DZone.com Feed

Runtime Formula Evaluation With MVEL Library in Spring Boot

Jun 16th 2026, 15:00 by Erkin Karanlık

In our software development processes, business units constantly want to update discount rates, loyalty points, or salary calculation logic.

If this logic is within the code, between when-or-if-else blocks, every change means a new unit test process, code analysis, CI/CD pipeline work, and ultimately a "deployment."

‍

Getting Started With GitHub Copilot CLI for Coding Tasks

Jun 16th 2026, 14:30 by Gunter Rotsaert

Nowadays, there are quite a lot of AI coding assistants. In this blog, you will take a closer look at GitHub Code CLI, a terminal-based AI coding assistant. GitHub Copilot CLI integrates smoothly with GitHub Copilot, so if you have a GitHub Copilot subscription, it is definitely worth looking at. Enjoy!

Introduction

There are many AI models and also many AI coding assistants. Which one to choose is a hard question. It also depends on whether you run the models locally or in the cloud. When running locally, Qwen3-Coder is a very good AI model to be used for programming tasks. In previous posts, DevoxxGenie, a JetBrains IDE plugin, was often used as an AI coding assistant. DevoxxGenie is nicely integrated within the JetBrains IDE's. But it is also a good thing to take a look at other AI coding assistants. In previous blogs, Qwen Code and Claude Code were used in combination with local models.

‍

Building a Multi-Agent Orchestration Capability: Architecture and Code Walkthrough

Jun 16th 2026, 14:00 by Narendra Lakshmana gowda

Artificial intelligence (AI) is quickly changing from simple conversation models to systems that can tackle complex problems through teamwork. As products become smarter, one key approach that is gaining traction today is multi-agent orchestration.

A single AI model can handle straightforward tasks like answering questions or generating content. Yet, modern product features increasingly need:

‍

Your AI Is Not Failing, Your Context Is

Jun 16th 2026, 13:00 by Faisal Feroz

Most AI failures in products do not happen because the model is weak. They happen because the model is guessing in the dark.

A large language model can write code, summarize meetings, draft emails, generate reports, and answer customer questions. But when it does not know which customer, which contract, which policy, which ticket, which version of the truth, or which permission boundary applies, it will still produce a confident answer.

‍

Reducing RAG Hallucinations With Relationship-Aware Retrieval

Jun 16th 2026, 12:00 by Mahesh Vaijainthymala Krishnamoorthy

Retrieval-augmented generation (RAG) is now the default pattern for grounding large language models in private or domain-specific knowledge. Yet most RAG systems still hallucinate, and the cause is rarely the model itself. It is the retrieval step. A language model can only reason over the passages it is handed; when retrieval returns an incomplete or disconnected set of passages, the model quietly fills the gaps with plausible-sounding but unsupported text. The retrieval layer, in other words, is where trustworthiness is won or lost.

This article examines a specific architectural idea — relationship-aware retrieval — and how it addresses the retrieval weaknesses that lead to hallucination. The reference implementation is RudraDB-Opin, a free, relationship-aware vector database. RudraDB-Opin is the free edition built for learning, prototyping, and real projects: it supports up to 100,000 vectors and 500,000 relationships — ample room to model a substantial knowledge base and demonstrate every retrieval pattern discussed here.

‍