<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Nicholas Borszich</title><description>Writing about engineering, statistics, and the systems behind them.</description><link>https://nicholasborszich.com/</link><item><title>One A/B testing product, two very different worlds: building for Shopify and the open web</title><link>https://nicholasborszich.com/blog/multiplatform-design/</link><guid isPermaLink="true">https://nicholasborszich.com/blog/multiplatform-design/</guid><description>What it actually takes to run one A/B testing product on both Shopify and arbitrary HTML sites — auth, event ingestion, available data, and why a half-dozen switch statements beat the interface I almost wrote.</description><pubDate>Tue, 12 May 2026 00:00:00 GMT</pubDate></item><item><title>Three boring Claude features inside a stats app — and the patterns that made them ship</title><link>https://nicholasborszich.com/blog/three-claude-features/</link><guid isPermaLink="true">https://nicholasborszich.com/blog/three-claude-features/</guid><description>LLM features that sit quietly inside a SaaS product: a pre-launch reviewer, an async anomaly detector that returns strict JSON, and a cached post-experiment analyzer. The wrapper is 100 lines.</description><pubDate>Tue, 05 May 2026 00:00:00 GMT</pubDate></item><item><title>Bayesian A/B testing in 200 lines of Go: what 5,000 samples actually buys you</title><link>https://nicholasborszich.com/blog/bayesian-ab-testing/</link><guid isPermaLink="true">https://nicholasborszich.com/blog/bayesian-ab-testing/</guid><description>A walkthrough of a production Bayesian A/B testing engine: Beta-Binomial for conversion, Normal for revenue, Monte Carlo sampling, and the LiftDistribution trick that makes credible intervals on dollars interpretable.</description><pubDate>Tue, 28 Apr 2026 00:00:00 GMT</pubDate></item></channel></rss>