Focused on sharing practical tech content about TypeScript, Vue 3, front-end architecture, and source code analysis.
The article reviews three zero‑intrusion, MIT‑licensed i18n toolkits—i18n‑auto‑extractor, auto‑i18n‑translation‑plugins, and i18n‑cli—detailing their features, installation steps, code examples, and scenario‑based recommendations for new or legacy Vue/React projects.
This guide shows how to create a fully functional, self‑hosted n8n workflow automation instance at zero cost by combining Hugging Face Spaces for free Docker hosting with Supabase's free Postgres backend, covering account setup, environment configuration, deployment, and verification steps.
The article explains why DOM screenshot tools like html2canvas are slow, presents benchmark data showing snapDOM being 30‑130 times faster across various scenarios, details snapDOM's native‑rendering implementation using SVG foreignObject, and provides step‑by‑step usage examples.
This article explains how TanStack Query Vue transforms the plain Axios HTTP client into a full‑featured data layer for Vue 3, offering built‑in caching, pagination, request cancellation, optimistic updates, and devtools, with concrete code examples and side‑by‑side comparisons to pure Axios usage.
The article introduces shadcn‑ui‑mcp‑server, a Node.js tool that implements a Model Context Protocol to let AI assistants read shadcn/ui component documentation, source code, and demos, enabling richer code suggestions and full‑page layout generation within editors like VS Code, Continue, and Cursor.
This tutorial walks through creating a eye‑catching like‑button effect using only CSS and a bit of JavaScript, covering parabolic particle motion, an optional oblique bounce, batch generation of random emojis, a +1 number animation, and a brief note on canvas alternatives.
The article examines why traditional try/catch becomes cumbersome in asynchronous JavaScript, then presents three cleaner solutions—a Stage‑1 try‑operator proposal, a custom safeAwait helper, and the community library await‑to‑js—detailing their syntax, benefits, drawbacks, and suitable use cases.
This article explains why the Model Context Protocol (MCP) is needed for LLMs, describes its client‑server architecture, data and transport layers, and provides step‑by‑step Python examples for creating both an MCP server and a client using FastMCP and low‑level APIs.
The article explains how breaking complex LLM tasks into sequential steps—known as prompt chaining—improves answer accuracy, debuggability, flexibility, and enables sophisticated AI workflows such as report generation, chatbots, and content creation using tools like n8n and Ollama.
Version 6.0 of the open‑source ECharts library introduces a sweeping visual redesign, dynamic theme switching, intelligent scatter jitter, new chord diagrams, axis breakpoints, matrix coordinate systems, label overflow protection, upgraded mark layers, and stronger custom series, making both new and existing projects more powerful and easier to develop.
