Why I Built Causly: The Debugging Pain That Started It All

Every founder has that moment—the problem so painful, so persistent, that you realize you have to build the solution yourself. For me, it was the soul-crushing cycle of vague bug reports.

This is the story of why I built Causly.

The Pain That Wouldn't Go Away

Before Causly, I was building software products and experiencing the same frustration over and over:

A customer would report: "It's broken."

What followed was predictable:

• 3-5 emails asking for details
• Days of waiting for responses
• Hours trying to reproduce something I didn't understand
• Finally fixing a bug that took 20 minutes once I understood it

The actual fix was never the hard part. Understanding the problem was.

A Typical Week From Hell

Let me paint the picture of what my weeks looked like:

Monday: Customer reports "checkout doesn't work." I ask for more details. Radio silence.

Tuesday: Same customer responds "I was just trying to buy something." I try every checkout flow I can think of. Everything works.

Wednesday: I ask for a screen recording. They send a 6-minute video. I scrub through it, trying to find the 10 seconds that matter.

Thursday: I finally spot it—a race condition that only happens when you click the button twice quickly. Fix takes 15 minutes.

Friday: Three more vague bug reports land in my inbox. The cycle repeats.

I calculated it once: I was spending 25-30 hours per week on bug triage. Not fixing bugs—just trying to understand them.

The Moment Everything Clicked

The breaking point wasn't dramatic. It was a Tuesday afternoon, and I was watching yet another screen recording from a user.

Eight minutes of video. I was scrubbing through it, trying to find the moment something went wrong. Pause. Rewind. Play at 0.5x speed. Squint at the screen.

Then it hit me: I was doing the exact same thing AI vision models do, just slower and worse.

I was:

• Watching frames sequentially
• Looking for anomalies (error messages, unexpected behavior)
• Trying to understand user intent from their actions
• Connecting what I saw to what I knew about the code

This is literally what computer vision is built for. Why was I—a human—doing this manually?

I opened a new file and started writing the first prototype that night.

The Realization

I started thinking about what I actually needed:

1. Instant understanding of what the user experienced
2. Automatic detection of what went wrong
3. Direct connection to the relevant code
4. No back-and-forth with customers

Screen recordings had the information I needed—but watching them was slow and tedious. What if AI could watch them for me?

Building the First Prototype

The first version of Causly was ugly. I built it for myself, to solve my own problem.

It could:

• Take a screen recording
• Extract key frames
• Use AI vision to understand what happened
• Search my codebase for relevant files
• Suggest where the bug might be

The first time it worked, I uploaded a customer's video and got back:

Issue: Form submission fails silently

Observed: User fills form, clicks submit, nothing happens
Error detected: Network request returns 422 
UI shows: No error message displayed to user

Likely files:
- src/components/ContactForm.tsx (form handler)
- src/lib/api/contact.ts (API call)

Suggested issue: Error response not being caught 
and displayed to user

I looked at ContactForm.tsx. Line 47. The catch block was empty.

// The bug
} catch (error) {
  // TODO: handle error
}

Ninety seconds from video upload to finding the bug. What used to take days.

From Side Project to Product

I kept using my prototype. Then I showed it to a few developer friends who had the same problem. Their reaction:

"Wait, it actually works? Can I use this?"

That's when I realized this wasn't just my problem. Every developer who handles customer bug reports deals with this. Every support team wastes hours on vague reports. Every customer gets frustrated waiting for fixes.

I decided to build Causly as a real product.

What Causly Does Today

Causly has evolved significantly from that first prototype:

Video Analysis: Upload a screen recording and get instant understanding of what happened—user actions, errors, frustration signals (like rage clicks).

Codebase Connection: Causly indexes your code and finds the exact files relevant to each issue. No more grep-ing through thousands of files.

AI-Powered Fixes: Using Claude, Causly analyzes the matched code in context and suggests specific fixes with explanations.

One-Click PRs: When you're ready, create a GitHub pull request directly from Causly.

Slack Integration: Analyze videos without leaving your workflow—just react with an emoji.

The Irony

Here's the funny thing: I now use Causly to debug Causly.

When a user reports an issue with our platform, I analyze their video with our own tool. It's the most satisfying form of dogfooding—the product that solves debugging problems helps me debug itself.

For Fellow Founders

If you're a developer or founder dealing with customer bug reports, you know this pain. The endless back-and-forth. The hours lost to reproduction. The customers who churn because fixes take too long.

I built Causly because I couldn't find a tool that solved this problem. Now it exists, and I want it to save you the same frustration it saves me.

The time you spend understanding bugs is time you're not spending building features. Let AI handle the detective work so you can focus on what you do best.

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Ready to escape the bug report treadmill? Try Causly free and see how much time you can reclaim.