Today we introduce a demonstrable advance that unifies okrummy, rummy, and aviator under a single, verifiably fair, skill-centric engine. Instead of isolated games with opaque randomness and generic matchmaking, the new stack brings auditable randomness, explainable coaching, cross‑game ratings, and built‑in safety. Every claim is backed by artifacts players and regulators can test in real time: public seed commitments, open telemetry, reproducible replays, and third‑party validators.

At the core is a provably fair randomness layer that works across card draws in rummy and flight multipliers in aviator. Before each round, clients and server co‑create a seed using player gestures and system entropy, publish a hash commitment, and lock it on a public timeline. After resolution, the engine reveals the seeds and a compact SHA‑3 transcript that anyone can recompute. A lightweight verifier lets players press “verify round,” instantly confirming the deck order or multiplier without trusting the host.

Skill is modeled as a vector, not a single number. Our rating engine decomposes performance into sequencing, probability estimation, memory, and risk timing, using a Bayesian factor model trained only on public outcomes. Because the factors are shared, progress in okrummy’s objective‑driven patterns improves rummy meld planning, and vice versa. A live benchmark set—open games with fixed seeds—makes gains demonstrable: players can reproduce their rating changes offline, compare against baseline bots, and verify that matchmaking respects confidence, not just win rate.

Explainable coaching is built in, but never prescriptive. On‑device models run hand evaluations and surface counterfactuals like, “If you had held the nine of hearts, your meld likelihood next turn rises from 31% to 47%.” In aviator, the overlay quantifies variance and session exposure rather than suggesting bets, highlighting how a proposed cash‑out changes expected loss within a user‑defined budget. In okrummy, players set OKR‑style goals—reduce deadwood by 15%—and the coach measures progress with transparent, testable metrics and reproducible drills.

Integrity scales through transparent detection, not surveillance. The system models table dynamics as a graph and flags improbable information flows, then invites participants to run an in‑client audit that anonymizes hands while preserving proofs. Outcomes are resolved with a community reviewer pool and explainable evidence, sharply reducing false positives against legitimate friends. Device attestation and pace signatures deter multi‑accounting without biometrics. For cash contexts, a public incident ledger records resolved cases, evidentiary hashes, and restitution, closing the loop with verifiable accountability.

Real‑time fairness survives poor networks via deterministic input buffers. In rummy variants, all player intentions are timestamped, committed, and revealed in lockstep, preventing advantage from latency or “race” discards. In aviator, the cash‑out signal is precommitted a few milliseconds ahead, then applied to the already committed multiplier trace, eliminating last‑frame edge exploits. These mechanics are documented with open test rigs so anyone can introduce jitter, packet loss, or clock skew and still reproduce the server’s decisions exactly.

Safety is proactive and measurable. Players can set hard budget envelopes and session objectives