Context & Methodology

First Principles Thinking

I derive conclusions from fundamental truths rather than reasoning by analogy. When everyone copies existing solutions, they inherit hidden assumptions. First principles forces you to ask: what do we know to be true? Then build up from there.

I arrived at the autonomous infrastructure vision independently—self-aware, self-healing, self-governing systems. Then discovered IBM had similar ideas in 2001 (Autonomic Computing Manifesto). This validation pattern recurs: derive from fundamentals, then cross-reference existing work. If you arrive at the same place independently, you understand why it's right, not just that it's accepted.

Mental Models & Synthesis

True understanding comes from synthesizing disparate concepts into a personal, coherent framework rather than adopting pre-packaged abstractions. I build mental models that connect ideas across domains—control theory informs orchestration, type theory informs API design, queueing theory informs load balancing.

Systems Thinking

I dig deep into how systems work. Understanding component interactions at a fundamental level means architectural decisions are grounded in reality, not assumptions. By seeing how components behave in concert, I ensure decisions reflect the emergent properties of the whole rather than isolated optimizations.

Theory of Computation

My work is heavily informed by state machine design and finite automata models. I focus on conceptual clarity, mapping abstract computational theory directly to implementation details.

This rigor ensures that systems like Synkti have a solid foundation in proofs of concept for robust state transitions in volatile environments—backed by simulations and mathematical foundations.

Empirical Validation

Theory without measurement is speculation. I validate assumptions through controlled experiments: A/B testing policies in simulation before production, measuring actual outcomes against predictions, and letting data override intuition when they conflict.

Synkti's simulation engine exists precisely for this: run 243 scenarios across model size × network × volatility × context × quantization. Compare Kuhn-Munkres optimal assignment against greedy baseline. The 46% improvement isn't a claim—it's a measured result, reproducible from the codebase.

This empiricism extends beyond code. Market validation before building. Customer conversations before feature design. The scientific method applied to engineering: hypothesize, test, measure, iterate.

Background

Past consulting work includes embedded async Rust (no_std/Embassy on STM32) and hardware orchestration systems. I've diagnosed and resolved critical issues in real-time systems, including 40% packet loss fixes and Tokio runtime panic resolution through proper async patterns.

For a Bluetooth device testing system, I architected an event-driven library with tokio::select! coordinating four concurrent streams. Resolved critical runtime panics by implementing the "runtime handle" pattern to pass scheduling capabilities explicitly across crate boundaries. Built centralized state management with Arc<Mutex<T>> for thread-safe device tracking.