Law Zava
Systems Engineering and Technical Leadership
I build reliable infrastructure, reduce platform cost, and lead small engineering teams that ship at the pace of much larger ones.
What I Focus On
Systems Performance
Binary size, tail latency, memory predictability. When the runtime is the bottleneck, I reach for Rust, Zig, or C++ — not as a default, but when the numbers justify it.
Distributed State
ScyllaDB, Cassandra, and the operational reality of global state. Failure semantics, consistency trade-offs, and making distributed databases behave predictably under real load.
Engineering Operations
Small teams outperforming large ones. Clear intent, fast feedback loops, and async-first coordination over meetings and headcount.
Privacy and Compliance
Data residency, zero-trust architecture, and AI systems that satisfy regulators without crippling the product. Designed in from the start, not bolted on.
What I Believe
Reliability and cost discipline aren't at odds — they're the same engineering problem. Teams that understand their hardware, shrink their runtime dependencies, and make failure modes explicit end up with systems that are both cheaper and more reliable.
The best engineering organizations run on clear intent and fast feedback, not process overhead. I've seen five people with the right operating model outship fifty without one.
Latest Writing
AI Security: Evolving Threats and Defenses
As of late February 2026, AI security is defined by adaptive attacks and layered, operational defenses.
AI Team Structures That Work
As of mid-February 2026, AI team structures have stabilized into a few workable patterns. This guide explains the models, tradeoffs, and roles that hold up in practice.
AI Cost Trends: Where We're Headed
A pragmatic look at AI cost trends in early February 2026, plus what to do about them.