Every training cycle is like a stopwatch for me – cut out any padding, keep the batch size just right, shuffle the data until the loss drops to the same decimal place each run. I treat the data pipeline like a pipe: tight, no leaks, constant velocity. And yeah, if the water feels flat I’ll tweak my routine. It’s not really superstition, it’s my rhythm. If your inference is lagging, tighten the batch, profile the kernel, shave a half‑microsecond off the GPU. Keep it moving, or the water gets bored.