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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.

LLMs used tactical nuclear weapons in 95% of AI war games, launched strategic strikes three times

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This doesn't mean stuffing your content with random numbers. It means grounding your claims in specific, verifiable data wherever possible. Instead of writing "Our tool is widely used," you'd write "Our tool has 150,000 monthly active users with a 4.7 out of 5 satisfaction rating based on 3,200 reviews." The specificity signals credibility to AI models, which learned during training that precise data indicates reliable sources.

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