Last year I was waiting for an answer to a question:

Can AI no longer be a black box?

Until I met Recall Network @recallnet

It is not to say that it perfectly solves all problems, but it has shown us for the first time that: AI is not something that can only run in the closed systems of large companies, nor is it only determined by whoever shouts the loudest. It can be open, transparent, and have a credit system.

The core design of Recall is very clear:
Let the behavior, data sources, and reasoning processes of every AI Agent leave a "trace on the chain." What does this mean? It means you can trace the source, path, and logic of an intelligent decision, rather than just looking at the final output.

This structure offers developers and users a whole new possibility:

AI no longer relies on promotion or POC, but instead wins trust through practical ranking. AI agents on Recall can compete on "real capabilities" through competitions, tasks, and data calls, where performance is verifiable and capabilities are comparable.

At the same time, Recall has built a network where AI can safely share knowledge using protocols like Ceramic and Tableland. What one AI learns is no longer just the private property of that single model, but can become part of the accumulation of the entire intelligent network.

You may have heard many stories about "Web3 + AI", but the underlying Recall is more engineering-oriented. It truly attempts to establish a decentralized AI network that can operate, starting from key points such as credit, behavioral records, data access permissions, and model context interface (MCP).

The community is also rapidly taking shape. Over 200,000 active Discord users, an intensive points task mechanism on Galxe and Zealy, and support from over 40 institutional funds... all of this is not just about the hype itself, but rather the seeds of network effects beginning to emerge.

My judgment is:

Recall is not an overnight sensation project; it is more like a proposal that integrates the spirit of Web3 into the core architecture of AI. It does not address the question of "Will AI make mistakes?" but rather the question of "Can we hold AI accountable and can it be improved after it makes mistakes?"

This is the future that is truly worth building.

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