I don’t optimize for tools. I optimize for systems that work under real constraints.
The work I do spans software, applied AI, and experimental systems. Instead of anchoring on specific technologies, I focus on capabilities that transfer across domains, tools, and problem spaces.
Mapping complexity to invariants.
Systems Thinking & First-Principles Engineering
I approach problems by breaking them down to fundamentals, constraints, and invariants before thinking about implementation. This helps avoid fragile solutions and ensures systems behave predictably outside ideal conditions.
AI-Driven Decision Systems (Applied)
I design AI systems to support real decision-making, especially where uncertainty is high and outcomes matter. My focus is on structuring inputs, understanding biases, and balancing model output with human judgment rather than replacing it.
Full-Cycle Product Building
I take ideas from ambiguity to working systems, handling the trade-offs between scope, time, technical debt, and clarity. This includes early validation, architecture decisions, iteration, and shipping under imperfect conditions.
Thinking through constraints on paper first.
Experimental R&D and Prototyping
I’m comfortable working in uncertain problem spaces where requirements are incomplete and failure is part of the process. I use rapid experimentation to test assumptions, learn from constraints, and refine systems through iteration.
Technical Review & Advisory
I review ideas, architectures, and early systems for feasibility and risk, helping founders identify blind spots before they become expensive mistakes. This work is focused on clarity, long-term viability, and realistic execution paths.
Tools change quickly. The capabilities above shape how I choose and adapt to them depending on the problem at hand.