A platform for sharing and discussing tech knowledge, guiding you toward the cloud of technology.
This article explores how DeWu's community activity platform evolved from ad‑hoc implementations to a componentized, event‑driven backend using OOP principles, modular design, rule engines, and observer‑hook mechanisms, while also outlining future AI‑driven optimizations and system‑wide standardization.
This article examines the current low self‑testing rates in the frontend team, explains why automated quality‑assurance techniques like code coverage and E2E testing are essential, outlines a comprehensive technical and operational plan—including tooling, rollout, metrics, and future road‑maps—to boost R&D self‑testing ratios and overall delivery quality.
This article details how replacing Java's native double parsing with the Ryu and FastFloat algorithms, alongside JVM GC tuning, dramatically reduces CPU usage and latency in backend services, offering practical code examples, benchmark results, and actionable optimization strategies.
This article explains how to transform a FastAPI service into an MCP‑compatible tool, enabling AI agents to automatically orchestrate multi‑step test data creation, covering installation, project structure, code annotations, tool configuration, and practical integration with the Cursor platform.
The article examines the drawbacks of writing raw CSS, explains how preprocessors, naming conventions, modular approaches, CSS‑in‑JS, and atomic‑CSS frameworks like Tailwind and UnoCSS address redundancy, maintenance, and scalability, and provides best‑practice recommendations for modern front‑end development.
This article presents a comprehensive AI-driven solution for automatically generating functional test cases, detailing the AI background, design scheme, core components such as PRD parsing, test‑point generation, test‑case creation, knowledge‑base construction, implementation results, and future development directions.
This article explains the need for automated, module‑level code impact analysis in large‑scale projects, outlines a multi‑step technical solution—including dependency‑graph construction, change detection, CI/CD integration, caching and optimization—and demonstrates how it improves testing coverage, system complexity assessment, and overall stability while reducing manual effort.
This article provides a detailed technical walkthrough of the DSearch search engine evolution, covering the RCU‑based 1.0 index architecture, the segment‑merge enhancements in 2.0, and the async non‑blocking graph framework introduced in 3.0, together with performance benchmarks and implementation details.
Meng, a former Tencent and Alibaba engineer, led Dewu’s one‑stop large‑model training and inference platform, cutting integration costs, creating a shared GPU pool and CI/CD pipeline, building a Milvus vector‑database, and driving self‑directed learning that boosted business value, user experience, and set a roadmap for future RAG and cloud‑native optimizations.
The article explains Anthropic’s Model Context Protocol, outlines its architecture, and provides a step‑by‑step guide to creating a Spring AI‑based MCP server in Java—including adding the starter, defining @Tool‑annotated services, packaging the jar, configuring the Cline plugin, and demonstrating advanced tools such as Elasticsearch queries.
