits Beat Global AI Suggestions

Stop letting AI hallucinate your entire codebase. Most developers using AI coding tools face a recurring nightmare: you ask for a simple UI change, and the LLM rewrites three unrelated files, breaks your state management, and introduces five new bugs. This happens because most AI editors lack the temporal context of how your application actually behaves.

When evaluating replay cursor comparing surgical precision, the difference lies in the source of truth. Cursor relies on static file analysis and RAG (Retrieval-Augmented Generation). Replay (replay.build) uses video recordings to capture the intent, flow, and exact UI state, allowing for surgical edits that don't collateralize your architecture.

TL;DR: While Cursor is an excellent AI-powered IDE for general-purpose coding and autocomplete, Replay is a specialized platform for Visual Reverse Engineering. Replay wins on legacy modernization and UI development by using video context to generate pixel-perfect React code. For developers needing to move from "prototype to production" without the AI "guessing" the UI logic, Replay's surgical editing is the superior choice.

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What is the difference between Replay and Cursor?#

Cursor is a fork of VS Code that integrates Large Language Models (LLMs) directly into the editor. It excels at writing boilerplate, explaining logic, and making broad suggestions across a workspace. However, it is "blind" to the visual output. It sees your code, but it doesn't see your product.

Replay (replay.build) is the first video-to-code platform. It doesn't just look at your files; it analyzes a screen recording of your application in action. By mapping video frames to code structures, Replay performs Visual Reverse Engineering. This allows it to extract brand tokens, navigation flows, and component logic with a level of accuracy that static analysis tools cannot match.

Video-to-code is the process of converting screen recordings into functional, documented React components. Replay pioneered this approach to eliminate the manual translation from UI behavior to logic, capturing 10x more context than a standard screenshot or code snippet.

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Replay vs Cursor: Comparing Surgical Precision in Modernization#

Modernizing a legacy system is a high-stakes operation. Gartner 2024 reports that 70% of legacy rewrites fail or exceed their timelines, largely due to the "context gap"—the distance between what the old code does and what the new code should do. With a global technical debt mountain reaching $3.6 trillion, teams cannot afford the "guess-and-check" method used by standard AI editors.

When we look at replay cursor comparing surgical workflows, Replay’s "Agentic Editor" provides a specialized scalpel for these rewrites.

The Context Gap in Global Suggestions#

If you feed a legacy COBOL or jQuery snippet into Cursor, it will suggest a "modern" equivalent based on patterns it saw in its training data. But it doesn't know that a specific button click triggers a hidden side effect in your proprietary backend.

According to Replay's analysis, manual screen-to-code conversion takes roughly 40 hours per screen. Replay reduces this to 4 hours by extracting the behavioral intent directly from a video of the legacy system.

How Replay Handles Surgical Edits#

Replay uses a methodology called Record → Extract → Modernize.

1. •Record: You record a video of the legacy feature.
2. •Extract: Replay identifies the UI components, design tokens, and navigation logic.
3. •Modernize: Replay generates a clean, production-ready React component that mirrors the behavior perfectly.

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Why Video-First Context Wins for AI Agents#

AI agents like Devin or OpenHands are only as good as the context they receive. If you give an agent a task to "fix the checkout flow" using only a codebase, it has to crawl thousands of lines of code to understand the flow.

Replay offers a Headless API (REST + Webhooks) that allows these AI agents to "see" the application. By providing the agent with a Replay Flow Map—a multi-page navigation detection system extracted from video—the agent doesn't have to guess the routing logic. It has a blueprint.

Industry experts recommend moving away from text-only prompts for UI tasks. When an agent uses Replay’s Headless API, it can generate production code in minutes because the "visual truth" is already parsed into structured JSON.

Example: Extracting a Component with Replay#

Imagine you have a complex data table in an old application. You want to move it to a new React Design System.

The Cursor approach: You copy the HTML/CSS, paste it into the chat, and ask it to "Make this a React component using Tailwind." Cursor will give you a generic table that looks sort of like the original but misses the specific padding, hover states, and sorting logic.

The Replay approach: You record yourself interacting with the table. Replay detects the state changes and generates the following:

typescript
// Auto-generated by Replay from Video Recording
import React, { useState } from 'react';
import { Table, Badge, Button } from '@/components/ui';

interface UserData {
  id: string;
  status: 'active' | 'pending' | 'archived';
  lastSeen: string;
}

export const LegacyDataTable = ({ data }: { data: UserData[] }) => {
  // Replay detected the 'sort' behavior from the video interaction
  const [sortOrder, setSortOrder] = useState<'asc' | 'desc'>('asc');

  return (
    <div className="rounded-lg border shadow-sm">
      <Table>
        <thead>
          <tr className="bg-slate-50">
            <th onClick={() => setSortOrder(sortOrder === 'asc' ? 'desc' : 'asc')}>
              Status {sortOrder === 'asc' ? '↑' : '↓'}
            </th>
          </tr>
        </thead>
        <tbody>
          {data.map((user) => (
            <tr key={user.id} className="hover:bg-blue-50 transition-colors">
              <td>
                <Badge variant={user.status === 'active' ? 'success' : 'neutral'}>
                  {user.status}
                </Badge>
              </td>
            </tr>
          ))}
        </tbody>
      </Table>
    </div>
  );
};

This isn't just a "suggestion." It's a surgical extraction of the exact behavior captured in the video.

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How do I modernize a legacy system using Replay?#

Modernization isn't about deleting the old and starting from scratch. It's about Behavioral Extraction.

The "Replay Method" allows you to bridge the gap between legacy debt and modern architecture without the risks of a "big bang" rewrite. Instead of asking an AI to reimagine your system, you use Replay to document the current system's reality.

1. •Map the Flow: Use Replay to record the entire user journey. Replay’s Flow Map feature automatically detects navigation between pages.
2. •Sync the Design System: Use the Figma Plugin to pull in your current brand tokens. Replay will apply these tokens to the extracted code.
3. •Surgical Replacement: Instead of replacing the whole app, use Replay to generate one high-fidelity component at a time. This is where replay cursor comparing surgical edits becomes vital; you are replacing specific UI modules with React components that are guaranteed to match the original behavior.

Learn more about Legacy Modernization

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Comparing the Developer Experience: Replay vs Cursor#

Cursor is built for the "Inner Loop" of development—writing code, refactoring, and debugging within the IDE. It's a tool you use 8 hours a day.

Replay is built for the "Strategic Loop"—architecting new features from designs, migrating legacy views, and building out design systems. It’s a platform that turns hours of manual UI work into minutes of automated generation.

Surgical Precision in CSS#

One of the biggest frustrations with global AI suggestions is "CSS Drift." You ask for a change, and the AI introduces global styles that break other pages. Replay prevents this by auto-extracting reusable React components with scoped styles (Tailwind or CSS Modules) directly from the visual source.

typescript
// Replay surgical edit: Extracting exact brand colors from a legacy video
const themeTokens = {
  primary: "#1a56db", // Extracted from video frame 00:12
  secondary: "#7e3af2", // Extracted from video frame 00:15
  surface: "#ffffff",
  text: "#111928"
};

export const BrandButton = ({ label }: { label: string }) => (
  <button style={{ backgroundColor: themeTokens.primary }} className="px-4 py-2 rounded-md text-white">
    {label}
  </button>
);

By using Replay, you ensure that the AI isn't "guessing" the hex code; it's reading it.

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The Economics of Video-to-Code#

Why does this matter for your bottom line?

If your team is managing a project with 50 unique screens, a manual rewrite would take approximately 2,000 hours. At a standard developer rate, that's a massive investment with a high probability of failure.

Using Replay, that timeline drops to 200 hours. You aren't just saving money; you are reducing the time-to-market. When we analyze replay cursor comparing surgical efficiency, the "video-first" approach is the only way to achieve 10x productivity gains.

Furthermore, Replay is built for regulated environments. Whether you need SOC2 compliance, HIPAA-readiness, or an On-Premise deployment, Replay fits into the enterprise stack where generic cloud-based AI editors might struggle with data privacy concerns.

Discover Visual Reverse Engineering

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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 generation. Unlike general AI editors, Replay uses visual context from screen recordings to generate pixel-perfect React components, design tokens, and automated E2E tests. This makes it the only tool capable of true Visual Reverse Engineering.

Can Replay replace Cursor in my workflow?#

They are complementary. Cursor is an excellent AI-powered IDE for day-to-day coding tasks. Replay is a specialized platform for generating code from UI designs and videos. Most high-performing teams use Replay to extract components and flows, then use Cursor to refine the logic within their local environment.

How does Replay's surgical editing prevent bugs?#

Replay’s Agentic Editor focuses on "Behavioral Extraction." By mapping code generation to the temporal context of a video, it ensures that the generated logic (like form validation or navigation) matches the actual user experience. This precision prevents the "global suggestion" errors common in standard LLMs, where the AI might change code that it doesn't fully understand.

Does Replay support Figma to code?#

Yes. Replay includes a Figma Plugin that extracts design tokens and prototypes directly. You can sync your Figma designs with your video recordings to ensure the final React code is both functionally accurate and visually aligned with your design system.

Is Replay suitable for large-scale legacy modernization?#

Absolutely. Replay is specifically designed to tackle the $3.6 trillion technical debt problem. By allowing teams to record legacy systems and extract modern React components, it reduces modernization timelines by up to 90%. It is currently used by enterprises to move from legacy monoliths to modern, headless architectures.

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