Looking for a one‑stop platform that deeply merges frontend development with AI? This community focuses on intelligent frontend tech, offering cutting‑edge insights, practical implementation experience, toolchain innovations, and rich content to help developers quickly break through in the AI‑driven frontend era.
This guide introduces Harness Engineering—a paradigm that shifts developers from merely using AI to commanding a team of AI agents—explaining its definition, technical foundations, workflow, real‑world examples, and why it can deliver ten‑fold efficiency gains.
This guide explains the five fundamental concepts—Agent, Harness, Protocol, Session, and Orchestration—through definitions, comparisons, concrete code examples, diagrams, and practical challenges, showing how they interrelate to enable safe and controllable multi‑agent AI workflows.
This guide shows how to replace the manual hand‑copying of design specs and API fields with an engineered AI workflow that reads Figma frames via Figma MCP and API schemas via YApi MCP, then generates reusable React + Tailwind code, type definitions, and integration steps for immediate delivery.
This tutorial walks you through setting up the environment, designing the skill structure, writing the SKILL.md metadata, implementing core Python code, testing locally, and publishing the OpenClaw skill to ClawHub, including best practices for error handling, logging, versioning, and maintenance.
This step‑by‑step tutorial shows how to integrate domestic LLM providers (GLM‑5, GLM‑4.7, GLM‑Flash, Kimi K2.5, DeepSeek, Qwen3‑Coder‑Next, BGE‑M3) into OpenClaw, define model routing, create dedicated controller, writer and coder agents, and run a complete multi‑agent workflow.
This guide outlines a role‑based selection strategy for building a fully domestic OpenClaw model stack, explains common pitfalls when replacing foreign models, details why specific Chinese models fit each role, presents three balanced configurations, and offers a step‑by‑step migration plan.
This guide walks you through twelve concrete configuration techniques—defining the AI's soul, identity, user profile, multi‑agent roles, toolset, memory, error logging, permissions, skills, parallel instances, security, and daily training—to transform OpenClaw from a static tool into a continuously evolving, employee‑like assistant.
This article documents the end‑to‑end process of using Lanhu MCP to convert UI design files into HTML/CSS, covering environment setup, installation, Cursor integration, common errors such as Python 3.13 incompatibility and HTTP 418 responses, and practical work‑arounds when the core API is blocked.
The article examines the shift from Vibe Coding to Agentic Engineering, showing that overloading AI with too many tools confuses it, and argues that developers must evolve from code writers to AI orchestrators who guide and supervise intelligent agents rather than merely issuing commands.
The article proposes a hybrid architecture where Kimi Claw serves as the primary knowledge and search hub while a self‑hosted OpenClaw node supplements it for intranet security and multi‑model experiments, detailing scenarios, data boundaries, step‑by‑step implementation, and model‑tool selection.
