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JD‑Tech leveraged large‑model innovations and a novel AnchorDP3 3D diffusion policy to win both stages of the CVPR 2025 dual‑arm manipulation competition, showcasing breakthroughs in synthetic data generation, multimodal perception, and precise trajectory control for embodied AI robots.
This article explains how to implement a lightweight MyBatis interceptor that annotates SQL statements with their mapper ID and execution stack, enabling developers to quickly locate performance bottlenecks during high‑traffic events, and also shows an AspectJ alternative for non‑MyBatis projects.
This article explores how cutting‑edge optimization and simulation techniques empower supply‑chain planning—covering network design, inventory layout, and large‑scale scenario modeling—to reduce costs, improve efficiency, and enhance user experience through fast, scalable algorithms and AI‑driven insights.
This article showcases three JD tech case studies—using large language models for e‑commerce query expansion, applying sparse large models with scaling‑law experiments to improve ad prediction, and building proactive risk‑prevention systems—to illustrate practical AI engineering that drives higher recall, conversion, and system robustness.
This article introduces Taro's open‑source Harmony C‑API solution, detailing its architecture, rich component and CSS support, performance optimizations, and step‑by‑step installation guide that enable developers to build native‑grade Harmony OS applications using familiar web technologies.
This article details how JD Retail's young algorithm engineers tackled a series of AI engineering problems—including advertising image quality assessment with multi‑reward models, large‑language‑model‑driven query expansion, FFT‑and‑RDP‑based model pruning, and agent‑centric reinforcement learning—while sharing practical growth insights and code snippets.
This article examines the common pain points of on‑call debugging, explains how to extract useful information from error screenshots, and presents a Java‑based method call stack tracing tool that filters and visualizes stack frames to quickly locate code origins and improve troubleshooting efficiency.
The award‑winning project from Tsinghua University and JD Retail introduces re‑parameterization model design, cross‑scene adaptive learning, and platform‑aware compression to overcome accuracy‑efficiency trade‑offs in visual deep learning, achieving over 20% accuracy gains and more than 50% inference speedup in real‑world e‑commerce deployments.
The paper "Omniforce" from JD Exploration Research Institute presents a cloud‑edge collaborative AutoML system that uses model distillation, data governance, Bayesian training optimization, and cloud‑edge cooperation to reduce large‑model training costs by 70% and improve inference efficiency by an average of 30%, offering a reusable technical paradigm for scalable AI deployment.
This article examines ClickHouse’s high‑performance OLAP design, covering its MPP architecture, columnar storage, vectorized execution, pre‑sorting, table engines, extensive data‑type system, sharding and replication strategies, as well as its sparse and skip‑index mechanisms that together enable ultra‑fast analytics on massive datasets.
