Identity & Ethos

Architecting intelligence with taste, architecture, and discipline.

“Compute is merely an amplifier of intent. Without architecture, taste, and discipline, it is only expensive noise.”

I am an AI systems engineer and software researcher building high-performance architectures, autonomous agent loops, and frontier language model tooling. My work bridges the gap between abstract artificial intelligence research and production-grade software engineering.

Rather than treating language models as black-box chatbots, I examine them as stochastic microprocessors: systems governed by memory bandwidth, cache invariance, attention topologies, and deterministic runtime constraints.

When I build, I prioritize lean systems: code that compiles quickly, runs with sub-second latency, respects computational cost, and avoids unnecessary framework bloat.

Core Engineering Pillars

04 Disciplines

Autonomous Agent Architecture

Engineering robust state machines and protocol dispatchers. Moving past fragile prompt chains into deterministic, observable agent loops that can self-correct, execute multi-step tools, and operate reliably in production.

Context Invariance & Token Economics

Pioneering KV-cache re-use strategies and prefix design. Slashing inference latency and API expenditure by treating model context as a warm memory cache rather than an ephemeral buffer.

Vector Topologies & Retrieval

Designing high-recall hybrid retrieval architectures. Evaluating trade-offs between graph-based indexing (HNSW) and inverted centroid clustering (IVF-Flat) under strict RAM constraints.

System Discipline & Zero Cost Overhead

Constructing serverless, edge-deployed pipelines that scale to zero when idle and surge gracefully under load. Clean, minimal dependencies with zero architectural waste.