Blog Archive

'Can' vs 'Does'

The difference between a system that can fail and a system that does fail is time. Murphy's Law is not a joke. It is a design constraint. Every moving part you add is another bet against the house, and the house always wins.

Control Repositories

A control repository is a git repository whose contents declare what should be running, intended to be reconciled by an automated controller. It looks like a source repo, but the discipline is different — reviews focus on what the system should be, duplication is often intentional, and the head of main is the state of the platform right now.

Trunk-Based Development

Long-running branches are an anti-pattern. The longer a branch is open, the more expensive the integration becomes, the less informative the diff, and the further the branch drifts from whatever is actually shipping. Trunk-based development is the alternative the continuous delivery community converged on two decades ago, and the evidence for it is overwhelming.

Web3 Is Just Infrastructure With a Hoodie

Everything in Web3 is something you already know. Wallet signing is SSH authentication v2, smart contracts are RPC endpoints with public immutable code, and the trust model is the same "verify, don't trust" principle that drives every good infrastructure system.

"Design Me a Highly Resilient Database"

There is no such thing as a "highly resilient database" in the abstract. The right answer starts with understanding the problem — the data, the product, the failure modes, the regulatory environment — not a product name.

Security Is Infrastructure

Security and infrastructure are not two disciplines that happen to overlap. They are one discipline that companies have artificially separated because org charts demand clean boxes and job postings demand clean titles.

Metrics, Logs, Traces, and Events: What's Actually Different

Four observability signals that get thrown around interchangeably. Understanding what makes each one distinct and where they overlap determines whether your observability stack scales or collapses under its own weight.

Distributed Tracing: A Practical Guide

Distributed tracing captures the complete journey of a single request as it passes through multiple services, enabling latency analysis, error propagation tracking, and root cause analysis across complex architectures.