Equational

Building advanced software systems by trial and error is a fast track to bankruptcy. Today, the cost of generating code has plummeted, but the cost of ensuring trust and verifiability has skyrocketed. To survive this economic squeeze, we can no longer rely on outdated, informal development cycles. Over the last few weeks, I have documented the mechanics, psychology, and processes behind a "zero-fail" methodology I have used for over fifteen years. I call it High-Performance Software Engineering . This post serves as your router and introduction to the series. It breaks down the six foundational pillars of this methodology and points you to the deep-dive articles that explain how to make it a reality. The Engine: A Product and DSL-Driven Process High-Performance Software Engineering completely bypasses the traditional Agile cycle of translating features into stories and stories into code. Because human translation always results in lost intent and bloated latency, we change th...

Introducing "Trusted Engineering Processes" During my CS studies, I spent two summers at the CSEM (Centre Suisse d'Electronique et de Microtechnique) , first improving their Design Rule Checker , then rewriting it. This was my first encounter with a rare breed of individuals: engineers who possess an almost pathological commitment to rigor, whose value lies not just in their raw intellect, but in the absolute predictability and safety of their methodology. Recently, in the context of blogging about how these engineers succeed , I thought it would be good to talk specifically about individuals and team formation for what I call "trusted processes," as collectively, we heavily rely on the innovation built by these individuals. It is, however, not the easiest task! In Deep Tech Servant Leader in Ten Rules , I presented team dynamics from its leadership perspective. How do we present it from a team perspective? Here things become a bit complicated. We have the follo...

In 2011, after fourteen years developing derivative market making software, I took some time for myself, and wrote a prototypal compiler for a reactive trading language. I bring this up because a reactive trading language is a DSL, and I wanted to write something about DSLs in the context of my recent writing about  High-Performance Software Engineering . Therefore I asked Gemini to rebuild something similar as my 2011 effort, with four new twists:  the introduction of profunctors (I had only monads and comonads back then),  the use of combinators to not only to parse the DSL's syntax, but also it's traced execution semantics,  a JIT approach based on working with traces, This is Python. It was F# in 2011. What you have below is Gemini's description of two days of work. And to highlight the still very toy language stage of this language. The goal is show how advanced computer science can be done very quickly in 2026. From a language design perspective, what we h...