CI/CD pipelines are essential, but they carry risks if not designed correctly. This post discusses common security mistakes and shares practices to avoid them.
Decouple heavy processing with Spring Boot, Kafka, and WebSockets: AI consumers analyze events asynchronously, while WebSockets deliver real-time insights to users.
Distributed AI systems fail faster than humans can respond, making traditional response insufficient. Self-healing systems use telemetry and automation to recover early.
AI-driven development expands attack surfaces; this article shows how continuous security, zero trust, and runtime enforcement scale DevSecOps in AI pipelines.
Software testing is a feedback system that drives better decisions. Learn how effective feedback, CLEAR principles, and testing levels improve quality and teamwork.
Reactive auto-scaling wastes cloud budgets on idle servers. Learn to replace it with a predictive C# and ML.NET engine that forecasts latency and scales proactively.
Prevent prompt injection in AI agents: default to read-only, require human approval for changes, and authenticate every tool call with end-user zero-trust permissions.
Microservices add flexibility and scalability but increase complexity. Learn key challenges in observability, DevOps, and data management when moving from monoliths.
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.
CI/CD-driven modernization of data platforms, improving release speed, observability, and reliability through automation, parallelization, and job-level telemetry.
Learn to transform Spring Boot REST APIs into an event-driven architecture by utilizing Kafka, RabbitMQ, or NATS to enhance scalability, resilience, and responsiveness.