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.
Microservices solve scalability problems but introduce troubleshooting nightmares. Here is a practical architectural pattern to unify logs, metrics, and traces.
Split control and data planes with versioned snapshots and four contracts — routing, policy, limits, release — for safer rollouts and reliably boring systems.