Technology reshapes logistics
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.
This article explains how AI technologies are applied to freight safety, detailing the challenges of traditional controls, the architecture of a real‑time AI safety system, data processing, risk detection, tiered interventions, and the resulting improvements in accuracy and operational efficiency.
LalaEval is a comprehensive human‑evaluation framework that tackles enterprise challenges in building domain‑specific large language models by automating QA set generation, reducing evaluator subjectivity through controversy and score‑fluctuation analysis, and providing extensible, data‑driven metrics for model construction and iterative improvement.
This article explores the practical application of Domain‑Driven Design and hexagonal architecture in a real‑world CRM system, detailing strategic and tactical design, module division, code examples, and a step‑by‑step migration from a traditional layered architecture to a more flexible, maintainable backend solution.
This article reviews the evolution of skeleton screens, explains their underlying principles, shares real-world case studies from Huolala, and provides practical guidelines for creating responsive, fine‑grained, and visually appealing skeleton screens that improve perceived performance without sacrificing actual load speed.
Huolala shares its LaLaEval framework, detailing how large‑model applications are evaluated through defined stages—background analysis, metric design, dataset generation, standards setting, and statistical analysis—while illustrating real‑world use cases in freight and driver invitation scenarios, and outlining future automation prospects.
This article details Huolala’s self‑developed Text‑to‑Speech system, outlining its architecture, the challenges of latency, naturalness, and language support, and the innovative solutions—including streaming synthesis, emotion modeling, and transfer‑learning‑based multilingual capabilities—that deliver more flexible and realistic voice interactions.
This article details Huolala's challenges in risk‑control testing amid rapid business growth, outlines the inefficiencies of manual configuration verification, and explains how a suite of automated tools and a full‑scope interception strategy dramatically improved testing efficiency, data quality assurance, and cross‑team collaboration.
