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mkdir -p .openclaw_data

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Another way to look at our threshold matrix is as a kind of probability matrix. Instead of offsetting the input pixel by the value given in the threshold matrix, we can instead use the value to sample from the cumulative probability of possible candidate colours, where each colour is assigned a probability or weight . Each colour’s weight represents it’s proportional contribution to the input colour. Colours with greater weight are then more likely to be picked for a given pixel and vice-versa, such that the local average for a given region should converge to that of the original input value. We can call this the N-candidate approach to palette dithering.

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Implementation requires evaluating your existing content and identifying opportunities to add structure without forcing it artificially. Look for places where you're listing multiple items in prose that would be clearer as bullet points. Find sections comparing options that would benefit from table format. Identify processes that could be broken into numbered steps. These changes often improve content quality while making it more AI-friendly.