Technology reshapes logistics
This article explains the design of a zero‑code, real‑time data quality alert platform that leverages Binlog‑based ingestion, configurable metrics, automated attribution, and LLM‑driven decision making to provide fine‑grained monitoring, rapid response, and measurable operational benefits across marketing workflows.
This article explains the fundamentals of Retrieval‑Augmented Generation (RAG), introduces GraphRAG as an advanced architecture using knowledge graphs, details implementation pipelines, evaluates performance improvements, analyzes common pitfalls, and outlines future enhancements for enterprise metadata search.
This article details Lalamove's 2025 "Big Cleanup" initiative that identified and removed over 30% of dead Java code and more than 100 obsolete DB fields, describing the tools, SA plugin, BigClean scanner, MyBatis interceptor, and quality‑assurance strategies used to ensure safe, efficient code slimming.
The article details how Huolala’s Dolphin platform engineers large‑model inference for high‑concurrency, long‑context, low‑latency production workloads, achieving 50‑60% GPU cost reduction through systematic resource allocation, model quantization, PD‑separation, speculative sampling, and kernel‑level optimizations while maintaining service stability.
Lalamove tackled the scalability and accuracy challenges of worldwide driver onboarding by designing a layered edge‑cloud AI architecture that combines lightweight mobile models, cloud‑based large‑language and computer‑vision models, OCR, and multimodal LLMs to filter low‑quality inputs, automate identity checks, and reduce manual effort while maintaining data compliance.
This article examines the challenges of manual mobile UI testing, introduces AI‑driven self‑healing techniques that combine multimodal perception, visual models and semantic analysis, and details the architecture, diagnostic workflow, smart popup handling, change‑aware engines, practical results and future directions.
To support HuoLala’s massive driver workflow, the team designed a platform‑wide Todo Center that standardizes tasks, optimizes performance, decouples services, and ensures strong and eventual consistency, while employing traffic‑shaping, asynchronous processing, and robust monitoring to sustain billions of daily queries with low latency.
This article presents a systematic approach to evaluating large language model security by defining threat models, categorizing attack surfaces such as jailbreak and privacy leakage, and describing an automated red‑team platform that generates, mutates, scores, and evolves adversarial prompts to continuously assess model robustness.
Facing the “last mile” challenge of correlating metrics, logs, and traces, the article examines common heterogeneous storage architectures, critiques existing Exemplar implementations, and presents HuoLala’s end‑to‑end solution that treats Exemplar as an independent observable dimension, detailing its data model, SDK integration, collector, and interactive visualization.
The article analyses a recurring crash in an iOS driver app caused by the __dispatch_barrier_waiter_redirect_or_wake function in libdispatch, explains the EXC_BREAKPOINT/SIGTRAP mechanism, compares libdispatch versions, reveals a premature queue release due to non‑atomic reference‑count handling, and proposes replacing GCD‑based barriers with pthread_rwlock_t to eliminate the bug.
