JD Cloud Developers (Developer of JD Technology) is a JD Technology Group platform offering technical sharing and communication for AI, cloud computing, IoT and related developers. It publishes JD product technical information, industry content, and tech event news. Embrace technology and partner with developers to envision the future.
This article details Taro’s self‑developed iOS rendering layer and TaroUI framework, explaining how a double‑thread pipeline, native‑view‑plus‑layer architecture, rich‑text engine, and extensive rendering optimizations deliver high performance, stability, and cross‑platform consistency for mobile applications.
JoyAI-Image-Edit, an open‑source multimodal foundation model from JD Research Institute, integrates text‑to‑image generation, image understanding, and instruction‑driven spatial editing, achieving world‑leading spatial perception and editing capabilities that unlock new applications across e‑commerce, robotics, 3D reconstruction, and design.
This article provides a comprehensive technical analysis of OpenClaw, an open‑source autonomous‑agent framework that integrates large language models with local system operations through a four‑layer architecture, detailed message‑processing steps, ReAct reasoning loops, security mechanisms, performance optimizations, and real‑world application scenarios.
This article explores the challenges of sudden traffic spikes that overload applications and databases, and presents a step‑by‑step design, implementation, and optimization of a MyBatis plugin that intercepts SQL, applies fingerprint‑based rate limiting, and integrates with Joycode for rapid development and testing.
In the era of hundred‑thousand‑GPU clusters and trillion‑parameter models, conventional monitoring can no longer rely on simple alerts; it must become an observability system that quantifies training and inference performance, breaks data silos across data centers, servers, and networks, and provides business‑aware insights for AI infrastructure.
This article examines the practical challenges of adding AI features to mobile clients, highlighting device fragmentation, performance trade‑offs, user pain points, and a layered approach that balances lightweight models, graceful degradation, and edge‑cloud collaboration to keep the experience smooth for the majority of users.
This comprehensive guide explains how AI agents can be selected, designed, and deployed across the insurance supply chain, detailing their economic impact, technical architecture—including domain‑specific large models, knowledge bases, planning strategies, and reinforcement‑learning loops—and outlines future roadmaps for pricing, fulfillment, and risk‑control automation.
This article examines Deep Research, an AI system that adds autonomous planning and deep reasoning to large language models, enabling them to browse the web, perform long‑chain reasoning, and generate professional, citation‑rich reports for complex tasks such as industry trend analysis and technical competitive research.
This article details JD Retail’s 9N-LLM unified training engine, which integrates TensorFlow and PyTorch across GPU and NPU hardware to tackle the massive data, model size, and reinforcement‑learning complexities of generative recommendation, offering concrete components, performance benchmarks, and future directions.
This article introduces a TypeScript MCP‑Server framework that simultaneously supports stdio, StreamableHttp, and SSE protocols, explains its motivations, core features, directory layout, startup commands, production configuration, key code snippets, and showcases deployment results.
