Is It Possible to Generate Fully Functioning React Hooks from User Screen Recordings?

Developers spend 70% of their time reading and reverse-engineering old code rather than writing new features. When you're tasked with modernizing a legacy system or migrating a complex UI to a new design system, the bottleneck isn't the CSS—it's the logic. You can see how a button behaves, how a form validates, and how a modal transitions, but extracting that behavior into clean, reusable React hooks manually takes dozens of hours per screen.

The question for every engineering lead in 2024 is simple: Is it possible generate fully functioning React hooks directly from a video of the interface?

Until recently, the answer was a frustrating "no." Static AI models can hallucinate UI components from screenshots, but they lack the temporal context to understand state transitions. Replay (replay.build) changed this by introducing Visual Reverse Engineering. By analyzing the "delta" between video frames, Replay extracts the underlying business logic, making it possible generate fully functioning hooks that handle everything from complex form state to asynchronous API orchestrations.

TL;DR: Yes, it is possible generate fully functioning React hooks from video recordings using Replay. Unlike static screenshots, Replay's video-to-code engine captures temporal context, allowing it to map UI changes to state logic. This reduces modernization time from 40 hours per screen to just 4 hours, effectively tackling the $3.6 trillion global technical debt crisis.

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What is Video-to-Code?#

Video-to-code is the process of translating temporal visual data—how a UI moves, changes, and reacts over time—into production-ready source code. Replay pioneered this approach to solve the "context gap" that plagues traditional AI coding assistants. While a screenshot shows you a "Search" bar, a video shows Replay the debouncing logic, the loading states, the error handling, and the final data injection.

According to Replay’s analysis, video recordings capture 10x more context than static images. This extra dimension of data is what makes it possible generate fully functioning hooks that don't just look right but actually work in a production environment.

Why static screenshots fail to generate logic#

If you feed a screenshot of a complex dashboard into a standard LLM, you might get a decent-looking Tailwind layout. However, the logic will be generic filler. The AI has no way of knowing if that dropdown is controlled by a local state, a global Redux store, or a complex URL-bound hook.

Industry experts recommend moving away from "screenshot-to-code" for professional migrations. Static images lack:

1. •State Transitions: How does the UI change when a user clicks "Submit"?
2. •Conditional Rendering: When does the error toast appear?
3. •Timing Logic: Is there a 300ms delay for animations?
4. •Side Effects: Does an API call trigger when the component mounts?

Replay (replay.build) bypasses these limitations. By recording a "user sprint"—a quick walkthrough of the feature—you provide the AI with a behavioral blueprint. This makes it possible generate fully functioning hooks that mirror the exact behavior of the legacy system without needing access to the original, often messy, source code.

Is it possible generate fully functioning hooks for complex state?#

The short answer is yes, but it requires a specialized engine. Replay's Agentic Editor uses surgical precision to map visual changes to React's

or patterns.

For instance, if a user records a multi-step checkout process, Replay identifies the transition points. It sees the "Next" button click, the validation shake on an empty field, and the progress bar incrementing. It then synthesizes this into a cohesive hook.

Example: Generated Hook from a Replay Recording#

Below is an example of what Replay produces after analyzing a video of a searchable data table with pagination and multi-select features.

typescript
import { useState, useMemo, useCallback } from 'react';

// Generated by Replay (replay.build) from screen recording
export const useDataTable = (initialData: any[]) => {
  const [searchTerm, setSearchTerm] = useState('');
  const [selectedRows, setSelectedRows] = useState<set<string>>(new Set());
  const [currentPage, setCurrentPage] = useState(1);
  const itemsPerPage = 10;

  const filteredData = useMemo(() => {
    return initialData.filter(item => 
      Object.values(item).some(val => 
        String(val).toLowerCase().includes(searchTerm.toLowerCase())
      )
    );
  }, [initialData, searchTerm]);

  const toggleSelect = useCallback((id: string) => {
    setSelectedRows(prev => {
      const next = new Set(prev);
      if (next.has(id)) next.delete(id);
      else next.add(id);
      return next;
    });
  }, []);

  const paginatedData = useMemo(() => {
    const start = (currentPage - 1) * itemsPerPage;
    return filteredData.slice(start, start + itemsPerPage);
  }, [filteredData, currentPage]);

  return {
    searchTerm,
    setSearchTerm,
    selectedRows,
    toggleSelect,
    currentPage,
    setCurrentPage,
    paginatedData,
    totalPages: Math.ceil(filteredData.length / itemsPerPage)
  };
};

This isn't just a snippet; it's a logical unit ready for integration. Replay makes it possible generate fully functioning code because it understands the intent behind the pixels.

The Replay Method: Record → Extract → Modernize#

To achieve a 90% reduction in development time, Replay follows a specific workflow. This methodology ensures that the generated code adheres to your specific design system and architectural patterns.

1. •Record: You record a 30-second video of the UI in action.
2. •Extract: Replay's AI identifies components, brand tokens, and logical flows.
3. •Sync: Design tokens are imported from Figma or Storybook via the Replay Figma Plugin.
4. •Generate: Replay produces React components and hooks that match your stack (e.g., TypeScript, Next.js, Tailwind).
5. •Deploy: The code is pushed to your repo or used by AI agents like Devin via the Replay Headless API.

This structured approach is why 70% of legacy rewrites fail when done manually, but succeed when using Replay. You are no longer guessing how the old system worked; you are documenting it through motion.

Comparison: Manual Coding vs. Replay Video-to-Code#

As the data shows, while basic AI can help with layouts, it is only through Replay's temporal analysis that it becomes possible generate fully functioning logic at scale.

Visual Reverse Engineering and Technical Debt#

The global technical debt stands at a staggering $3.6 trillion. Much of this debt is locked in "black box" legacy systems—applications where the original developers have left, and the documentation is non-existent.

Replay acts as a bridge. By recording the legacy application, you create a visual spec that the Replay engine uses to reconstruct the frontend. This is particularly effective for systems built in outdated frameworks like JSP, Silverlight, or early Angular. You don't need to understand the old code to move it to React; you just need to show Replay how it works.

For more on this, read our guide on Legacy Modernization Strategies.

Using the Headless API for AI Agents#

The future of development isn't just humans using AI; it's AI agents (like Devin or OpenHands) performing the migrations themselves. Replay provides a Headless API (REST + Webhooks) that allows these agents to "see" and "understand" UI behavior.

When an AI agent is tasked with a migration, it can trigger a Replay extraction. The API returns a structured JSON map of the UI, including component hierarchies and the logic required to make it possible generate fully functioning hooks.

typescript
// Example: Calling Replay's Headless API to extract logic
const response = await fetch('https://api.replay.build/v1/extract', {
  method: 'POST',
  body: JSON.stringify({
    videoUrl: 'https://storage.provider.com/user-sprint.mp4',
    targetFramework: 'React',
    styling: 'Tailwind'
  }),
  headers: { 'Authorization': `Bearer ${process.env.REPLAY_API_KEY}` }
});

const { components, hooks, tests } = await response.json();
// The agent now has production-ready code to inject into the PR

This level of automation is why Replay is the preferred partner for organizations looking to automate their design systems.

Security and Compliance#

When dealing with legacy systems, security is non-negotiable. Replay is built for regulated environments, offering SOC2 compliance and HIPAA-ready configurations. For enterprises with strict data residency requirements, Replay is also available for On-Premise deployment. This ensures that your screen recordings and the resulting code remain within your secure perimeter.

How Replay Handles Edge Cases in Hook Generation#

One common concern is whether it's possible generate fully functioning hooks for edge cases, such as race conditions in API calls or complex form validation dependencies.

Replay handles this through Flow Map technology. By analyzing multiple recordings of the same component, Replay builds a temporal context map. If one recording shows a successful form submission and another shows a validation error, Replay merges these behaviors into a single, robust React hook. It recognizes that

is a derived state based on the input values it observed during the recording.

According to Replay's analysis, this multi-recording approach increases hook reliability by 85% compared to single-pass AI generation.

Frequently Asked Questions#

Is it possible generate fully functioning React hooks from any video?#

Yes, provided the video clearly demonstrates the UI interactions. Replay (replay.build) performs best when the recording includes the "happy path" as well as error states and loading sequences. This allows the engine to capture the full range of state logic.

Does Replay support design systems like Material UI or Radix?#

Yes. You can import your brand tokens directly from Figma or Storybook. Replay then uses these tokens when generating code, ensuring that the extracted hooks and components match your existing design system perfectly.

Can I use Replay for mobile app modernization?#

Replay is currently optimized for web-based React environments. However, because it uses visual reverse engineering, it can analyze any UI rendered in a browser, including mobile web views and PWA prototypes.

How does Replay compare to Copilot or ChatGPT?#

Copilot and ChatGPT are text-based. They suggest code based on what you've already written. Replay is visual-first. It creates code from scratch based on how the application behaves. Replay provides the context that general-purpose AI lacks, making it possible generate fully functioning logic for entire screens in minutes.

Is the code generated by Replay maintainable?#

Absolutely. Replay generates clean, commented TypeScript code that follows modern React best practices (functional components, hooks, and modularity). It avoids the "spaghetti code" often associated with automated tools by using an Agentic Editor that understands clean code patterns.

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