As AI and on-chain applications gradually integrate, the industry faces a long-standing overlooked issue: the difficulty in clearly verifying the sources of data and models.
The emergence of @codexero_xyz has begun to make this fundamental aspect traceable and understandable. It aims to establish clear source records for code, models, and data, so that creation and usage are no longer ambiguous.
This traceability provides a new trust foundation for collaborative AI and the open-source ecosystem.
When contributions can be accurately recorded, incentives and responsibilities can truly be aligned, which holds long-term value for the entire decentralized AI industry.
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As AI and on-chain applications gradually integrate, the industry faces a long-standing overlooked issue: the difficulty in clearly verifying the sources of data and models.
The emergence of @codexero_xyz has begun to make this fundamental aspect traceable and understandable. It aims to establish clear source records for code, models, and data, so that creation and usage are no longer ambiguous.
This traceability provides a new trust foundation for collaborative AI and the open-source ecosystem.
When contributions can be accurately recorded, incentives and responsibilities can truly be aligned, which holds long-term value for the entire decentralized AI industry.
@ClusterProtocol @wallchain @x__score @kyparus @easydotfunX