As generative AI and Web3 technologies continue to develop rapidly, a new class of applications called crypto AI agents has surfaced. These agents leverage large language models (LLMs) for their intelligence, executing a variety of blockchain-related tasks through a supervisor-collaborator framework. The supervisor agent coordinates specialized collaborator agents to evaluate blockchain data, identify trends, and facilitate decision-making processes.
In another enlightening blog post, this article explores additional facets of this innovative technology.
The importance of decentralized autonomous organizations (DAOs) in overseeing LLM training data governance cannot be overstated. In a series of articles, we delve into the use of a DAO for managing the lifecycle of an AI model. The first entry discussed the ingestion process of training data, while subsequent parts have outlined the deployment of a smart contract on the Ethereum Sepolia testnet using Remix and MetaMask. We have also implemented Amazon API Gateway and AWS Lambda functions to facilitate data transfer from the InterPlanetary File System (IPFS) to Amazon Simple Storage Service (Amazon S3), creating a streamlined data flow. For a comprehensive understanding of these processes, check out another informative piece from this authority on the subject.
In the latest installment, we demonstrate how to set up MetaMask authentication, build a user-friendly front-end interface, and test the overall solution. Additionally, if you’re interested in what to expect on your first day, this resource is excellent.
Stay tuned for more insights as we continue to explore the intersection of blockchain and AI in the context of Amazon Bedrock.
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