theORQL

Product Introduction

1. Definition: theORQL is a vision-enabled frontend AI debugging tool that integrates with development environments (VS Code, Cursor) to automate UI issue resolution. It captures runtime data (DOM, CSS, state, network) and visual evidence (screenshots) to map UI elements to source code.
2. Core Value Proposition: It eliminates blind frontend debugging by reproducing failures in real browsers, auto-generating verified fixes, and reducing UI regressions—ensuring changes work correctly before deployment.

Main Features

1. Vision-Enabled Coding + Debugging:

   • How it works: Captures DOM structure, computed CSS, network activity, and screenshots during runtime. Uses multimodal AI (image understanding + code analysis) to correlate visual UI issues with source code.
   • Technologies: Chrome DevTools Protocol for runtime data extraction, computer vision for screenshot analysis, and transformer-based AI models for code mapping.

2. Auto Repro → Fix Loop:

   • How it works: Automatically scripts user interactions (clicks, inputs) to replicate bugs in Chrome. Injects targeted JavaScript patches, reruns tests, and iterates until the repro passes—outputting a reviewable diff.
   • Technologies: Headless Chrome automation, runtime error tracing, and hypothesis-driven AI agents.

3. Runtime Evidence Capture:

   • How it works: Snapshots console logs, network requests, local variables, and UI state at failure points. Organizes data in VS Code for contextual debugging without DevTools switching.
   • Technologies: Chrome debugger API, state serialization, and real-time editor-browser synchronization.

Problems Solved

1. Pain Point: "Debugging black holes" where UI bugs (e.g., silent state failures, CSS flakiness) evade traditional code inspection due to missing runtime context.
2. Target Audience:
   • Frontend developers (React/Next.js teams)
   • Freelancers handling complex UI clients
   • QA engineers verifying visual regressions
3. Use Cases:
   • Reproducing production-only browser crashes locally
   • Fixing layout shifts caused by dynamic CSS
   • Resolving async state mismatches in data-heavy apps

Unique Advantages

1. Differentiation vs. Competitors: Unlike text-only AI (Copilot/Cursor), theORQL uses visual runtime evidence to verify fixes in-browser—avoiding "compiles but breaks in prod" issues. Outperforms manual DevTools debugging with automated repro/fix cycles.
2. Key Innovation: The agentic layer that drives Chrome to test hypotheses, validate fixes via screenshots/DOM diffing, and loop until resolution—collapsing the "tweak → refresh → test" workflow.

Frequently Asked Questions (FAQ)

1. Does theORQL work with React or Vue?
   Yes, it supports modern JavaScript frameworks (React, Next.js, Vue) running in Chromium browsers, capturing component state and DOM hydration issues.

2. How does theORQL handle privacy with screenshots?
   Screenshots are processed locally; no UI data leaves your machine. Runtime evidence is used solely for in-editor debugging.

3. Can theORQL debug API/network errors?
   Yes, it captures failed network requests, response payloads, and CORS errors during repro cycles, linking them to frontend code.

4. Is there a performance overhead during debugging?
   Minimal impact—theORQL only activates during explicit debug sessions, avoiding constant monitoring. Runtime data capture is optimized for speed.

5. Does it replace end-to-end testing tools?
   No, it complements them by automating root-cause analysis for failures caught in CI/CD pipelines, reducing triage time.