Developers often face challenges in setting up consistent and reliable environments across teams. With the rapid pace of technological advancement, tools like VS Code DevContainer and Google Cloud Workstations have become invaluable in standardizing development setups. This guide walks you through creating a robust Java development environment tailored to modern needs.

All configurations and scripts mentioned in this post are available in the Java Development Environment Template repository. Feel free to explore and adapt it to your needs.

Adding Vue Flow to Your Project

This guide will walk you through integrating Vue Flow into your Monorepo project, including installing dependencies, setting up routing, and creating an editable flow page.

What is Vben Admin?

Vben Admin is a comprehensive solution for developing modern admin dashboards and management systems. It is built with Vue 3.0, Vite, and TypeScript, offering a robust and scalable foundation for enterprise applications.

Key Features:

• Cutting-Edge Technology Stack: Developed using the latest front-end technologies, including Vue 3, Vite, and TypeScript.
• Ready-to-Use: Comes with a complete set of admin dashboard features right out of the box.
• Multi-UI Framework Support: Compatible with popular UI libraries such as Ant Design Vue, Element Plus, and Naive UI.
• Full Internationalization: Includes a well-designed multi-language solution for seamless localization.

In today’s complex, microservices-driven landscape, understanding how requests flow through distributed systems is essential for maintaining performance, diagnosing issues, and optimizing applications. Distributed tracing provides a powerful tool for developers and operations teams to visualize and analyze the journey of requests across various services and components.

This comprehensive guide will take you through the process of integrating Spring Boot with OpenTelemetry using Micrometer to achieve robust distributed tracing. We’ll cover everything from setting up a local development environment to deploying your application on Google Cloud Platform (GCP) Cloud Run, ensuring you gain a thorough understanding of how to implement and leverage distributed tracing in both local and cloud environments.

This guide provides a step-by-step approach to integrating OpenTelemetry with Spring Boot and deploying it to Google Cloud Platform (GCP). The setup includes configuring the necessary dependencies, setting up OpenTelemetry Collector, creating a Docker environment, and deploying the application to GCP Cloud Run.

Important Notice: This article is for reference only. Please refer to the latest guide: Step-by-Step Guide to Integrating Spring Boot and OpenTelemetry with Micrometer on GCP for Distributed Tracing for up-to-date information and recommended practices.

It’s important to note that Micrometer is now the recommended framework by Spring for metrics and tracing in Spring Boot applications. The latest Spring Boot versions have integrated Micrometer as the default metrics and tracing facade, which can be easily configured to work with OpenTelemetry. For the most current approach to implementing observability in Spring Boot applications using Micrometer and OpenTelemetry, please refer to the guide linked above.

In modern software development, cloud-based development environments are becoming increasingly popular. Google Project IDX is a powerful tool that helps developers efficiently develop and deploy applications. This article will guide you through using Google Project IDX to develop a Spring Boot project, covering the necessary configurations and usage. If you want to try it out immediately, you can open it directly using my GitHub at https://github.com/samzhu/demo-project-idx.

Function calling is a powerful mechanism in AI development, allowing developers to specify a set of tasks or functions that an AI model can execute. These functions are clearly defined using a schema that outlines expected inputs, outputs, and any additional parameters the AI needs to perform the desired action effectively. By utilizing function calling, developers can precisely guide the AI’s behavior to ensure its responses are aligned with the application’s specific requirements.

In this installment of our series on building AI applications with Spring AI, we explore the powerful capabilities of vector databases. These specialized databases enhance AI systems by efficiently managing and utilizing embeddings—dense vector representations crucial for processing and understanding large-scale, complex data sets. Read on to learn how vector databases integrate seamlessly with Spring AI to revolutionize data handling in AI applications.

As of February 1, 2024, Sonatype has abandoned the old way of registering via Jira tickets for Maven Central. This article will guide you through the new process of publishing a Jar file to the Maven Central Repository.

Following our initial dive into creating prompts with Spring AI, this article ventures further into enhancing user interactions. We focus on incorporating chat histories and delivering responses in real-time using Server-Sent Events (SSE). This combination not only elevates the user experience by providing instant feedback but also simulates a dynamic conversation flow, akin to real-life interactions.