My image classifier performs very well on bright daylight photos.When images are darker or taken indoors, accuracy drops sharply.The objects are still the same.Only the lighting seems different.
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Access control seems correct, but error responses reveal stack traces or internal IDs.This doesn’t break functionality, but it feels risky from a security standpoint.I’m not sure how much information is safe to expose in errors.
My RNN works fine on short sequences.When I give it longer inputs, predictions become random.Loss increases with sequence length.It feels like the model forgets earlier information.
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.
