Randomness Pipeline
Bankroll Solvency
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About This Devnet
Exohash Devnet — Live Risk Engine Simulation
This environment runs a fully on-chain gaming engine with live liquidity, verifiable randomness via threshold DKG beacon, automated bots simulating player behavior, and real-time solvency enforcement. The purpose of this devnet is to demonstrate capital efficiency, risk controls, and throughput under sustained load.
What This Demonstrates
›On-chain bet lifecycle — placement → RNG → settlement
›Threshold DKG randomness — committee-generated, verifiable, non-predictable
›Bankroll-based liquidity model with deterministic capital reservation
›Per-position and global risk caps enforced at consensus layer
›Real-time solvency tracking under continuous automated load
Liquidity & Risk Model
›Each game is backed by a dedicated on-chain bankroll
›Every bet reserves its maximum theoretical payout at placement
›A per-position cap limits single-bet exposure to a fraction of liquid capital
›Global reservation cannot exceed a fixed ceiling of bankroll assets
›Settled positions release capital immediately on-chain
This ensures deterministic solvency under bounded risk conditions.
Simulation Notice
›Traffic is generated by automated bots
›Stakes are programmatically distributed within configured bounds
›Liquidity pools are seeded for testing purposes
›This environment is designed to test mechanics and capital dynamics, not organic user growth
Capital at Risk
Represents reserved capital as a percentage of total bankroll assets. Reflects worst-case payout exposure, not expected loss.
Healthy operating range: 10–30% depending on game volatility and concurrent position count.
Live Traffic
Current load is generated by 30 bots running across crash, dice, and mines game engines simultaneously.
Throughput target: 20,000 USDC / hour under continuous bot activity.
Technical Stack
›Cosmos SDK — modular application chain
›CometBFT — Byzantine fault-tolerant consensus
›Threshold DKG — on-chain verifiable randomness beacon
›Pluggable game engines — stateless, auditable reducers
›BFF stats server — server-side state, SSE push to clients
What To Observe
›Capital at Risk fluctuations during high-multiplier crash streaks
›Stable block times under continuous transaction load
›Risk cap enforcement — bets rejected when bankroll nears ceiling
›Randomness pipeline — DKG session rotation every ~200 blocks
›Immediate capital release after position settlement
›Validator participation in each DKG committee phase