When reviewing tracking data, look for patterns rather than obsessing over individual fluctuations. Is your visibility generally improving, declining, or stable? Which topics show stronger AI citation rates? Where are competitors consistently appearing instead of you? What queries used to show your content but no longer do? These patterns inform where to focus future optimization efforts and what's working well versus what needs adjustment.
Due to this more measured approach, error-diffusion dithering is even better at preserving details and can produce a more organic looking final image. However, the algorithm itself is inherently serial and not easily parallelised. Additionally, the propagation of error can cause small discrepancies in one part of the image to cascade into other distant areas. This is very obvious during animation, where pixels will appear to jitter between frames. It also makes files harder to compress.
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