Regal IT Solutions — Blog
Ideas, tools and lessons from building software in Australia.
Practical writing on AI, developer tools, website design and running a digital business — from the team behind DineKit and Regal IT Solutions in Perth.
Why My Java Application Was Using More Memory Than Expected
Java memory usage keeps climbing? Learn to tell a real Java memory leak from normal JVM behaviour, and how to investigate heap, Garbage Collector and native memory.
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AI-Powered Java Development: How AI Is Changing the Way We Code
See how AI-powered Java development helps with unfamiliar code, stack traces, tests and refactoring, and where developers still need to verify every result.
Integrating AI into Java IDEs: A Practical Guide for Developers
How Java developers can use AI coding assistants inside IntelliJ, Eclipse, and VS Code - real use cases, real failures, and a workflow that keeps you in control.
Building Reliable AI Applications: Why LLMs Alone Aren't Enough
A fluent LLM answer isn't the same as a reliable one. Here's what I found when I looked into why an AI support assistant gave a wrong answer, and what actually fixes it.
Should You Upgrade From iPhone 17 Pro Max to iPhone 18 Pro Max — Or Wait for Apple’s Foldable iPhone?
iPhone 17 Pro Max to iPhone 18 Pro Max: Is the Upgrade Significant Enough?
Why AI Agents Fail: The Hidden Problems Behind Autonomous AI Systems
Autonomous AI agents fail not from a lack of intelligence, but from compounding error loops, memory poisoning, indirect prompt injection, and missing deterministic boundaries. Discover why multi-step agentic systems break in production—and the 7-pillar systems engineering blueprint required to build safe, bounded, and reliable enterprise AI agents.
RAG vs Fine-Tuning vs Prompt Engineering: Which Should You Use?
A comprehensive engineering guide and practical decision framework comparing Prompt Engineering, RAG, and Fine-Tuning. Learn when to adjust instructions, retrieve external context, or modify model weights to build cost-effective, production-ready GenAI applications.
Retrieval-Augmented Generation (RAG): How AI Systems Answer Questions Using Your Data
Retrieval-Augmented Generation (RAG) gives AI systems access to external knowledge before generating an answer. This guide explains how RAG works, from document ingestion and embeddings to retrieval, reranking, context assembly, and grounded responses.
Understanding Model Context Protocol (MCP): The Standard Behind Modern AI Agents
Model Context Protocol (MCP) is emerging as the standard for connecting AI agents with business tools, APIs, databases, and enterprise applications. Learn how MCP works, why it matters, and how it enables scalable, secure AI automation for modern businesses.
Claude Code vs. Codex CLI: Which AI Coding Assistant Should You Choose?
Comparing Claude Code and OpenAI's Codex CLI — two terminal-based AI coding agents — to help developers pick the right one for their workflow.
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