Been tracking @recallnet closely and I think it’s carving out one of the most critical layers in the AI × Web3 stack

→ Decentralized AI memory infra: lets models persist, recall, and verify knowledge across sessions without giving up control to centralized silos
→ On-chain provenance: every stored/retrieved data point is cryptographically verified, making output auditable
→ Interoperable APIs: agents, dapps, and LLMs can tap into the same shared recall layer across multiple chains
→ Privacy-preserving queries so sensitive data doesn’t leak, yet remains usable for AI reasoning

The bet here is simple: AI use cases in Web3 need state, context, and trust to scale @recallnet builds all three directly into the protocol

What I’ll watch next:
• Latency + cost tradeoffs as usage scales
• Composability with existing agent frameworks
• Adoption by L2s and AI-native dapps

If execution keeps pace, #recallnet could become the default memory layer for onchain AI