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This article explains how the inherent knowledge limits of large language models drive the rise of Retrieval‑Augmented Generation (RAG), outlines its three evolutionary stages, introduces Agentic RAG and DeepSearch, and discusses the knowledge and ability boundaries that shape future AI search systems.
This article explores the essence of software architecture, tracing its definition, its role in solving division‑of‑labor challenges, and how a pragmatic, balanced design that treats architecture as software design itself can deliver sustainable value while navigating business complexity, team coordination, and evolving requirements.
This article explores how AI infrastructure has shifted from CPU‑centric designs to GPU‑driven architectures, detailing hardware evolution, software changes, and the engineering challenges of large‑model training and inference, while offering practical insights for traditional backend engineers transitioning to AI systems.
This article explores how first‑principle thinking underpins AI agents, traces their development from single‑craftsman tools to enterprise‑level collaborations, outlines core capabilities such as compute, memory, prediction and action, and forecasts future directions like multimodal models, reduced prompting, and extensive data sharing.
This article examines Grok 4’s impressive 45% HLE score—nearly double Gemini 2.5 Pro—explaining its significance in AI evaluation, and then delves into the technical challenges of implementing reliable read‑receipt mechanisms for group messages, covering data storage and acknowledgment strategies.
This article systematically outlines the evolution of Retrieval‑Augmented Generation (RAG) from its naive three‑step pipeline to advanced, modular, and agentic architectures, highlighting each generation's motivations, core features, advantages, drawbacks, and practical implementation details for large language model applications.
This article provides a concise, developer‑focused overview of emerging AI concepts—including AIGC, multimodal models, Retrieval‑Augmented Generation, intelligent agents, Function‑Calling, and the Model Context Protocol (MCP)—explaining their core principles, differences, and how they interrelate to enable advanced AI applications.
Payment systems serve as the essential bridge linking consumers, merchants, and financial institutions in e‑commerce, handling everything from transaction processing and security compliance to multi‑channel payment management, order settlement, refunds, and detailed architecture design that evolves from closed internal apps to open, scalable micro‑service platforms.
This article explores how GPUs evolved from graphics accelerators to general‑purpose processors for AI, detailing the CPU‑GPU heterogeneous architecture, the CUDA programming workflow, compilation into fat binaries, kernel launch mechanics, hardware components, and the differences between SIMD and SIMT models, with performance comparisons and code examples.
This article surveys the rapid evolution of large language models from the Transformer breakthrough to trillion‑parameter capabilities, explains key techniques such as self‑attention, MoE and KV‑Cache, explores practical aspects like temperature tuning, sales AI applications, and compares private versus cloud deployment strategies for enterprises.
