Inject an error, then hit Run race. The same syndrome goes to four different decoding algorithms (a lookup table, minimum-weight matching, belief propagation, and research-grade BP-OSD), and each guesses a correction on its own. See where they agree, where they diverge, and which ones actually return the code to safety.
Every decoder sees only the syndrome, never the actual error. But many different errors produce the same syndrome. A decoder picks the most likely explanation (usually the lowest-weight one), and most of the time that's correct. But when the real error is large, for instance a chain spanning the whole code, its syndrome can look identical to "nothing happened," and no decoder can tell the difference using syndrome information alone. That irreducible ambiguity is exactly why a code has a finite threshold: push the error rate too high and even a perfect decoder starts guessing wrong. Try a full-column X error below and watch all three "succeed" on the syndrome while a logical error silently slips through.
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