Vanilla Java

TL;DR Human-in-the-loop is essential; AI offers probability, not certainty. AI excels at word-smithing , so spend more time on documentation and context. Leverage diverse AI models for varied research, improvements, and analysis. Be wary of deskilling : if AI makes a task trivial, agents may soon replace it. You should feel like you are testing the boundaries of what AI is capable of for at least some tasks. The Problem AI’s proficiency in handling routine coding allows human engineers to dedicate more time to strategic activities such as system design, architectural planning, intricate requirement elicitation, and the rigorous evaluation of application performance across multifaceted metrics. Tools often amplify underlying behaviours and failures — Rob Lambert Value is increasingly found not in rote knowledge, which AI can often provide, but in the capacity to frame complex problems effectively for AI, critically evaluate its probab...

This article was AI-generated using this project as context. AIDE Project . The purpose of this project is to see how much an AI could generate given enough context, and in this project, all the "source" code is generated using the requirements and unit tests as context. This follows the Next-Level Development: Harnessing AI with AIDE approach. The main parts are: Requirements Documents in .adoc format. JUnit tests in Java. AIDE itself. AI-generated code in Java. Even the articles written about AIDE are part of the context for the AI. This article is about how to write requirements as a prompt for AI to generate code, seeded with AIDE in the context. Everything after this line is AI generated. Prompt engineering can differentiate between AI-driven code that works and genuinely excels. Drawing insights from real-world Java projects—especially those striving for low latency, high throughput, and clear domain logic—this article will show y...

Introduction Java’s annotation system has come a long way since its introduction in Java 5. At first glance, annotations appear to be mere metadata markers on classes and methods. However, annotations can do much more than that. You can nest types within them, incorporate fields that reference helper classes, and even embed logic via static singletons. These capabilities provide a powerful mechanism for integrating domain-specific or framework-specific functionality right into your code, in ways that are both compact and self-documenting. Why Add Code to Annotations? The Java language specification usually treats annotations as static metadata describing a type, method, field, or parameter. However, you can leverage nested classes (including enums, interfaces, and even other annotations) to extend the functionality of a single annotation. This approach allows you to keep logic closely tied to the metadata, rather than scattering it across multiple classes. Common use ca...

I find time fascinating and surprisingly complex. Time zones and calendars change from place to place over time. There are a number of interesting websites on the subject. Time is one of those concepts that appears deceptively simple on the surface yet becomes increasingly intricate the more we examine it. As software developers, we often face scenarios where we must handle dates and times and their myriad associated rules—time zones, calendar systems, cultural conventions, and historical irregularities. Working with time can lead us into subtle pitfalls that affect everything from straightforward user interfaces to global financial systems. Time is an illusion. Lunchtime doubly so. — Douglas Adams The Hitchhiker’s Guide to the Galaxy The Surprising Complexity Behind Time Time is not uniform. Humans have invented calendar systems and measurement techniques, each influenced by politics, religion, and culture. As a result, how we record and interpret dates ha...

Artificial Intelligence (AI) has long promised to transform software development. Yet, as many experienced engineers discover, initial enthusiasm often settles into a more subdued reality. This is the "Trough of Disillusionment" within the Gartner Hype Cycle—where inflated expectations give way to measured assessments. In this phase, teams confront the practical limitations of AI-driven tools, refine their strategies, and seek a balance between what AI can deliver and what human expertise must still provide. This article continues from AI on the Hype Cycle . We do these things not because they are easy, but because we thought they were going to be easy. — Programmer’s Credo Challenges of AI Adoption When integrating AI into software engineering workflows—be it code completion, architectural documentation, or performance tuning hints—teams quickly encounter stumbling blocks: Accuracy and Reliability: AI-generated content may contain inaccuracies, ou...