Official JD Tech public account delivering best practices and technology innovation.
The article details JD.com’s AI‑driven supply‑chain innovations, including a billion‑scale pure time‑series model, advanced demand forecasting, intelligent inventory selection, and end‑to‑end allocation algorithms that dramatically improve efficiency, cost, and global supply‑chain transformation.
This article details the open‑source Taro on HarmonyOS framework, covering its evolution, technical architecture, performance optimizations, and cross‑platform development benefits that enable developers to write once and deploy across HarmonyOS, web, and mini‑program ecosystems.
This guide walks you through quickly trying the open‑source JoyAgent‑Genie online, then cloning the repository, running the one‑click start script, and reconfiguring the .env and application.yml files to replace the default GPT‑4.1 model with DeepSeek, all with clear step‑by‑step instructions and screenshots.
This article introduces JD's open‑source JoyAgent‑JDGenie, a lightweight multi‑agent framework, explains its architecture and performance, and provides step‑by‑step instructions for local Docker deployment, configuration, and usage for creating AI‑driven applications.
JD.com’s Genie open‑source AI agent introduces eight powerful capabilities—including plug‑in multi‑agent support, iterative planning, cross‑task context sharing, digital employee enhancements, deep model‑plus‑search, CodeTool for code lifecycle, refined system prompts, and a configurable MCP server—designed to boost intelligent workflow efficiency.
This article presents a thorough evaluation of the open‑source JoyAgent‑JDGenie multi‑agent AI framework, covering its background, test cases for restaurant recommendation and travel planning, deployment steps, performance metrics, and concluding recommendations, highlighting its efficiency, ease of deployment, and result quality.
This article presents a comprehensive migration plan for a legacy MongoDB‑based system, detailing scope analysis, dual‑write implementation, data source selection, bulk and incremental migration strategies, and a three‑pronged online approach (monitoring, gray release, rollback) to ensure a smooth cut‑over without service disruption.
This article describes how integrating causal inference with large language models and Retrieval‑Augmented Generation can automate and explain e‑commerce product pricing, detailing the three‑step workflow, reinforcement‑learning rewards, experimental results, and future directions for end‑to‑end RAG‑LLM training.
Learn how Spring interceptors work, their relationship with WebApplicationContext, various types, configuration methods, execution flow, advanced features, and practical examples such as logging and authentication, plus a comparison with servlet filters and demo results.
JD Advertising built a massive ad‑data warehouse on Apache Doris, reaching nearly 1 PB and 18 trillion rows, then implemented a hot‑cold data tiering strategy—first a lake‑based approach, later a native tiering solution in Doris 2.0—reducing storage costs by 87% and boosting query performance over tenfold.
