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For Pika, it helps to think in terms of inputs, constraints, verification, and iteration. One important area is quality versus speed. Define what a good result looks like before changing tools or settings, then test one variable at a time so you can tell what actually improved the outcome...

The point about workstations and understanding is interesting. In practice I would keep the first test simple, record what worked, and only add extra complexity if it solves a specific limitation. That should make it easier to tell whether the change is actually helping.

When working with DeepSeek Models, the biggest gains usually come from treating it as a repeatable workflow rather than a one-off prompt or experiment. One important area is cost control. Define what a good result looks like before changing tools or settings, then test one variable at a time so...