Multi-agent AI systems need built-in accountability. With the right logging and observability setup, when agents fail, you can see what happened and why.
AI-driven infrastructure is non-deterministic. Chaos testing ensures systems maintain intended behavior under stress, improving reliability and safety.
A Kubernetes pod may restart due to an OOMKill when the Java process exceeds the container’s memory limit. JVM memory tuning and correct resource limits prevent crashes.
This article explains a practical design for a LinkedIn-style “People You May Know” system, focusing on real-world tradeoffs, graph embeddings, and low-latency serving.
AI coding tools boost commit metrics, but hide deeper issues. Learn how the SPACE framework reveals real developer productivity beyond traditional DevOps metrics.
The Fact-Based Labeling framework replaces ‘black-box’ flags with machine-extracted facts that trigger structured human questionnaires for consistent content governance.
AI-native platforms embed intelligence into cloud infrastructure, allowing systems to sense events, generate insights with AI, and trigger automated actions in real time.
QA is evolving for AI-driven business, focusing on data quality, model validation, and risk management to ensure reliable, trustworthy, well-governed systems.
Modern Java backend design is evolving from traditional APIs to event-driven architectures, enabling more scalable, resilient, and real-time distributed systems.
Jakarta EE is an open standard for enterprise Java: specs define behavior, APIs expose it, TCK enforces it, and multiple implementations ensure portability.
Twelve LLM prompt injection defenses were tested, and all bypassed. Stop relying on perimeter filters. Strip model privileges and design for containment instead.
Your Angular app isn’t slow your API is. Fix backend bottlenecks like request waterfalls, overfetching, and slow queries before touching a single Angular component.