Microservices introduce distributed-systems complexity most teams underestimate: failures, coordination drag, observability sprawl, and ballooning costs.
Keep GenAI cheap and fast: cache aggressively, route models by confidence, cap tokens and tools, compress context, and monitor cost per successful outcome.
AI Agents perceive, reason, plan, and act autonomously using LLMs. This article breaks down the core components that power every agent and shows you how to build one.
How cloud-native microservices transform insurance analytics by enabling scalability, real-time processing, and seamless modernization of legacy platforms.
Most Android AI features stay single-modal; this architecture fuses vision, text, and sensor inputs to deliver smarter, context-aware, privacy-conscious experiences.
Spring Expression Language is a flexible way to evaluate expressions at runtime. However, in the context of caching, this flexibility can lead to errors.
Reinforcement learning is powerful, but managing thousands of iterations is a nightmare. Here is a practical architecture for building a lightweight experiment system.
This Android recommendation architecture streams events to the backend and uses on-device ranking to deliver fast, resilient, privacy-aware recommendations.
Building a GenAI chatbot for IT support is easy. Building one that actually solves tickets is hard. Here is a blueprint to boost resolution rates using GenAI.