Vanilla Java

A functional interface can extend a marker interface, functional interface, non-functional interface, and an annotation. A recent X poll suggested 25% of those responding didn't know that.

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 Prompts as Meta-Programming When AI is useful What Generative AI Can’t Yet Do Human in the Loop Conclusion This article is background material for this talk Lessons learnt from founding my own company, and over 30 years hands-on coding Introduction Unlike most deterministic development tools, Generative AI is a productivity...

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 the value of AI-generated documentation I tried to gauge the relative value of AI-generated documentation for my knowledge and set expectations for others. I asked o1 pro to generate user guides in several different ways I considered effective for: a simple class and package a large class and package a complex class and package Then, ...

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 Alice Springs on the map, and it reminded me of the Reverse Baltimore Phenomenon. Generating documentation can give a "sense of completeness" that will likely be a distraction rather than have practical value. The text produced by a generative AI system can superficially convincingly feel "whole", but much of it is fluff that isn’t actually helpful to the reader or an...

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...