Use case of Recall Protocol and Sapien

Sapien Protocol: A decentralized platform for data labeling and AI training that uses gamified mobile and social experiences to focus on human tasks like labeling, annotation, and verification.

Recall: mainly for persistent AI use cases, a decentralized agent architecture and AI memory layer that allows agents to store, reason, and act transparently with verified logs.

Target Users:
Sapien is based on human contributors, like as labelers, validators, and DAO participants, are the focus of Sapien. It is mobile-first, gamified, and social.
Recall focuses on AI engineers, autonomous agent developers, and technologies that create persistent memory for AI agents.

Architecture Focus:
For incentives and trust, Sapien emphasizes reputation, soulbound tokens, identity, and staking. Recall emphasizes Wasm-based execution, IPC (Interplanetary Consensus), agent storage, and model context.

Incentive Model:
Tokens are earned by Sapien users through reputation building, data verification, and microtask completion. Recall agents communicate via transparency, logging, and buckets (memory), and their incentive systems are based on competition results and performance.

Use Cases:
The Sapien annotation, reputation development, data labeling, and community/DAO interaction. Recall is memory-layer infrastructure, verified task execution, agent collaboration, and AI competitions.

Layer of the Stack:
The Sapien crowdsources human intelligence at the data input layer. Recall: Drives self-governing AI systems by functioning at the agent logic and storage layer.