A practical guide to structured API logging and observability: schemas, tracing, metrics, correlation IDs, pipelines, and cost-efficient practices.
A practical blueprint for deploying autonomous AI agents to production—architecture, safety, reliability, evals, cost control, and ops patterns.
A practical guide to token bucket rate limiting for APIs: concepts, math, parameters, production implementations, and tuning tips.
A practical guide to building React micro-frontends with Module Federation, routing, shared state, deployment, testing, SSR, performance, and security.
A practical guide to API performance benchmarking tools, metrics, and methods with examples using k6, JMeter, Locust, Gatling, wrk, and Vegeta.
How AI code review tools work, key features, pitfalls, and a step-by-step plan to pilot them in your engineering workflow.
