AWS Web3 Blog
Use a DAO to govern LLM training data, Part 4: MetaMask authentication
In Part 1 of this series, we introduced the concept of using a decentralized autonomous organization (DAO) to govern the lifecycle of an AI model, focusing on the ingestion of training data. In Part 2, we created and deployed a minimalistic smart contract on the Ethereum Sepolia using Remix and MetaMask, establishing a mechanism to govern which training data can be uploaded to the knowledge base and by whom. In Part 3, we set up Amazon API Gateway and deployed AWS Lambda functions to copy data from InterPlanetary File System (IPFS) to Amazon Simple Storage Service (Amazon S3) and start a knowledge base ingestion job, creating a seamless data flow from IPFS to the knowledge base. In this post, we demonstrate how to configure MetaMask authentication, create a frontend interface, and test the solution.
Use a DAO to govern LLM training data, Part 3: From IPFS to the knowledge base
In Part 1 of this series, we introduced the concept of using a decentralized autonomous organization (DAO) to govern the lifecycle of an AI model, focusing on the ingestion of training data. In Part 2, we created and deployed a minimalistic smart contract on the Ethereum Sepolia testnet using Remix and MetaMask, establishing a mechanism to govern which training data can be uploaded to the knowledge base and by whom. In this post, we set up Amazon API Gateway and deploy AWS Lambda functions to copy data from InterPlanetary File System (IPFS) to Amazon Simple Storage Service (Amazon S3) and start a knowledge base ingestion job.
Use a DAO to govern LLM training data, Part 2: The smart contract
In Part 1 of this series, we introduced the concept of using a decentralized autonomous organization (DAO) to govern the lifecycle of an AI model, specifically focusing on the ingestion of training data. In this post, we focus on the writing and deployment of the Ethereum smart contract that contains the outcome of the DAO decisions.
Use a DAO to govern LLM training data, Part 1: Retrieval Augmented Generation
Blockchain and generative AI are two technical fields that have received a lot of attention in the recent years. There is an emerging set of use cases that can benefit from these two technologies. In this four-part series, we build a solution that governs the training data ingestion process of an AI model, using a smart contract and serverless components. We guide you through the different steps to build the solution. In this post, we review the overall architecture of the solution, and set up a large language model (LLM) knowledge base.
An introduction to real estate tokenization on AWS
Tokenization is the process of creating a digital asset on a blockchain that can represent various assets, including physical assets, digital assets, or even rights of ownership. In this post, we explore the advantages of tokenizing real estate assets using blockchain technology in order to streamline property ownership transfers. We discuss the benefits of tokenization and how it can modernize existing real estate processes, which could potentially extend to other industries and asset classes.
How to deploy Stacks blockchain nodes on AWS with the AWS Blockchain Node Runners Stacks blueprint
Bitcoin is the most widely adopted and valuable cryptocurrency, known for its decentralization and security. Stacks, a Layer 2 solution built on top of Bitcoin, aims to unlock Bitcoin’s full potential by enabling fast, cheap, Bitcoin-secured transactions and smart contract functionality without modifying the Bitcoin protocol itself. Stacks uses a consensus mechanism called Proof of […]
Analyze blockchain data with natural language using Amazon Bedrock
Data within public blockchain networks such as Bitcoin and Ethereum can be accessed by anyone. However, accessing and making sense of this information has traditionally been a complex and technical undertaking. Much of the data is encoded and stored as bytes, rather than in a human-readable format. In this post, we introduce a solution that demonstrates how you can chat with blockchain data using Amazon Bedrock and the AWS Public Blockchain datasets. We discuss Amazon Bedrock, review the solution architecture, provide example prompts, share interesting findings, and go over how you can extend the solution to integrate with different data sources.
Run an Ethereum staking service on Amazon EKS
In September 2022, Ethereum transitioned to a Proof of Stake (PoS) consensus model. This change allows anyone with a minimum of 32 ether to stake their holdings and operate a validator node, thereby participating in network validation and earning staking rewards. In this post, we explore the technical challenges and requirements of operating an institutional-grade Ethereum staking service. Additionally, we outline a solution for deploying an Ethereum staking service on AWS.
Build secure multi-party computation (MPC) wallets using AWS Nitro Enclaves
Different types of blockchain applications and users demand varying types of private key management solutions, referred to as wallets. Custodial wallets are managed by third-party vendors such as a centralized crypto exchange, whereas non-custodial wallets give you full control and ownership over your private keys and funds. Crypto-native users with experience managing their own digital […]
How Agnostic Engineering improved storage latency for running Polygon nodes on AWS
This is a guest post co-written by Arnaud Briche, the Founder of Agnostic. At Agnostic, our mission is to democratize access to well-structured blockchain data. We aim to provide a swift, user-friendly, and robust method for querying the vast volumes of data generated by smart contract blockchains. As a company, for performance reasons we first […]