Technology reshapes logistics
This article explains how frontend developers can use large language models to detect and prevent marketing content violations in WeChat mini‑programs, covering pain‑point discovery, LLM‑driven compliance architecture, prompt optimization, model selection, testing methods, and seamless frontend integration with Feishu notifications.
This article explains how Huolala leverages large‑model AI to overcome high‑volume, complex logistics customer inquiries, detailing specific applications such as intent summarization, emotional support, and vehicle recommendation, and showing measurable improvements in coverage, speed, accuracy, and cost.
This article details Huolala's performance testing platform architecture, covering background challenges, a JMeter‑based solution, distributed agent design, unified logging, plugin management, data collection via Kafka, and future enhancements such as AI integration and improved file distribution, illustrating a comprehensive backend development effort.
This article explains how view‑driven (or BFF) API design replaces bulky data‑centric interfaces with UI‑aligned response models, reducing data redundancy, eliminating logic leakage, and streamlining front‑back collaboration for faster, more reliable product delivery.
This article provides a comprehensive analysis of MSSQL attack vectors—including stored procedures, COM components, CLR, sandbox bypass, triggers, proxy jobs, Kerberoasting, and linked servers—detailing prerequisites, exploitation steps with code examples, and practical mitigation recommendations to harden database security.
This article walks through a real-world investigation of memory leaks in a Rust-based frontend service, detailing common leak scenarios, profiling tools like tokio-console and jemalloc, load testing with k6, and the step-by-step analysis that uncovered regex misuse and cache bugs, ultimately stabilizing memory usage.
Huolala’s Wukong platform tackles the common “technology hype, implementation difficulty” dilemma of generative AI by unifying multimodal enterprise knowledge, enabling dynamic multi‑agent workflows, and providing low‑code tools, observability, and stable deployment across dozens of business scenarios.
This article outlines a practical approach to API anomaly detection, covering background, objectives, a comprehensive framework, feature engineering, threshold profiling, daily operations, detection methods, anomaly types, and response strategies, all driven by big‑data and machine‑learning techniques.
This article explains a comprehensive AI‑powered loss‑prevention framework that automatically identifies financial‑risk scenarios in both existing and new code, integrates model‑based detection into product, development, testing, and release stages, and continuously refines coverage through intelligent monitoring and rule enforcement.
This article describes the design and implementation of an automated H5 performance testing platform that integrates UI automation, mitmproxy traffic capture, waterfall visualization, and Lighthouse scoring to continuously monitor, evaluate, and improve web page performance in mobile applications.
