Overview of the Fabric-X-Committer architecture and share performance evaluation results that highlight its scalability and efficiency.

In this meetup, we will provide an overview of the Fabric-X-Committer architecture and share performance evaluation results that highlight its scalability and efficiency.

Overview of the Fabric-X-Orderer architecture, underlying protocols, and performance evaluation results highlighting scalability and efficiency.

Practical deployment strategies using Ansible and Kubernetes to bring Fabric-X solutions into production.

To close the session, we’ll walk through practical deployment strategies using Ansible and Kubernetes, equipping you with the tools and confidence to bring your Fabric-X solutions into production.

A session exploring the Fabric-X endorsement phase, how it differs from the traditional Hyperledger Fabric model, and implications for developers. We'll cover tokenization use cases, hands-on examples, and practical deployment strategies using Ansible and Kubernetes.

A deep dive into the Fabric-X endorsement phase, highlighting how it differs from the traditional Hyperledger Fabric model. Through hands-on examples, we’ll demonstrate how the new model streamlines development for designing decentralized applications and tokenization use cases.

A session focusing on the endorsement phase of Fabric-X, comparing it to traditional Hyperledger Fabric, with hands-on examples showing how the new model streamlines development for tokenization use cases and on-chain asset transfer. The session will also cover practical deployment strategies using Ansible and Kubernetes.

In this session, we’ll take a deep dive into the endorsement phase of Fabric-X, highlighting how it differs from the traditional Hyperledger Fabric model and what these changes mean for developers. Through hands-on examples, we’ll demonstrate how the new model streamlines development and unlocks exciting possibilities for designing decentralized applications. We’ll also explore how to build applications tailored for tokenization use cases.