confusable-vision takes the 1,418 TR39 confusable pairs that map a non-Latin character to a Latin target (a-z, 0-9), renders both characters across every available system font, and computes SSIM for each pairing. The output is a scored JSON artifact: one continuous similarity score per pair, per font.
What surprised me was that this entire walk is fully hardware-driven -- no microcode involvement at all. The state machine reads the page directory entry, reads the page table entry, checks permissions, and writes back the Accessed and Dirty bits, all autonomously. Since it's hardware-driven, it runs in parallel with the microcode and needs its own memory bus arbitration -- the paging unit must share the bus with both data accesses from the microcode and prefetch requests from the instruction queue.
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view = result.value; // Must reassign
一夜暴裁4000人，股价飙涨25%，联创发文：AI时代不需要那么多人了