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Decode Trail Latest Questions
Debugging used to be simple. Now it’s hard. I want to understand why.
Deployments move metadata, not configuration completeness. Permission sets, licenses, feature toggles, and org-level settings are often excluded. As a result, deployed features may exist but remain inaccessible or inactive.
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 retrained my model with more recent data.The assumption was that newer data would improve performance.Instead, the new version performs worse in production.This feels counterintuitive and frustrating.
