Next.js、React、TypeScript、前端工程化
This collection features articles and community discussions in the Web 开发 space, focusing on real-world engineering scenarios, tool selection, practical paths, and risk boundaries.
The page is continuously updated and serves as a learning roadmap, technology selection reference, and technical review entry point.
"Streaming can make or break an AI app's perceived UX: one character per second feels entirely different from waiting ten seconds for a complete response. This article explains front-end streaming from protocol to rendering: SSE/ReadableStream parsing, smooth React updates, AI SDK useChat, and interruption/error handling."
Type declarations do not validate network, Worker, URL, IndexedDB, or cross-tab messages. This article converts unknown input into canonical internal types and designs schema evolution, dual-read rollouts, and rollback paths for legacy data.
Web Locks can serialize same-origin tabs entering a critical section, but they do not provide fencing tokens for server writes. This article separates browser leader election, persistent generations, and side-effect commits to prevent stale tabs from overwriting current state.
"AI apps are natively multilingual (models know dozens of languages), but 'answering in multiple languages' and 'product internationalization' are different. This article clarifies AI app i18n: UI translation, model output language control, regional compliance, cultural adaptation, multilingual RAG, and a next-intl architecture."
AI accessibility has two layers: the product must be accessible, and AI can empower accessibility. This article covers AI product a11y essentials—semantic HTML/keyboard/ARIA/dynamic content, a11y control for AI-generated UI, AI empowerment (image descriptions/speech/text simplification), and an automated + manual a11y testing workflow.
RAG is not a "single response": retrieval and generation should both stream so users can see progress while they wait. This article covers streaming retrieval, typewriter answer generation, citation rendering, smooth incremental React rendering, and retrieval-to-generation state transitions.
Cloud AI is admired for its capabilities but feared for its privacy risks. Local-first principles and on-device AI offer a new approach: data stays on-device, small local models handle routine work, and cloud enhancement is optional. It covers the principles, AI's role, architecture (local CRDT storage, on-device models, and cloud fallback), and use cases.
Streaming output defines how responsive an AI app feels, but many developers do not understand SSE, vendor data-stream formats, streaming tool calls, or cancellation. This article moves from HTTP SSE through the AI SDK data stream protocol to streaming tool calling and interruptible streams, explaining frontend parsing, backend emission, and common pitfalls.