Three core technology modules form the hard foundation of HumanCloud.
Global nodes match tasks with executors in real time — millisecond dispatch, massive concurrent throughput. Calling humans becomes as simple as calling a cloud API.
AI generates multiple answers; human judges pick the best in real time, producing genuine preference signals that feed straight back into large-model alignment training.
Entertainment scenarios continuously accumulate active human compute, building the cold-start flywheel of traffic → user pool → compute.
Scarce "real human preference judgment" compute — an absolute necessity for large-model training.
The model produces several candidate outputs for the same question, and they enter the judgment queue.
Distributed human nodes evaluate in real time and select the higher-quality output.
Results are converted into preference and labeling data that flow back into the training pipeline in real time; the AI task SDK also opens an in-app task center for crowdsourced execution.