Writing about AI, technology and commercial growth. From someone with the track record to close it, build the team, and ship it.
Nathan Petralia
Twenty years inside APAC's most demanding digital programs. Estée Lauder, Shiseido, Microsoft, Merkle. Managing Director, Hong Kong. I started building AI products a few years ago, now I work on several open source projects and commercial ones. Check out my GitHub.
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Latest Writing
A complete walkthrough of every Lighthouse bottleneck on a Next.js 16 Vercel site — TBT from 3,020ms to 20ms, LCP from 3.0s to 1.7s — including the config options that don't exist in Next.js 16 and will silently break your build.
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CSS column-count creates a masonry layout in one line. It also silently breaks left-to-right reading order. Here is what is actually happening in the DOM, and a reliable fix that holds up under variable card heights.
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Every AI session starts from scratch. Four tools are racing to solve the AI memory problem - OpenClaw, Hermes, Karpathy's LLM wiki, and a plain Obsidian vault. Here's how they differ and which approach actually survives tool churn.
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I wanted a clean weekly digest for petralian.com without paying for RSS automations. This is the exact architecture we implemented, the issues we hit, and the code patterns that made it reliable.
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WordPress was slowing down the actual writing. Here's why I rebuilt petralian.com on Next.js, how Obsidian now sits at the center of my publishing workflow, and why I decided to open source the whole thing.
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Unit tests catch code failures. They do not always catch AI quality regressions. Here is how to add quality thresholds as a first-class release gate.
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The why was clean. The how had corners. A ground-level account of building petralian.com — the masonry layout that fought back, a 404 page with a working Asteroids game, the TinaCMS newline problem nobody warns you about, and how AI wrote most of it.
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I had a moment a few months ago that I suspect a lot of people in leadership positions will recognize. I was preparing for a client meeting, and I knew I had…
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A practical rollout framework for introducing Gravio across many repos without creating process fatigue, policy confusion, or noisy quality signals.
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