AWS Generative AI CDK Constructs are sample implementations of AWS CDK for common generative AI patterns.

WebApp Proof of Concept - Live Translation + Polly

The Generative AI Newsletter Application sample is a ready-to-use serverless solution designed to allow users to create rich newsletters automatically with content summaries that are AI-generated.

GPS data platform supporting auth, storage, and athletic data analytics. S3, AppSync, Lambda, DynamoDB, CDK, Cognito

AWS CDK construct to build AppSync JS resolvers using Typescript

This project demonstrates a video collaboration application powered by Amazon IVS Real-time, serving as a reference for building compelling social video experiences.

s6pack is a Serverless, Scalable, Secure, SOftware as a Service Starter Pack. It is boilerplate code that offers Stripe for subscription based payments, multi-tenant user management, and graphQL application.

1on1 call demo using Chime SDK meetings, Next.js, AppSync, and CDK

A repo to accompany a YouTube video on AWS Amplify Gen 2 and using AppSync JavaScript resolvers

The Daily Targum

A Bedrock Backed AppSync WebSocket API

This is an inspirational quote generator project to learn how to run scripts in AWS with Next.js

This project is an Amplify Gen2 demo app that Lambda mutates to AppSync and delivers events to subscribed React App in near real time.

appsync boilerplate: include typescrpipt resolvers

A serverless, real-time, wordle-inspired, multiplayer game.

This sample demonstrates the use of the Chime SDK React components together with an AppSync local resolver implemented with the "None" data source to provide GraphQL mutation driven subscription notifications without being backed by a persistent data store. It is a simple video chat React UI built and deployed using the AWS CDK.

AWS SAM Fullstack application (+CICD) example

Nuxt+Amplify+Appsyncでリアルタイムなチャットアプリを作成

⚡ Turns your ◭ Prisma Schema into a fully-featured GraphQL API, tailored for AWS AppSync.

An end-to-end blueprint architecture for real-time fraud detection(leveraging graph database Amazon Neptune) using Amazon SageMaker and Deep Graph Library (DGL) to construct a heterogeneous graph from tabular data and train a Graph Neural Network(GNN) model to detect fraudulent transactions in the IEEE-CIS dataset.