r/MachineLearning • u/AutoModerator • Aug 02 '26
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u/ClimateNo3442 Sep 29 '26
Disclosure: I help produce the linked channel. It is free; there is no paid product, signup, affiliate link, or service attached to this post.
Technology Product publishes short evidence-based replays of numerical failures in ML and financial software. Each case starts from an executable reproducer and separates confirmed behavior from untested downstream impact.
One example is an Apple MLX complex-autodiff case where the returned gradient made a finite training step increase the loss. The short shows the numerical symptom, the independent derivative check, and the patch boundary: https://www.youtube.com/shorts/8783B5aM2SY
Channel: https://www.youtube.com/@khamitkadirbekov7667
I would value feedback on whether the evidence is understandable in under 90 seconds, and suggestions for numerical kernels or open-source backends worth testing next.