Writing on experimentation, analytics, entrepreneurship, and career questions.

An honest technical explanation of why current AI agents have no persistent memory, what that costs in production, and what better agent memory architectures would actually need to solve.

How subscription metrics - ARR, ARPPU, retention, LTV, CAC, and unit economics - connect into one system, with formulas, worked examples, and the decisions each metric informs.

A practitioner's guide to the 10 machine learning algorithms that handle the majority of real-world tabular, text, and classification problems, with decision criteria and production tradeoffs.

Data-backed analysis of startup success and failure: modern risk frameworks, market validation, the first-mover myth, Lean Startup evolution, and how AI changed the math.

How confidence intervals, Z-scores, and statistical significance actually work in A/B testing, with worked examples and Python code.

A plain-language explanation of A/B testing fundamentals — control groups, test groups, confidence intervals, statistical significance, and common pitfalls.

Five practical tips on projects, resumes, and recruiter conversations for data science roles.