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This guide introduces the Model Context Protocol (MCP), explains its purpose as a standardized USB‑C‑like interface for LLMs, and provides step‑by‑step Python code to set up SSE‑based MCP servers and a client, plus essential installation details and execution commands.
This article explores why outliers destabilize online A/B tests, explains their statistical definitions, compares trimming and winsorizing techniques, reviews classic and machine‑learning detection methods, and offers practical guidance for applying these approaches to improve experiment reliability.
This article introduces a lightweight SQL‑coloring technique for MyBatis that annotates each executed query with its mapper ID and call‑stack, enabling developers to pinpoint performance bottlenecks instantly without hunting through multiple code paths, and provides both MyBatis interceptor and AspectJ weaving implementations.
This article outlines the motivations, progress, and common issues encountered when upgrading over 100 Java applications to JDK 21, including deprecated APIs, module system constraints, Maven plugin incompatibilities, and GC choices, and provides concrete migration steps, configuration templates, and best‑practice recommendations for a smooth transition.
Taro, an open‑source cross‑platform solution from JD, now supports pure HarmonyOS through its C‑API version, enabling developers to build H5, mini‑programs, RN and native Harmony apps using familiar web technologies, offering high‑performance rendering, extensive component support, and seamless migration of existing Taro projects.
This study investigates the “sandwich” bottleneck observed in residual‑quantized semantic identifiers (RQ‑SID) used in generative search and recommendation systems, revealing that token concentration in intermediate codebooks caused by path sparsity and long‑tail distributions degrades performance, and proposes two effective mitigation strategies that improve efficiency and generalization in e‑commerce applications.
Young JD algorithm engineers share how they solve tough AI problems—from optimizing large‑model training and reward‑model design for ad image generation, to building LLM‑based query expansion, agent evaluation, and model pruning with FFT and RDP—illustrating practical breakthroughs and personal growth in cutting‑edge AI research.
This article outlines the comprehensive big‑front‑end technology stack—including native app development, web frameworks, mini‑programs and cross‑platform solutions—details common network libraries for Android, iOS, HarmonyOS, and browsers, explains the full network request lifecycle, and presents practical performance, security and weak‑network optimization techniques.
This article recounts a JD researcher’s journey from academic data‑mining competitions to building an AI‑driven, LLM‑enhanced anti‑fraud platform that balances detection accuracy, computational cost, and business value in large‑scale e‑commerce advertising.
This article explains the Aho‑Corasick automaton, detailing its construction, fail‑pointer mechanism, linear‑time complexity, and practical Java implementation for highlighting keywords in HTML, demonstrating its power for massive real‑time text search.
