In single-org projects, small shortcuts often feel harmless and efficient.When the same solution must work across many orgs, those shortcuts quickly become liabilities.This transition forces a deeper level of architectural discipline.
Decode Trail Latest Questions
Tests pass, but production fails. I want to understand why
A PyTorch inference script produces different outputs on every run.The model weights are loaded from the same file and the input tensor never changes.This only happens after moving from training to deployment.There are no errors or warnings.
I am training a deep network for a regression task.The loss drops initially but then stops changing.Even after many epochs it never improves.The model is clearly underperforming.
Some business logic exists in both Flows and Apex, and conflicts occasionally arise. Records end up in unexpected states. Debugging feels difficult because execution spans multiple layers. I want to understand why this happens.
