Software engineering, AI engineering, and how systems actually behave.
I take things apart until I understand what's actually happening — the mechanism, the assumptions, where it breaks. The subject changes; the way I look at it doesn't. Written and reasoned through by me.
Selected
Designing Deterministic Boundaries Around Probabilistic AI
LLMs are probabilistic components. Reliable systems are built by deciding, explicitly, which parts of the system are allowed to be probabilistic and wrapping everything else in code that behaves the same way every time.
What Is an LLM?
A precise mental model for what a large language model actually computes, why that computation is useful, and where its limits come from structurally rather than incidentally.
Recent articles
Engineering Teams Have Feedback Loops Too
The same feedback-loop analysis engineers apply to systems — latency, signal delay, damping — applies just as directly to the teams that build those systems, and it's a more useful lens than most process advice.
Caching Is a Consistency Problem
A cache isn't a performance feature that happens to need invalidation. It's a second copy of the truth, and every caching decision is really a decision about how wrong that copy is allowed to be, and for how long.
Why Retrieval and Reasoning Should Be Separate
Retrieval-augmented generation collapses two very different failure modes into one system when retrieval and reasoning aren't treated as distinct stages with distinct contracts.
What Is a Vector Database?
What a vector database actually adds on top of raw embeddings — approximate nearest neighbor search at scale — and the trade-offs that come with the word "approximate."
What Are Vector Embeddings?
How text becomes a point in space, why "similar" ends up meaning something precise and measurable, and where that precision quietly breaks down.