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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
FEATUREDANALYSIS

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.

Mustafa Eskin·March 2026·8 min read
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A Developer's Guide to Multi-Armed Bandits
GUIDEMarch 2026

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
RESEARCHMarch 2026

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