Advanced AI Application Practice
This guide walks you through real‑world OpenClaw scenarios—email monitoring, a 24‑hour enterprise WeChat customer‑service bot, and an AI tutoring assistant—detailing prerequisites, step‑by‑step configuration, code commands, and testing procedures to boost productivity.
This guide walks through a real‑world 500‑person tech company’s OpenClaw rollout, detailing environment requirements, quick Windows/Linux installation, security hardening, multi‑system troubleshooting, Docker/K8s containerization, multi‑model routing, office‑tool integrations, automation scripts, RBAC, performance tuning, and high‑availability configuration, all achievable within 8‑10 hours.
The article walks through a new OpenClaw scenario where users attempt to create a sub‑agent as cheap labor, explains the required /spawn parameters (runtime, agentId, task, label), shows a concrete example command, and discusses why the resulting agent fails to act as intended, offering guidance for non‑IT users.
This article walks through a real‑world case where a teacher's mixed‑format exam papers are automatically converted into a structured template using Openclaw, detailing the training process, results, challenges faced by non‑IT users, and the supportive community built around the tool.
This article explains why Ollama has become popular for local LLM deployment, outlines its core features, and provides a detailed, step‑by‑step tutorial for integrating OpenClaw with Ollama—including model selection, configuration, troubleshooting common errors, and advanced tips for customization and multi‑model switching.
The article explains why manual API documentation drains testers' time, then demonstrates how Trae Skill’s AI‑driven, customizable rules can generate accurate, real‑time interface docs in about ten seconds, complete with examples, curl commands, and reusable configurations.
By training an AI model on normal network traffic, testers can let Fiddler automatically highlight security leaks, API errors, and performance degradation, turning a tedious manual review into a fast, reliable, and intelligent quality‑assurance process.
This guide walks through setting up the OpenClaw AI‑agent runtime on a local machine, configuring Node.js, handling common pitfalls, and finally integrating the assistant with Feishu so it can read files, manage messages, and automate daily tasks.
The article details how an AI‑driven test platform combines large language models with a corporate‑level knowledge graph to automatically generate end‑to‑end API test scripts for complex business flows, overcoming context loss, dependency gaps, and scalability limits of single‑interface generation.
The author reviews optimistic and conservative AI forecasts, argues that enterprise AI adoption will surge, outlines infrastructure bottlenecks, predicts a shift from pure model performance to ecosystem competition, and highlights the rise of world‑model approaches and edge‑side applications for 2025.
