This repository contains the source code for the Metal Release Butler microservice. The service is a Groovy based Spring Boot application. It collects information about announced album releases of metal bands from external sources. This information is persisted in a database and made available through a REST endpoint.

This repository contains the source code for the Metal Detector backend application. The application is based on Spring Boot.

Spring Boot example application with spring cloud integration

♨️ Spring Boot 3 + Angular + HTML5 router mode + HTTP interceptor + Lazy loaded modules

Extract artwork from MP3/FLAC and convert them to ascii art

A web application that simulates a library, complete with an account system, multiple user roles (reader, librarian, administrator) and two different working modes: REST API (pure back-end) and MVC ( both back-end and Thymeleaf-processed HTML pages).

Food ordering system following Clean, DDD, Saga, CQRS, and Hexagonal Architectural Design Patterns

基于Spring Boot搭建的新一代教学管理平台，提供权限管理、考试、问卷、练习等功能

Asynchronous programming in Java allows tasks to run concurrently, not blocking the main thread. It can be achieved using Threads, ExecutorService, CompletableFuture, or Reactive Programming.

Single Table - Add/Delete/Change/Query

samples for www.pkslow.com: Java, Spring Boot, Spring Cloud, Docker, Kubernetes, Cloud, Big Data

Modern Reactive CQRS Architecture Microservice development framework based on DDD and EventSourcing | 基于 DDD & EventSourcing 的现代响应式 CQRS 架构微服务开发框架

An implementation of a User Account service integrated with Debezium

microservice provided by API used by roxy blog. https://roxy.iro.ooo/api/v1

Auction site made for software engineering class (Next.js, Spring boot, PostgreSQL, AWS, Azure)

A library web app

🪴 Self-hosted, open source gardening companion app

백엔드 공부에 대해 기록/저장하는 레포지토리

Example to run Liferay and Client Extensions in a Docker Compose stack.

A project to show howto use SpringAI with OpenAI to chat with the documents in a library. Documents are stored in a normal/vector database. The AI is used to create embeddings from documents that are stored in the vector database. The vector database is used to query for the nearest document. That document is used by the AI to generate the answer.