Cache reads with Redis, use @CachePut for write-through consistency, and prevent stampedes with distributed locks, then prove it works under load with JMeter.
Microservices assume predictable callers. AI agents break this with non-deterministic calls, fan-out, and retries. Here are 5 core assumption breaks and fixes.
Observability costs spiral when teams optimize for visibility, not cost. Fix it by making spend visible, sampling aggressively, and cutting low-value data.
Demonstrates how to expose Spring Boot metrics with Prometheus and build Grafana dashboards to track memory usage and error rates for production-grade Java services.
Distributed AI systems fail faster than humans can respond, making traditional response insufficient. Self-healing systems use telemetry and automation to recover early.
Apereo CAS is one of the largest open-source Spring Boot applications in production. Learn about seven battle-tested patterns from its codebase that will improve yours.
Flutter 3.41 continues to improve rendering efficiency and developer tooling, but building high-performance mobile apps still depends on how developers structure the UI.
Building sub-microsecond HFT dispatchers requires bypassing the operating system. Learn how to achieve zero-copy IPC using C++ lock-free structures and memory mapping.
A real intelligent AI system automatically detects anomalies, irregularities, and potential fraud by leveraging hybrid architectures and explainable predictive models.
Egress — not compute — drives surprise cloud costs. Fix it by designing for data locality, using compression/caching wisely, and actively monitoring data flows.
AI-driven development expands attack surfaces; this article shows how continuous security, zero trust, and runtime enforcement scale DevSecOps in AI pipelines
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