Explore Google Gemini 3 API’s architecture, native multimodality, and agentic workflows with a hands-on guide to building a production-ready multimodal AI.
How cloud-native microservices transform insurance analytics by enabling scalability, real-time processing, and seamless modernization of legacy platforms.
Presents a slightly different use of WebSockets — an action is taken at the front-end level when the HTTP session expires, and the back-end signals it.
Server automation is largely solved, but networks remain manual due to multi-vendor complexity. Here’s a Python-based solution to automate network operations.
A clear-eyed breakdown of serverless costs — why they’re hidden, when they make sense, and how to choose between functions and containers before surprises hit your bill.
A new volume type has recently joined the Kubernetes ecosystem: the image volume. This feature promises to change how we manage static data and configurations.
GPU-as-a-Service makes it easier to share accelerators, but it also raises concerns about isolation and security. This introduces a secure Kubernetes architecture.
NetOps teams often face a skills gap when troubleshooting complex infrastructure. This article presents an automation pattern for an AI co-pilot for incident response.
A guide to eight AI agent types with implementations, real-world use cases, and selection framework. Learn about LCM, HRM, LAM, SLM, VLM, LRM, MOE, and GPT architectures.
Active Directory is the heartbeat of the enterprise, and a favorite target of attackers. Here is an architectural pattern for AI-driven anomaly detection and remediation.
Hashing detects tampering, but it doesn't prevent it. Here is an architectural pattern for securing business-critical files using Amazon QLDB and the Symbol Blockchain.
Learn the three production-proven Modern RAG architectures Basic, Agentic, and Multi-Agent RAG and how to choose the right one based on cost, complexity, and scale.
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.