Understanding Adaptive Optimization
Technical deep dives, simulations, and practical guides on multi-armed bandits, adaptive optimization, and real-time decisioning systems.
The Hidden Costs of A/B Testing at Scale
Why traditional A/B testing breaks down under real-world conditions — cross-test contamination, temporal drift, and the statistical burden of parallel experiments — and how adaptive algorithms offer a fundamentally better model.
8 min read
A Developer's Guide to Multi-Armed Bandits
What multi-armed bandits are, how the core algorithms work — from Epsilon-Greedy to Thompson Sampling to contextual and adversarial policies — with Python implementations and practical guidance on when to use each.
14 min read
Distributed Bandits in Production: Theory, Architecture, and Trade-offs
What happens when you take a multi-armed bandit — designed for a single agent pulling one lever at a time — and run it across a fleet of servers? A deep dive into staleness, delayed rewards, and the engineering that makes distributed bandits work.
18 min read