ByteStack Labs

It joins slm-autopsy as the second public artifact in a body of work defined by one standard. Diagnostic rigor. Reproducible methodology. Engineering that shows its work. Precision is the authority. #StateEstimation #KalmanFilter

The diagnostics are not heuristics added after the fact. NIS, NEES, innovation whiteness, divergence detection. They follow from the statistical properties the filter is guaranteed to have when it is specified correctly.

ByteStack Labs has released kalmangrad. A from-scratch implementation of the standard linear Kalman filter: a complete first-principles derivation, a NumPy-only reference implementation, and diagnostics that reveal whether a running filter is actually operating correctly. github.com/ByteStack-Labs…

The Model That Passed Every Benchmark. We built the experiment, diagnosed the root cause, and open-sourced the full pipeline. @bytestacklabs/the-model-that-passed-every-benchmark-769f5986feff" target="_blank" rel="nofollow noopener">medium.com/@bytestacklabs…

Production ML Autopsy. Three diagnostic investigations. Three failure modes. One thesis. Premature Convergence diagnosed the optimization. Comprehension Debt diagnosed the engineer. The Model That Passed Every Benchmark diagnosed the deployment. We built the experiment, broke the model, and diagnosed why.

Comprehension Debt. The gap between using a system and understanding it accumulates silently. It compounds. @bytestacklabs/comprehension-debt-0b1bd9e6230e" target="_blank" rel="nofollow noopener">medium.com/@bytestacklabs…

Premature Convergence. Why the most expensive failure mode in machine learning is also the most expensive failure mode in thinking. @bytestacklabs/premature-convergence-ea9fde1e88f2" target="_blank" rel="nofollow noopener">medium.com/@bytestacklabs…