When you start a new role, many challenges are placed on you. Once you have been in a role for a while, you may feel like you aren’t learning as much, your career isn’t progressing, and you wonder what job you might move to next. Having been in that situation many times, my suggestion is that rather than waiting to be given challenges or the next role, you take the opportunity to challenge yourself to grow in your current role. This doesn’t mean accepting a role with too little growth, but rather see if there are opportunities to grow while you have some capacity, if you drive them. For…
While I am sceptical of using AI for release code, it has plenty of uses that previously weren’t practical, such as determining how easy your software is to use. If an AI can “figure it out” with a few hints, then you are on the right track. For me, the value of AI is what you learn using it. For more Techincal Information on Chronicle-FIX What AI Does Well and What It Doesn’t Claude and Codex are effective for producing idiomatic code; for low-latency code, it needs a significant body of example code. In this case, it was able to utilise sample code for benchmarks. If it was being used to…
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In a previous article I looked at one-shoting a solution to optimise code to show the variation in different AI. Thsi is the not the best way to get what you want however. More often you need to either refine the prompt or give feedback. After one-shoting the same prompt on multiple AI, I have created a refined prompt based on the various concerns with previous results. The prompt Based on the results in a previous run Asking multiple AI to optimise the same code Suggest how to implement this more optimally using low latency techniques to minimize any objects created. ## Use - a ThreadLocal…
As different AIs are implemented differently, they don't all provide the same answer, nor do they consistently outperform one another. The best approach is to use multiple AI and pick the one you like best. My goal here is not to declare a winner based on one example, but instead to show the variety of answers you can get with different AI. I asked each AI to Suggest how to implement this more optimally private static String formatOffset(int millis) { String sign = millis < 0 ? "-" : "+"; int saveSecs = Math.abs(millis) / 1000; int hours = saveSecs / 3600; int mins = ((saveSecs / 60) % 60);…
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…
Table of Contents Introduction Superhuman Intelligence Is Already Here ATMs Didn’t Replace Bank Tellers About Me Multidimensional Growth Areas of Career Development Scope of Consideration Roles Where All Areas Are Important The Range of a Founder’s Role How Will AI Change Development? How You Ask the Question Changes the Result Some key terms in understanding how Generative AI works Estimating the Value of AI-Generated Documentation AI and the Reverse Baltimore Phenomenon The Baltimore Phenomenon The Reverse Baltimore Phenomenon Filling a void Brainstorming Ideas Sample Project 2048 Using…
As many have observed, at best, AI generates either: Mundane, repetitive documentation or code that most experts already know or If an expert doesn’t know it, they can ask an AI to explain it anyway. Is it the case that if an AI can generate it, it’s not worth adding to your documentation or code? While this is usually the case, there is still value in reading AI-generated documentation as a means of reviewing and validating what you might write differently. In this post, I show how changing the documentation can affect the AI’s output and how reviewing it can be a useful exercise. Estimating…
One of the first challenges developers might face is getting generative AI to produce accurate documentation. Once you are comfortable doing this, the next challenge is creating enough documentation to be helpful without overwhelming the reader. Until generative AI came along, it might have seemed like there could never be too much documentation. Now, the challenge is to provide just enough detail to give understanding without overwhelming the material with unnecessary details. I was exploring the best way to generate accurate documentation for a project as I was flying over Australia and saw…
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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If it’s worth doing by hand, it’s worth automating. Just because not everyone is (yet) a world-class developer; that doesn’t mean we can’t step closer to that expert-level space. In this post, I will introduce AIDE (Artifical Intelligence Development Environment), a powerful workflow that merges AI-driven code generation with a sharp focus on documentation-driven development. With AIDE, I tap into the best of artificial intelligence (AI) while respecting the real human insight needed for domain-specific logic. The result? An environment that streamlines repetitive coding, synchronises…
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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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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TL;DR Problem: Java’s standard serialisation can be slow due to scattered object fields and reflection-based overhead. Approach: Emulate C++-style trivially copyable objects by restricting fields to primitives, enabling bulk memory copies. Result: Near C++-like serialisation performance, dramatically reducing latency and improving throughput. Trade-offs: Requires careful design, limited flexibility, and testing for JVM compatibility. Outcome: Low-latency systems with high performance, suitable for financial data feeds, real-time analytics, and other latency-sensitive domains. Introduction For…
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