In the discussion of decentralized AI, many issues ultimately come back to the same starting point: where does the data come from, is it genuine, and can it be used long-term.
The reality is that data is scattered across different entities, lacking unified standards and continuous incentives, making it difficult for AI applications to grow stably.
@codexero_xyz starts from the data source, building data infrastructure around verifiability, traceability, and aligned incentives to ensure data contributors receive fair rewards, while enabling users to assess data quality.
This bottom-up approach to solving trust issues allows AI to no longer rely on vague data black boxes.
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In the discussion of decentralized AI, many issues ultimately come back to the same starting point: where does the data come from, is it genuine, and can it be used long-term.
The reality is that data is scattered across different entities, lacking unified standards and continuous incentives, making it difficult for AI applications to grow stably.
@codexero_xyz starts from the data source, building data infrastructure around verifiability, traceability, and aligned incentives to ensure data contributors receive fair rewards, while enabling users to assess data quality.
This bottom-up approach to solving trust issues allows AI to no longer rely on vague data black boxes.
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