Validation is continuous and distributed. Quality emerges from consensus.
Fixies validate, infer, and update the graph
Multiple validators must agree before information is accepted
Errors are detected and corrected automatically as they occur
New data enters the network from various sources and agents
Fixies analyze data for consistency, accuracy, and completeness
Multiple validators vote on data quality and accuracy
Validated data is integrated into the knowledge graph
Fixies are autonomous agents embedded in the system. They validate, infer, and update the graph directly. A Fixie might infer a new relationship and add it, or observe data that contradicts existing knowledge and flag it for review. They also gather feedback and incorporate learning.
Fixies operate on-premises, in the cloud, or embedded in Letta.
Validation is continuous and distributed. Fixies analyze, cross-check, and update the graph as new data arrives.
Multiple Fixie agents independently validate each piece of data before it's accepted into the knowledge graph.
Human experts can be brought in for complex validations, especially for domain-specific knowledge. Reputation scores and token-based incentives (Florin) apply.
The network continuously monitors for changes, inconsistencies, and new information that might affect existing knowledge.
Every piece of data receives a trust score based on its source, validation history, and consensus level among validators.
ƒ(xyz) networks automatically detect and repair inconsistencies, ensuring data integrity over time.
When conflicts are detected, the system applies corrections based on consensus and trust scores.
Validation algorithms improve over time, learning from past validation decisions and outcomes.
Building the future of decentralized finance and governance