ASIC operating-efficiency intelligence
Optimize the operating profile.
Never pretend to predict the nonce.
Explore reverse targets, constrained planning, sensitivity and Monte Carlo robustness against a synthetic ASIC Digital Twin. Everything runs inside this browser tab.
Public simulator LIVEReal ASIC pilot HOLDHardware writes 0Autonomy OFF
Honest boundary: this experience does not connect to a miner, pool, wallet, LAN or server-side device API. It optimizes simulated operating efficiency—not Bitcoin SHA-256.
Controlled experiment
Choose one explicit objective.
A candidate can win only for the selected objective while all hard constraints remain satisfied.
SHADOW RUNREADY
Select an objective and run the synthetic experiment.
Digital Twin confidenceNot evaluated
Champion / challenger
Baseline comparison, not marketing.
Raw hashrate and accepted work are reported separately. Trade-offs remain visible.
| Metric | Stock champion | Shadow challenger | Delta |
|---|---|---|---|
| Run an experiment to populate the comparison. | |||
Awaiting telemetry
- No causal conclusion before evidence.
Applicability engine waiting
Sensitivity is calculated only when a candidate is meaningful.
Monte Carlo not run
The simulator will disclose the run count, uncertainty and violation probability.
Safety contract
The website is live. The real ASIC is not connected.
These are separate statuses and remain separate in code and evidence.
Public Mining Shadow
- Synthetic Digital Twin
- Reverse target solver
- Constrained optimizer
- Monte Carlo and stability
- Champion/challenger comparison
Real ASIC telemetry pilot
- No owner-controlled ASIC configured
- No private IP on this server
- No LAN discovery
- No telemetry upload
- Waiting for local collector
Hardware control
- No frequency or voltage writes
- No fan or power-limit writes
- No pool configuration
- No firmware, reboot or shell
- No autonomous tuning
Approved claim: “ReverseML Mining Intelligence can optimize and compare simulated ASIC operating profiles under explicit power, thermal, efficiency and stability objectives using reverse optimization and robustness analysis.”