Zongmin L is a Technology Architect based in Guangzhou with 14 years of engineering experience and a 7+ year focus on web design and development across HTML5/CSS3, mobile web, UX, Node.js/JavaScript, PHP, and Go. He blends hands-on backend expertise—having authored and hardened core libraries like a widely used XSS filter and improved Chinese word segmentation in node-segment—with front-end UX refinements for community projects. At Rabbitpre he leads architecture work while drawing on prior Node.js backend experience at SuperID.me to deliver scalable, secure web systems. His background in product design and technical writing gives him a strong product-oriented perspective that helps translate user needs into robust implementations. Self-described as "The Real AI Engineer," he also experiments with AI-driven solutions and tooling beyond traditional web stacks.
14 years of coding experience
2 years of employment as a software developer
Undergraduate, Business Administration, Undergraduate, Business Administration at Guangdong University of Technology
Sanitize untrusted HTML (to prevent XSS) with a configuration specified by a Whitelist
Role in this project:
Back-end Developer
Contributions:9 reviews, 438 commits, 73 PRs in 10 years
Contributions summary:Zongmin appears to be the primary author of the core XSS filtering logic. Their contributions focused on the initial implementation and subsequent enhancement of the XSS filtering functionality, including defining the whitelist, handling attributes and property values, and addressing edge cases. The user also implemented several tests to ensure the effectiveness of the XSS filter.
Contributions:84 commits, 1 PR, 20 pushes in 4 years
Contributions summary:Zongmin primarily contributed to the core logic of the node-segment library, with a focus on improving Chinese word segmentation. Their work included refining the Tokenizer component by adjusting word selection algorithms, optimizing name recognition (ChsName) to improve accuracy, and incorporating enhancements like URL recognition. Furthermore, the user refactored and restructured code elements to streamline overall functionality.
nlpnode-js
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