InfoQ - Development - Articles

Article: Scaling Java-Based Real-Time Systems: The Hidden Tradeoffs of Event-Driven Design

Sagar Deepak Joshi — Tue, 30 Jun 2026 09:00:00 GMT

Event-driven architecture promises scalability, but in Java-based real-time systems the tradeoffs only surface in production. Drawing on a Java/Kafka contact center platform handling 80k BHCC across 10k agents, this article details where the design breaks down—state management, partition limits, deduplication, JVM tuning, cascading consumer failures—and the Redis-backed patterns that fixed each.

By Sagar Deepak Joshi

Article: Virtual panel: Security in the Machine Age: Expert Insights on AI Threat Evolution

Claudio Masolo, Elham Arshad, Sabri Allani, Vijay Dilwale, Igor Maljkovic — Mon, 29 Jun 2026 11:00:00 GMT

This virtual panel brings together AI security experts to examine the evolution of AI-driven threats, from prompt injection and data poisoning to agent abuse and AI-powered social engineering. The discussion explores emerging attack patterns, incident response challenges, and the changes security teams must make as AI systems become more autonomous and integrated into critical workflows.

By Claudio Masolo, Elham Arshad, Sabri Allani, Vijay Dilwale, Igor Maljkovic

Article: Beyond CLEAN and MVP: Architecting an Offline-First Reactive Data Layer in Android

Mervyn Anthony — Wed, 24 Jun 2026 09:00:00 GMT

With the Reactive Data Layer Architecture (RDLA), you establish a clear boundary between public data APIs and private, framework-specific data-source implementations. Your presentation layer operates in a purely reactive manner, observing data changes rather than procedurally querying them. RDLA also simplifies testing by encouraging you to program to interfaces and use clean seeding patterns.

By Mervyn Anthony

Article: Understanding ML Model Poisoning: How it Happens and How to Detect it

Igor Maljkovic — Mon, 22 Jun 2026 11:00:00 GMT

In this article, the author explores data poisoning as a threat to machine learning systems, covering techniques such as label flipping, backdoors, clean-label poisoning, and gradient manipulation. The article reviews real-world incidents, discusses the challenges of detecting poisoned data, and presents practical defenses, tools, and operational practices for securing ML training pipelines.

By Igor Maljkovic