Vanilla Java

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 example, imagine the roles you would wish to have, how much of that could you be doing now without being asked, possibly in your own time? I am not suggesting doing more unpaid work, rather extending the work you have to benefit your career. Ways to extend your current role to help you grow Teach others. Often, you don’t really understand something until you have had to explain it to someone else. ...

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 Single-File Ratio AsciiDoc: 1.023x, HTML: 1.26x (due to tables and code blocks) Markup share of file content Markdown 0.9%, AsciiDoc 1.3%, HTML 9.9% - a 10x overhead for HTML Read/write Assessment In terms of readability, both Markdown and AsciiDoc are good. HTML has far more tags, which add noise. One of the most common tags is <code> some code </c...

Optimising your memory allocations in Java could make far more difference than your choice of Garbage Collector and may even change which is the best garbage collector. In this post I look at a simple event to response latency benchmark, MarketDataSnapshot to NewOrderSingle at 50K/s for 30 minutes using JLBH to test Chronicle-FIX. The goal is to compare a system which is doing redundant work (in this case logging each message using SLF4J), compared with not logging (Chronicle-FIX records every message internally using Chronicle Queue) and how this changes the choice of Garbage Collector For the p99 (worst 1 in 100) the choice of Garbage Collector makes a different on par with optimising how loggin is done However, for the p99.99 (worst 1 in 10,000) optimsing how the logging is done is orders of magnitude more signifciant than the choice of Garbage Collector Unoptimised Benchmark This takes the optimised benchmark and adds one SLF4J log line of just ...

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 write business logic, it would need the code to be mostly complete examples, and then it could write variations on that. If you were starting, it would be better to either; a) get it to write something functionally correct with the expectation you would rewrite it again manually, or b) write the code yourself and use AI to assist you in improving it. The AI Benchmark Trial I ga...