The Code That Said 'Wait' — And How I Found the Right Fix
In the middle of building the Arise & Shine Transporters platform, I ran into a problem that left me stuck for hours. With the help of AI collaboration, I found the right path forward — and it changed how I approach debugging in real-time.
It was a Tuesday in late August, and I was deep in the process of refining the fleet tracking features of Arise & Shine Transporters. The GPS telemetry was working well, but I noticed that the OPS dashboard was freezing when users tried to save changes. At first, I assumed it was a frontend issue — a bug in the React code or a problem with the state management. But after hours of debugging and no progress, I needed a new perspective.
I turned to my AI collaborators, Claude and Codex, and described the problem clearly: the dashboard was freezing on save, and I couldn’t find the cause. Within minutes, they suggested I look at the backend API calls and the data being sent over. They pointed out that the dashboard might be trying to load a large volume of telemetry data all at once, which could be overwhelming the system.
That made sense. The backend was pulling 1.9 million rows of telemetry data, which was clearly too much for the dashboard to handle in real time. But how could I fix that? The AI tools suggested filtering the data before it reached the frontend — specifically, only fetching telemetry data for the specific truck being viewed on the dashboard. That way, the system wouldn’t have to load all the data at once.
I implemented the change, filtering the data on the backend before sending it to the frontend. Within minutes, the dashboard was responsive again — no more freezing, no more delays. It wasn’t a dramatic moment, but it was a clear reminder of the value of working with AI as a partner in the building process. It wasn’t just about finding a fix — it was about understanding the root of the problem and knowing where to look.
This experience reinforced a lesson I’ve learned repeatedly: AI tools don’t just solve problems — they help you ask the right questions. And sometimes, the answer is not a dramatic overhaul, but a small, precise adjustment that makes everything work better.
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