ai-bidder

name

ai-bidder

description

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.

AI instinct bidder — operating manual

The full design lives in docs/ai-bidder/. Read README.md first (the dream, the decisions, the ML glossary), then the part relevant to the task. plan.md is the milestone map and the source of truth for "what's next".

Standing decisions (do not re-litigate without the user)

• Distill, then search — clone american() first (M1), beat it with constrained-sampling + double-dummy search second (M3).
• Search runs at inference time too — not only as an offline teacher. M2.3 is a gated, slow "thinking" bidder (retired with the M1–M3 line) that simulates before it bids; bidding only (no cardplay engine). Distillation (M3) stays the path to the fast (distilled) floor; the live search bidder is an added gated option and the teacher whose EVs M3 distills. (instinct() stays the baseline; both learned floors are added options, never a removal.)
• Both LM roles, sequenced — authoring compiler (M4) before runtime meaning-encoder (M5).
• Offline train, distill to Rust — training is off-crate; only a lean weights artifact + a hand-rolled forward pass ship, feature-gated. pons stays dependency-light.

How to work here

1. Find the next unblocked chunk in plan.md (start at M0; respect the deps). Confirm with the user before starting a milestone — the docs are a map, not a green light.
2. Honor the invariants in 01-foundations.md §0. The learned floor is wrapped in a deterministic legality + forced-situation shell; the instinct test suite is the rails and must stay green. The net is trusted for judgement, never for the forced rails.
3. Measure on the existing harness. Every milestone's win condition is IMPs/board on the A/B duplicate match (examples/ab-instinct-floor is the template), not training loss. Follow docs/measurement.md (protocol, scorers, decision table). Report board counts; treat sub-0.1 IMPs/board as noise unless the sample is large.
4. Keep the baseline. instinct() stays as default and comparison anchor; a neural floor is an added option (measured against american_instinct()), never a removal.
5. Version artifacts together — weights, feature-spec version, teacher/system version, data seed, git SHA. A model is meaningless without its exact feature extractor.

Laziest thing that works (ponytail)

This effort is where over-engineering breeds — speculative abstractions, heavy ML runtimes, training infra, a node authored per artificial bid. Stop at the first rung that holds:

1. Does it need to exist? YAGNI. Smarten the keyless instinct floor (forced-to-game, transfer-completion) and floor the book; do not author a node per artificial bid. A milestone the user hasn't chosen is not work yet.
2. Reuse the seam. The output is already an ML interface — Logits, Classifier/System, Inferences, Rules::explain. Wire to those before writing new machinery.
3. Inference is arithmetic. A forward pass is a few matmuls + one nonlinearity, hand-rolled in Rust. Training (autodiff) stays off-crate; never a heavy ML dep in the default build.
4. Shortest diff that moves IMPs/board. Deletion over addition. Floor the book, don't replace it; instinct() is never removed.
5. Mark a deliberate shortcut with a // ponytail: comment naming the ceiling and the upgrade path.

Teaching stance (important)

The user is expert in bridge, math, and low-level programming, and is learning ML. When introducing any ML idea, ground it in those: a net is a parameterized function fit by gradient descent; inference is matmuls + an elementwise nonlinearity (hand-writable in Rust); softmax/logits are already in the codebase; distillation is matching a teacher's distribution (cross-entropy); embeddings are encode_call-style lookups with learned vectors. Prefer the math/systems framing over ML jargon. The glossary in README.md is the reference; extend it when a new term comes up.

Divide and delegate

Mechanical chunks (data export, feature plumbing, harness variants, verification harnesses) suit focused subagents. Keep architecture, the safety shell, ML target design, and integration decisions in the main loop.

Guardrail

The ponytail ladder, made non-negotiable: do not add a heavy ML runtime to the crate's default build, and do not write crate ML code before its milestone is explicitly chosen. Design docs may contain illustrative sketches; the crate stays untouched until a milestone starts.