I have a Spec Driven Project with 674 documents incl 181 specs, 429 tasks, and 40 project docs. About 1/3 of these are an acceptance sub-project to show the root project meets fit-for-purpose requirements. I used Fable to convert these to and from AsciiDoc, Markdown, and HTML. From this, I tried to draw some conclusions about which format is better than the others for AI specifications. TL;DR: Markdown for AI working documents, AsciiDoc for curated human-reviewed specs, HTML only as a publishing target. Objective Metrics Corpus Tokens (Approx) Markdown: 594k, AsciiDoc: 598K, HTML: 661k Worst…
Peter Lawrey is an Australian/British software engineer and entrepreneur best known for work on ultra-low-latency Java systems and for leading the open-source OpenHFT libraries. He is the founder and chief executive of Chronicle Software, a London-based company whose technology is used in trading and market-infrastructure workloads. Lawrey is also a recognised Java community figure: he was named a Java Champion in 2015, has been described by conference organisers as having provided the most answers for the Java and JVM tags on Stack Overflow, and writes the long-running Vanilla Java blog.…
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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…
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…
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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…
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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…
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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…
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This is the first in a series of posts supporting a talk I will be giving online at JChampionConf 27th January 27th 2025. Lessons learnt from founding my own company, and over 30 years hands on coding In these posts, I am looking to provide some theory as well as practical examples. One way to try to predict what is possible in the future is to look at the past. One of may favourite ways to look at the past is through aphorisms. Aphorisms are short, pithy statements that express a general truth or opinion. I love quotations because it is a joy to find thoughts one might have, beautifully…
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Not surprisingly, the system prompts for “o1” are restricted, but it can provide a hypothetical answer. Understanding the Role of System Prompts System prompts serve as the invisible backbone of an AI’s reasoning process. They define core objectives, ethical boundaries, and operational tactics well before the user asks a question. In older models, these prompts were often implicit or underspecified, leaving the AI uncertain about handling ambiguous instructions or potentially unsafe requests. Hypothetical System Prompts for o2 The system prompts for a hypothetical next-generation O2…
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Introduction Measuring an object’s size in Java is not straightforward. The platform encourages you to consider references and abstractions rather than raw memory usage. Still, understanding how objects fit into memory can yield significant benefits, especially for high-performance, low-latency systems. Over time, the JVM has introduced optimisations like Compressed Ordinary Object Pointers (Compressed Oops) and, more recently, Compact Object Headers. Each of these can influence how large or small your objects appear. Understanding these factors helps you reason about memory usage more…
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Standard Java libraries and idioms may only sometimes suffice in high-performance and low-latency Java systems. This article explores unconventional yet practical techniques that push Core Java to its limits, focusing on performance, diagnostics, and determinism. Drawing on experiences from building ultra-low-latency libraries and infrastructure, we will highlight patterns such as capturing stack traces without exceptions, system-wide unique timestamps, "trivially copyable" data types, zero-garbage strategies, and more. We will also discuss lessons from applying these approaches in production…
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This article explores a subtle Java conversion puzzle that challenges assumptions about how arithmetic operations, implicit casting, and floating-point conversions interact. Inspired by complexities often encountered in low-latency and high-performance environments, it demonstrates why a keen understanding of Java’s type system is essential for building reliable and efficient applications. Introduction The following example demonstrates a scenario where an innocuous-looking arithmetic operation leads to a surprising result. While such questions are rare and arguably impractical, they…
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1. Defining the Problem In many numerical computations, one would reasonably expect that rounding 0.499999999999999917 should yield 0, since it appears to be slightly less than 0.5. Yet, in Java 6, calling Math.round() on this value returns 1, a result that may initially seem baffling. This seemingly minor discrepancy stems from the interplay of binary floating-point representation, rounding modes, and the particular internal implementation details of Math.round() in earlier Java releases. For professionals in performance-sensitive environments—such as those working in financial technology or…
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