Fixing a Vibe-Coded App with Tetrix

Building apps with AI feels effortless — until you deploy them.

This is the story of how a vibe-coded application that worked perfectly on localhost completely broke on AWS, throwing endless 503 Service Unavailable errors — and how Tetrix, a context-aware AI, helped fix what traditional debugging and generic AI tools couldn’t.

If you’ve ever deployed an app using Terraform, struggled with AWS health checks, or faced mysterious cloud failures, this story will feel very familiar.

The project started with a simple concept:
use AI to automatically find profitable Amazon arbitrage deals.

Instead of manually browsing listings and calculating margins, the app — called Arbitrage AI — was designed to let AI identify good opportunities automatically.

Development went smoothly:

Everything felt ready for production.

That confidence lasted right until deployment.

For cloud deployment, Amazon Web Services (AWS) was chosen. To manage infrastructure cleanly, Terraform was used as the Infrastructure as Code (IaC) tool.

Using a vibe coding workflow:

And then… everything failed.

Immediately after deployment:

The confusing part?
The same code worked perfectly on localhost.

Initial debugging relied on the same AI used for vibe coding. It helped with syntax and configuration guesses — but the real issue wasn’t just code. It was spread across networking, health checks, and infrastructure state.

That’s when I switched tools.

Unlike general-purpose coding assistants, Tetrix works with full infrastructure context.

Tetrix had visibility into:

Instead of guessing, Tetrix could analyze how the system actually behaved in production.

And the results were immediate.

Tetrix identified three critical issues that together caused the deployment failure.

Health checks were misconfigured, causing AWS to mark the service as unhealthy — even when parts of it were running.

Fix: Health check paths and settings were corrected.

Security groups or network rules were blocking communication between application components.

Fix: Cloud rules were updated to allow proper inter-service traffic.

Some AWS settings had been manually changed outside Terraform, causing infrastructure drift. Terraform believed one thing; AWS was doing another.

Fix: Terraform state was resynced with the actual AWS resources.

This is a silent but extremely common cause of failed deployments — and very hard to detect without full visibility.

After applying Tetrix’s recommendations:

What looked like a random AWS failure turned out to be a multi-layer infrastructure misconfiguration — and Tetrix pinpointed it quickly.

This experience reinforced some important truths:

Coding-only AI struggles when issues span infrastructure, networking, and cloud services.

Manual changes outside Terraform can silently break production systems.

They often involve:

AI that understands code + logs + infrastructure can reduce debugging time dramatically.

Vibe coding can help you build fast — but production environments demand precision.

Fixing a vibe-coded app with Tetrix showed how powerful AI becomes when it’s given real-world context. Instead of just fixing errors, it explained why the system failed and how to fix it properly.

If you’re deploying applications on AWS — especially with Terraform — context-aware AI tools like Tetrix can save hours of frustration and turn cloud chaos into clarity.

Watch the full video walkthrough:
https://www.youtube.com/watch?v=BG_AiOIMBR0

Tetrix connects code, infrastructure, and operations to your AI, enabling it to reason across your full software system. Gain system-aware intelligence for faster debugging, smarter automation, and proactive reliability.

👉 Sign up or book a live demo to see Tetrix in action.