Author of the rpcx microservice framework, original book author, and chair of Baidu's Go CMC committee.
This guide walks through two practical approaches—Skill and Prompt—to configure OpenClaw for automated RSS aggregation, AI‑driven filtering, summarization, and channel delivery, comparing their setup steps, code examples, advantages, limitations, and when to choose each method.
The article walks through how the author refactored the Go‑based AI assistant GoClaw by leveraging Claude Code's Agent Teams feature, detailing the motivation, architecture comparison, configuration steps, team role assignments, and practical tmux integration to achieve a more modular and reliable agent module.
This article provides an in‑depth technical analysis of GoClaw, a Go‑based personal AI assistant that mirrors OpenClaw's design while introducing a more robust agent loop, reflective reasoning, extensible skill system, sophisticated error handling, and multi‑provider failover, complete with code excerpts, architecture diagrams, and performance trade‑offs.
The article breaks down Claude Code’s six building blocks—Subagents, Skills, Model Context Protocol (MCP), Slash Commands, Hooks, and Plugins—detailing their characteristics, execution methods, programmability, complexity, autonomy, use cases, customization difficulty, persistence, and integration patterns, and concludes with a comparative matrix and key distinctions.
This article shows how developers can generate full‑featured tutorial or marketing videos in minutes by combining Remotion’s AI Skills with Claude Code, covering installation, project setup, natural‑language prompts, code generation, customization options, real‑world use cases, and the broader impact on development workflows.
This article walks through constructing a multi‑step AI comic‑generation agent using the LangGraphGo framework and the GoSkills plugin system, covering architecture design, declarative tool definitions, automatic configuration discovery, parameter conversion, code implementation, common pitfalls, best practices, and performance optimizations.
The article analyzes how Manus, a general‑purpose AI agent, leverages E2B's Firecracker micro‑VM sandbox and self‑hosting deployment to achieve fast startup, full OS capabilities, session persistence, multi‑tenant isolation, and future cross‑OS support, illustrated with real‑world use cases and trade‑off assessments.
The article analyzes the context‑window bottleneck of large language models, compares two architectural strategies—strengthening the model (Gemini Deep Research) and parallel agent decomposition (Manus Wide Research)—and details a wind‑power investment case study, technical implementation, and future directions.
This article explains how the ManusAgent, built on LangGraphGo, combines a persistent three‑file Markdown workflow with a graph execution engine to solve AI context window constraints, detailing its design, implementation steps, core features, usage scenarios, and a side‑by‑side comparison with a simpler planning agent.
The report analyzes the rapid emergence of Claude Skills and OpenAI's Agentic Commerce Protocol, detailing their technical architectures, benchmark performance, cross‑platform compatibility, enterprise adoption metrics, security challenges, and strategic implications for businesses entering the agentic AI era.
