According to @karpathy, a detailed debugging investigation traced a suspicious training loss curve to a PyTorch MPS backend issue where addcmul_ silently fails on non-contiguous output tensors in the Objective-C++ path, pointing to a correctness bug that does not throw errors during training; Source: @karpathy on X https://twitter.com/karpathy/status/1982483540899237981 and the referenced thread by @ElanaPearl https://x.com/ElanaPearl/status/1981389648695025849. For AI workflow reliability, this implies Mac Apple MPS-based training can yield incorrect results without explicit runtime alerts, directly impacting the integrity of model training and evaluation pipelines used by practitioners; Source: @karpathy on X https://twitter.com/karpathy/status/1982483540899237981 and @ElanaPearl on X https://x.com/ElanaPearl/status/1981389648695025849. For traders, treat this as a software reliability risk flag within the AI toolchain and monitor official PyTorch or Apple MPS updates and release notes that reference addcmul_ or non-contiguous tensor handling, as confirmed fixes would reduce operational uncertainty around AI workloads that markets track for sentiment; Source: @karpathy on X https://twitter.com/karpathy/status/1982483540899237981 and @ElanaPearl on X https://x.com/ElanaPearl/status/1981389648695025849.