1cor13

Reverse engineering ICE’s AI to understand what’s really running under the hood.

What I found isn’t just data analytics—it’s an automated surveillance network built for precision at scale. The system draws from DMV databases, data brokers, phone metadata, facial recognition, and license plate readers. Together, these feeds form a unified view of movement and identity across most of the U.S. adult population.

The data isn’t just collected; it’s synthesized. ICE’s AI links records, learns patterns, and ranks potential targets by probability, not certainty. In technical terms, it operates as an entity resolution and pattern inference engine that keeps improving with every data refresh. Accuracy improves with density, but so do the stakes. One mismatched address or facial false positive can cascade into real consequences for someone who has no idea they’re even in the system.

What stands out most is how the technology has shifted enforcement from reactive to predictive. It no longer waits for an event—it f

1cor13

 void multisigNativeScript_stakeKeyRegistration() throws CborSerializationException {
        Policy policy = PolicyUtil.createMultiSigScriptAllPolicy("staking-scripts", 2);

        var scriptAddress = AddressProvider.getBaseAddress(policy.getPolicyScript(), policy.getPolicyScript(),
                Networks.testnet());
        var scriptStakeAddess = AddressProvider.getRewardAddress(policy.getPolicyScript(), Networks.testnet());

        System.out.println("Base Address: " + scriptAddress.toBech32());
        System.out.printf("Script Stake Address: " + scriptStakeAddess.toBech32());