The library.

We put a seven-level reasoning-effort knob on a local AI gateway, made graded thinking budgets enforceable on llama.cpp-served models, and swept the curve on two models plus a judge-selection leg. A named level turns out to be a bet about a question's natural deliberation length: it only pays below that length, its meaning shifts between models, and small budgets are riskier than none at all.

Months of production serving, training, and tooling on a DGX Spark, distilled into the traps that caused real incidents: a unified memory pool where standard tools misreport, page cache that can kill model loads, and bandwidth — not capacity — as the true ceiling. Written for the code agents operating the box, with measured numbers over spec-sheet claims and a symptom→cause→fix table for when something is already on fire.

An audit of the canonicalize-then-generate recipe for neural-network weight generation: what quotienting permutation and scale symmetries actually buys under a memorization-guarded, novel-and-performant criterion — measured on three model-zoo rigs, with a division of labor between learned flows and canonicalized soups, and velocity extinction, a collapse law for small-zoo flow matching.

The Orb's open foundations, the coordination of a hive of agents, and the correlated-failure trap that undermines naive fleets.