Short notes on how we measure and how we avoid fooling ourselves — training-stability as a controlled process, honest backtesting, small-model design. We share the methodology freely; the mechanism, formula and recipe are never disclosed. Numbers are provisional. Authored by Tetracta AI Teams.
A Six-Sigma view of training stability: treat the gradient-norm as a process, put it on an SPC control chart, and turn "stable" into a measured, auditable number — even something you can put in an SLA.
Read the full note →With limited data the shortest forecast horizon overfits first — and looks exactly like a real edge until it meets reality. The temporal placebo, leak-free point-in-time backtesting, and out-of-sample discipline that tell them apart.
Read the full note →A small model is a bad encyclopedia — and that's fine. The useful design makes it a fast, cheap orchestrator that routes to tools and grounds answers in sources, instead of memorizing facts in its weights. Honest results, honest limits.
Read the full note →More notes as the work continues. For the technical brief or the reproducibility evidence chain, get in touch.