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Symmetrical, Multi-Pass, Trace-Driven Compiler Pipeline in Python in two days

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...

Deep Tech Servant Leader in Ten Rules

In an earlier blog on high-performance software engineering , I noted that cruising at maximum height and speed to avoid turbulence requires depending on an experienced pilot—a deep tech servant leader. Beneath that operational framework, however, lies a practical reality: true psychological safety—an absolute necessity if you want team members to fully invest in a project—is not a natural, organic human state. It is an unnatural, synthetic environment relentlessly enforced from the top down. As a result, the project lead must act as both the evangelistic emotional booster and the team's shield against the brutal asymmetry of startup and stakeholder power dynamics. To survive the sheer, un-hedgeable fragility of a 0-to-1 build without suffering decision paralysis, the experienced pilot runs a dual-process system. They project the illusion of a "simple liquid world"—a psychological firewall that allows for rapid decision-making with cold precision—while quietly and constan...

Engineering and math, the final frontier, to boldly go where no human has gone before!

What happens when you are building something so unprecedented that the words to describe it do not even exist yet? Standard Agile assumes product and engineering can just sit in a room, write stories, and align. But you cannot write a user story for the "new new". The vocabulary simply isn't there! If you try to push uncharted concepts through standard communication channels, the sheer friction of translation loss will kill your project. You cannot manage the unexplainable with tickets. The fix is to stop talking and start specifying. Before you build the product, you build the language . A Domain Specific Language (DSL) forces abstract, never-before-seen concepts into a rigorous grammar. It creates a structured medium for ideas that previously had nowhere to go. Once that language is defined, the engineering team's job shifts entirely. They do not need to decipher a deeply complex, uncharted business domain. Their only job is to build a compiler, the engine, that mak...

The Efficient Frontier of High-performance Software Engineering

In a recent post, I outlined the mechanics of what I call " High-Performance Software Engineering ". I made the claim that failure simply isn't an option, and that treating your development process like a hedge fund, or keeping a "six-shooter" of limited fixes in your pocket, is the only way to survive extreme technology projects. But claiming a method works, and actually understanding why it works under the hood, are two different things. It is time to lift the hood a little bit. If we step back and look at this through the lens of systems engineering, specifically looking at latency, throughput, fault tolerance, and something called Little's Law , we can see exactly why this somewhat rigid approach outpaces standard agile. Here is the structural reality of why we manage to not fail. Building engines, not just features In any software process, you have "latency" (how long it takes a single idea to become delivered code) and "throughput"...