InfoQ Homepage Development Content on InfoQ
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/articles/ai-speed-context-store-architecture/en/smallimage/ai-speed-context-store-architecture-thumbnail-1783673492911.jpg)
Comprehension at AI Speed: Building a Context Store for Evolutionary Architecture
AI makes the first 80% of development feel fast, but hides architectural complexity until it's too late. To prevent system instability, engineering leaders must shift from raw throughput to systemic comprehension. By unifying spec-anchored SDD, TDD, and automated fitness functions into a repo-bound "Context Store," teams can ensure AI agents and human reviewers evolve code safely.
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/articles/aws-multi-region-signing/en/smallimage/aws-multi-region-signing-thumbnail-1783602485181.jpg)
Removing a Hidden Round Trip from a Multi-Region AWS API
When a series of regional outages forced a rethink of a multi-region AWS API, the team discovered that an obstacle to global failover was hiding in plain sight: a pre-flight discovery call baked into every client session years earlier as the only available option. This article describes what it took to remove it, and what the rollout actually cost.
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/articles/multi-region-latency-cost-tradeoffs/en/smallimage/multi-region-latency-cost-tradeoffs-thumbnail-1783416229477.jpg)
Trade-Offs in Multi-Region Architectures: Latency vs. Cost
Adding cloud regions changes latency and cost in ways simple math can't capture. This article presents a framework from multiple launches: decompose your latency budget before committing to infrastructure, choose deployment patterns by consistency and traffic profile, and optimize before expanding. A phased approach cut latency 35% through routing alone, before a new region brought it under 60ms.
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/articles/android-beat-aligned-mobile-audio-streaming/en/smallimage/android-beat-aligned-mobile-audio-streaming-thumbnail-1783408153943.jpg)
Beat-Aligned Mobile Audio Streaming with Virtual Chunks and Native Playback
In this article, I describe the challenges and the design of a React Native real-time mobile beat-aligned playback system for iOS and Android. The system combines personalization with low-latency, and seamless navigation and was the result of careful analysis and experimentation to address strict mobile and network constraints as well as meet user expectations.
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/articles/tradeoffs-event-driven-design/en/smallimage/tradeoffs-event-driven-design-thumbnail-1782458803116.jpg)
Scaling Java-Based Real-Time Systems: the Hidden Tradeoffs of Event-Driven Design
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.
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/articles/security-ai-threat-evolution/en/smallimage/security-ai-threat-evolution-thumbnail-1782202845102.jpg)
Virtual panel: Security in the Machine Age: Expert Insights on AI Threat Evolution
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.
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/articles/rdla-offline-first-reactive-android-data-layer/en/smallimage/rdla-offline-first-reactive-android-data-layer-thumbnail-1781776366032.jpg)
Beyond CLEAN and MVP: Architecting an Offline-First Reactive Data Layer in Android
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.
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/articles/understanding-ml-model-poisoning/en/smallimage/thumbnail-understanding-ml-model-poisoning-1781597719188.jpg)
Understanding ML Model Poisoning: How it Happens and How to Detect it
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.
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/articles/adoption-curve-twenty/en/smallimage/20-birthday-InfoQ-thumb-image-updated-1780931679034.jpg)
The Technology Adoption Curve, Twenty Years On
Today, June 8th, InfoQ celebrates 20 years. This is not a comprehensive history, but a deliberately selective look at the technologies and practices InfoQ identified early, where they sit on the adoption curve in 2026, and how that curve may evolve over the next five to ten years.
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/articles/architectural-change-cases/en/smallimage/architectural-change-cases-thumbnail-1780316814045.jpg)
Architectural Change Cases: a Practical Tool for Evolutionary Architectures
Architectural change cases extend architecture decision record (ADR) thinking by evaluating how decisions may evolve over time. Change cases expose hidden assumptions and help teams estimate the reversibility and cost of change.
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/articles/spark-oom-kubernetes-misconfigurations/en/smallimage/spark-oom-kubernetes-misconfigurations-thumbnail-1780044756757.jpg)
Two Misconfigurations That Caused Spark OOM Failures on Kubernetes
After migrating Spark pipelines to Azure Kubernetes Service, two infrastructure settings interacted destructively: spark.kubernetes.local.dirs.tmpfs=true backed shuffle spill with RAM instead of disk, and a hard podAffinity rule forced all executors onto one node. Together, they caused repeated OOM kills invisible to standard diagnostics.
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/articles/solving-ai-productivity-paradox-test-automation/en/smallimage/solving-ai-productivity-paradox-test-automation-thumbnail-1779953915743.jpg)
The AI Productivity Paradox in Test Automation: Moving beyond Structural Validation to Perception and Intent
The AI productivity paradox states that AI scales whatever abstraction it is built on. If that abstraction is structurally brittle, it scales structural brittleness. This article shows that to build a future of reliable, AI-driven test automation, we must stop scaling DOM-centric abstractions and build a new testing paradigm grounded in perception and intent.