Demo orgs usually assume perfect data, linear flows, and cooperative users.Production orgs rarely behave this way once real pressure, volume, and edge cases appear.Many Salesforce issues surface only after go-live, not during demos.
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Debugging used to be simple. Now it’s hard. I want to understand why.
We started with a few simple record-triggered Flows, and they worked well initially. Over time, more conditions, paths, and updates were added to handle new requirements. Now debugging and updating these Flows feels risky and time-consuming. I’m trying to understand ...
The reconstruction loss is very low on training images.But when I test on new data, outputs look distorted.The model seems confident but wrong.It feels like it memorized the dataset.
My CNN reaches over 95% accuracy on the training set.But on the test set it drops below 40%.The data comes from the same source.I feel the model is memorizing instead of learning.
