Stop Debugging. Start Shipping: Introducing theORQL

The ORQL

July 18, 2025 • 8 min read

It's 3 PM on a Tuesday. You've been debugging the same error for 45 minutes.

You've added console.logs. You've checked the network tab. You've inspected the DOM. You've copied the error to ChatGPT—it suggested three fixes, none worked. Now you're on Stack Overflow, reading a thread from 2019 that's almost your problem but not quite.

Sound familiar? This is the reality of modern debugging: a frustrating cycle of context switching, trial and error, and educated guessing. You switch between Chrome DevTools, VS Code, ChatGPT, Stack Overflow, and back to Chrome. Each switch costs 15-30 seconds and breaks your mental model of the problem.

Stop Coding Blind. Try it in 2 Minutes on your App

theORQL sees all: auto-repro UI/Code failures and auto-fixes so UI changes actually stick.

And when you finally ask an AI for help? It's debugging blind. It doesn't know what values your variables had, what the DOM looked like, or what network requests were pending. It's making educated guesses based on your error message and source code.

We built theORQL to change this.

The Problem: Debugging Is Broken

Developers spend 30-50% of their time debugging. That's 15-20 hours per week lost to bug hunts instead of shipping features. The cost? Trillions annually in lost productivity and poor software quality.

But here's the real problem: AI tools are debugging blind.

When you paste an error into ChatGPT or Copilot, it sees your error message and maybe some source code. It doesn't see what actually happened when your code ran. It doesn't know:

What values your variables had at the moment of failure
What the DOM looked like when the error occurred
What network requests were pending or had just completed
What the user clicked to trigger the error

So it guesses. And when the guess doesn't work, you're back to square one—adding more console.logs, switching between tools, and hoping the next AI suggestion will be the one that works.

The average debugging session involves 10-15 context switches. Chrome to VS Code. VS Code to ChatGPT. ChatGPT to Stack Overflow. Stack Overflow back to Chrome. Each switch costs 15-30 seconds and breaks your mental model of the problem.

Traditional debuggers don't help either. They're great for stepping through variables, but they can't tell you why the bug happened or suggest fixes. They're magnifying glasses in haystacks—useful for inspection, useless for resolution.

Introducing theORQL: Full-Picture Runtime Debugging in Your Editor

theORQL is a VS Code extension that changes the debugging equation by giving AI what it's been missing: full‑picture visibility into what actually happened in development and what failed in deployment.

When you launch theORQL from VS Code, it overlays your Chrome browser with an agentic chat interface and connects to your deployment platforms via webhooks. Instead of asking you to explain the error, theORQL captures it automatically from Chrome's console, network, DOM, and stack traces, and ingests deployment failures from Vercel, Netlify, and GitHub Pages with full build and runtime logs. Instead of guessing what went wrong, it analyzes the actual runtime and deployment state. Instead of making you copy patches between tools, errors from Chrome and deployments all flow into a single workflow in your editor.

The result: Debugging sessions that take 2 minutes instead of 20.

Here's what makes theORQL different:

Chrome ↔ VS Code Real-Time Sync
The only tool that bridges where errors happen (Chrome runtime and deployments) and where code lives (VS Code editor). Chrome, your deployment platforms, and VS Code stay in lockstep—view diffs in your editor, preview in the browser, and apply patches instantly. Zero context switching between DevTools and dashboards.

Agentic Chat Interface
Ask questions, explore errors, and understand root causes in plain language—all through a proxy overlay in Chrome. The AI has complete runtime context from Chrome's console, network, DOM, and stack traces, plus deployment logs when a build or deployment fails, so it explains exactly why your bug happened.

Self-Hosted Open-Source Model Layer
theORQL routes your structured runtime context only to open-source models that theORQL operates on its own infrastructure. You get AI-powered runtime debugging without ever sending your code or your clients' code to third-party AI vendors like OpenAI or Anthropic—your data stays under your control, encrypted in transit.

Complete Runtime Context
When an error occurs, theORQL captures everything: stack traces, variable values, DOM state, network requests, console history, user interactions, and deployment logs. AI isn't guessing anymore—it's analyzing evidence.

80% Faster Debugging
What used to take 20 minutes now takes 2. What used to require 10-15 context switches now happens in one flow. What used to be trial and error is now evidence-based fixes.

How theORQL Works: From Error to Fix in 7 Steps

Step 1: Install and Launch

Install the VS Code extension (no configuration needed). Launch theORQL from VS Code—it opens your app in Chrome with a proxy overlay that displays the agentic chat interface. You use Chrome/Chromium as usual. theORQL monitors console, network, and stack traces silently in the background and connects to your Vercel, Netlify, and GitHub Pages deployments via secure webhooks.

Step 2: Automatic Error Capture

When an error occurs, theORQL automatically captures complete runtime context:

Stack traces with source maps
Variable values at the moment of failure
DOM state when the error happened
Network requests (pending and completed)
Console history leading up to the error
User interactions that triggered it
Deployment logs and build output when a deployment fails

This isn't just an error message. This is complete forensic evidence of what went wrong.

Step 3: AI Analysis with Runtime Context

theORQL's agentic chat interface analyzes the error with full runtime and deployment evidence. Unlike ChatGPT, which guesses based on your error message, theORQL sees exactly what happened:

The actual variable values
The actual DOM state
The actual network responses
The actual user interaction
The actual deployment logs and environment details when a build or deploy failed

AI isn't guessing anymore. It's analyzing evidence.

Step 4: Plain Language Explanation

The analysis appears in theORQL's Chrome chat interface with a clear explanation:

"The error occurs because the API response format changed. The user object exists but has a user property instead of a data property. The fix is to change user.data.name to user.user.name."

You understand exactly what went wrong and why—no cryptic error messages, no guesswork.

Step 5: Validated Fix Generation

theORQL generates a patch with complete rationale:

What to change (exact code modification)
Why it works (explanation based on runtime evidence)
Alternatives (other approaches you could take)
Live validation (see the fix work immediately in Chrome)

You can validate the fix live—apply it in Chrome and see the result immediately, or review the diff in VS Code first since both are synced in real-time.

Step 6: One-Click Apply with Real-Time Sync

Click "Apply Patch" in Chrome or VS Code—both surfaces are 100% synchronized in real-time.

You can:

View the diff in VS Code before applying (Chrome and VS Code show the same patch)
Apply in Chrome and see the result immediately in your browser
Apply in VS Code and watch Chrome update automatically

The file is saved, your dev server reloads, and Chrome refreshes. Zero context switching. Zero copy-paste. Zero manual work.

Step 7: Verify and Learn

Your browser refreshes. The error is gone. The feature works. Total debugging time: 2 minutes.

And because theORQL explains the root cause in plain language, you learn from the fix. You understand why the bug happened and how to prevent similar issues in the future.

Why Runtime Context Changes Everything

The difference between traditional AI debugging and theORQL comes down to one thing: full runtime and deployment context.

Traditional AI Debugging (ChatGPT, Copilot)

What AI sees:

Your error message
Your source code (if you paste it)
Maybe a stack trace

AI's response:
"The error suggests that user.data is undefined. This could happen if the user object is null, the data property doesn't exist, or the API call failed. Try adding a null check."

The problem: AI is guessing based on common patterns. It doesn't know which scenario is actually happening.

theORQL (Runtime-Aware AI)

What AI sees:

Your error message
Your source code
Your stack trace
The actual runtime values of every variable
The actual DOM state when it failed
The actual network responses
The actual user interaction that triggered it
The actual deployment logs and environment details when builds or deploys fail

AI's response:
"The error occurs because the API response format changed. The user object exists but has a user property instead of a data property. The fix is to change user.data.name to user.user.name."

The difference: AI isn't guessing. It's analyzing actual evidence.

The Real Impact: Time Saved, Productivity Gained

For Individual Developers

Before theORQL:

2-3 hours per day debugging
Frequent context switching and frustration
Trial-and-error approach
Uncertainty about fixes

After theORQL:

20-30 minutes per day debugging
Smooth, focused workflow
Evidence-based approach
Confidence in fixes

Time saved: 1.5-2.5 hours per day

Productivity increased: 15-20%

Frustration reduced: Significantly

For Development Teams

A 10-developer team debugging 120 errors per week:

Before theORQL:

120 errors × 20 minutes = 2,400 minutes (40 hours/week)
Cost at $60/hour: $2,400/week = $124,800/year

After theORQL:

120 errors × 2 minutes = 240 minutes (4 hours/week)
Cost at $60/hour: $240/week = $12,480/year

Annual savings: $112,320

Time reclaimed: 36 hours per week for building features

Privacy and Security: Your Code Stays Under Your Control

Self-Hosted Open-Source Models & Team Security:

Runtime context is processed only by open-source models that theORQL runs on its own infrastructure
Your code and your clients' code are never sent to OpenAI, Anthropic, or other external AI vendors
Data is encrypted in transit; you stay aligned with your own privacy and compliance requirements
Role-based access, encrypted communication, and GitHub/Jira sync for teams

Perfect for:

Startups protecting their competitive advantage
Enterprises with strict security requirements
Regulated industries (healthcare, finance, government)
Any team that values code privacy

Get Started: Join the Beta

theORQL is currently in beta with select development teams.

Beta includes:

Full access to Chrome and VS Code extensions
Self-hosted open-source model debugging on theORQL’s own infrastructure
Deployment platform integrations for Vercel, Netlify, and GitHub Pages
Direct feedback channel with our engineering team
Priority feature requests
Free during beta period

Ready to debug in 2 minutes instead of 20?