We’re partnering with gdotv to simplify development with our Amazon Neptune cloud emulator component. You can now easily query, visualise and model your graph data either interactively or using the Gremlin querying language with G.V() - Gremlin IDE.With G.V(), you can considerably enhance your graph database development experience whilst gaining access to a powerful reporting and visualisation toolset for your production data. With LocalStack’s core cloud emulator, parity is ensured between a local Neptune instance and AWS’s own, meaning Gremlin queries in your development environment will behave identically on Amazon Neptune. In this video we demonstrate how to use G.V() with LocalStack Neptune.Read the announcement blog here: https://blog.localstack.cloud/2024-06-05-localstack-neptune-development-with-gv-gremlin-ide/

Real cloud developers aren’t pushing straight to AWS.They’re building and testing everything locally before a single deploy goes live.This episode breaks down the modern cloud dev workflow and how tools like LocalStack make it possible to move fast without burning money (or trust).Learn how local-first dev culture is changing the cloud game.

LocalStack Applications in Developer Hub provides sample templates to help LocalStack users adopt real-world scenarios to rapidly and conveniently create, configure, and deploy applications locally. ## Getting startedIn this demo, we will setup a Sagemaker on Localstack

What if your AI coding assistant could not only write infrastructure code, but also deploy it, test it, and fix issues automatically — all on your local machine? That's exactly what the LocalStack MCP Server makes possible.In this session, we'll introduce the LocalStack Model Context Protocol (MCP) Server, a new tool that lets AI agents manage your entire local cloud development lifecycle through a conversational interface. You'll learn:What MCP is and why it's a game-changer for AI-assisted developmentHow the LocalStack MCP Server turns manual cloud tasks into automated workflowsHow to set up and configure the server with your favorite AI editor (Cursor, VS Code, etc.)Real-world demos: deploying CDK apps, analyzing logs, running chaos tests, managing state with Cloud Pods, and more.Through hands-on examples, we'll walk through a complete workflow where an AI agent deploys a serverless application, verifies resources, troubleshoots issues, and tests resilience, all without leaving the conversation.If you've ever wished your AI assistant could do more than just generate code, this talk will show you what's possible when agents can actually manage your local cloud environment.