Another way to approach dithering is to analyse the input image in order to make informed decisions about how best to perturb pixel values prior to quantisation. Error-diffusion dithering does this by sequentially taking the quantisation error for the current pixel (the difference between the input value and the quantised value) and distributing it to surrounding pixels in variable proportions according to a diffusion kernel . The result is that input pixel values are perturbed just enough to compensate for the error introduced by previous pixels.
A useful mental model here is shared state versus dedicated state. Because standard containers share the host kernel, they also share its internal data structures like the TCP/IP stack, the Virtual File System caches, and the memory allocators. A vulnerability in parsing a malformed TCP packet in the kernel affects every container on that host. Stronger isolation models push this complex state up into the sandbox, exposing only simple, low-level interfaces to the host, like raw block I/O or a handful of syscalls.
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Players can also rearrange and shuffle the board to make spotting connections easier. Additionally, each group is color-coded with yellow being the easiest, followed by green, blue, and purple. Like Wordle, you can share the results with your friends on social media.
source .env && node server.js