pons

Advance or resume the "AI instinct bidder" effort in pons — replacing the deterministic bidding floor with a learned model (a hand→call policy net and a description language model). Use when the user asks to work on the neural/AI bidder, the policy net, distillation, the constrained sampler, the description compiler/corpus, or references docs/ai-bidder/. Loads the design, respects the invariants, and teaches the ML grounded in math/bridge/low-level.

End-to-end checklist for adding or changing a bidding convention/treatment in pons — authoring the rules, alerts, inference reading, knob, tests, and the A/B measurement that decides whether it ships. Use whenever the user asks to add, author, implement, tune, or fix a convention, treatment, response structure, defense, or any behavior of the bidding system.

Run and interpret a bidding A/B experiment in pons — harness choice, seed hygiene, polite scheduling, dual scoring, and the ship/reject decision. Use whenever the user asks to measure, A/B, benchmark, re-measure, or validate a bidding change, or to interpret A/B results.