AWS Web3 Blog

Syncing an Ethereum node can be a time-consuming and costly process if not well optimized, with the need to find the right balance between speed and security. Compute requirements are different between the initial synchronization phase with the network and the steady-state phase where the node only needs to process new blocks (for additional details, refer to Synchronization modes in the Ethereum documentation). This challenge can be addressed by using different types of Amazon EC2 instances corresponding to your requirements. In this post, we demonstrate how to use the latest generation of storage optimized EC2 instances during the synchronization process, and switch back to right-sized memory optimized instances for the run phase to minimize cost.

A crypto wallet is a tool for managing cryptocurrencies, often built using a combination of software and hardware components. While developers prefer to offload infrastructure management to a trusted provider, this creates a new challenge. Giving full control to a third-party entity can introduce risks, such as service denial or the potential loss of funds if the provider is compromised. Coinbase Developer Platform (CDP) solved this challenge by building theWallet API, used to create and manage programmatic wallets. This post describes the Wallet API system architecture, threat models, and how Coinbase and AWS partnered to increase enclave throughput by a factor of 10.

Recent security incidents across blockchain and broader IT sectors underscore the persistent risk of sophisticated attacks on software supply chains and build environments. Reproducible builds offer a powerful mitigation strategy by making sure that software compiled from the same source code and dependencies consistently produces identical binaries, making it possible to detect tampering. In this […]

Digital assets is a rapidly maturing set of asset class, and customers are choosing AWS to build exchanges and trading platforms to differentiate their offerings across this dynamic and growing industry. In this multi part series, we explore the world of centralized exchange (CEX) and market maker (MM) infrastructure on AWS. In Part 1, we […]

Stablecoins offer significant advantages in the crypto space. They provide price stability and can serve as a reliable medium of exchange, store of value, or bridge between the fiat and crypto ecosystems. The ability to transfer stablecoins across multiple blockchains further enhances their utility by improving cross-chain interoperability and letting users take advantage of the […]

In this post, we share how Derive successfully scaled their hybrid decentralized trading platform to achieve billions of dollars in trading volume and low-latency execution by using a robust compute and database infrastructure, using AWS Graviton on Amazon Elastic Kubernetes Service (Amazon EKS) and Amazon Aurora. We explore Derive’s hybrid exchange model and how AWS played a crucial role in their growth and scalability.

Explore a reference architecture for real-world asset tokenization which integrates AWS services and Fireblocks’ tokenization SaaS platform with existing financial services infrastructure. By using innovative and novel technologies, such as distributed ledger technology combined with well-architected, secure and resilient cloud architecture patterns, this approach not only demonstrates the feasibility of asset tokenization but also highlights its potential to enhance efficiency, transparency, and accessibility in today’s digital economy. As businesses continue to explore the possibilities of blockchain, this architecture serves as a solid foundation for more complex and scalable tokenization solutions in the future.

As Web3 and generative AI technologies continue to rapidly evolve, a new category of applications known as crypto AI agents has emerged. These agents use large language models (LLMs) for their intelligence to accomplish a variety of blockchain-related tasks through a supervisor-collaborator architecture. A supervisor agent orchestrates specialized collaborator agents to analyze blockchain data, identify […]

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.

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.