This tutorial demonstrates how to create a functional publish_article tool for AI agents using DeepSeek Harness's defineTool, covering the five core components, output schema versus render separation, execution pipeline hooks, and real-world WeChat API permission constraints.
Based on a hands‑on evaluation, this guide presents nine open‑source AI coding tools—totaling over 650 k GitHub stars—organized by foundation and development stage, explains how each fits into a full coding workflow, and offers practical tips and pitfalls for effective use.
Claude Code 2.1.224 introduces cross‑session messaging, letting independent sessions exchange plain‑text messages via local sockets without server involvement, and the article walks through setup, a full round‑trip demo, message routing details, limitations, and practical use cases.
Anthropic’s new default Auto Mode for Claude Code uses a dedicated classifier that caught 89% of dangerous commands versus 14% for manual approval, a study of 1,053 paid testers showed equal or better safety, fewer harmful actions, and zero successful attacks on Claude models compared with competing systems.
The author consolidates a categorized index of AI programming resources—including official CodeBuddy documentation, open‑source agents, monitoring utilities, workflow tools, code‑review plugins, Skills ecosystem, Git worktree strategies, AI builder feeds, community standards, and a recent research paper—providing practical selection guidance for developers.
This article explains why robust Agent Tools are essential for LLM agents, outlines a five‑stage lifecycle with concrete design principles such as type safety, LLM‑friendly interfaces, OpenAPI integration, self‑healing error handling, human‑in‑the‑loop safeguards, and performance optimizations, and demonstrates their impact through retail and fintech case studies.
This article compares Skill.md and Agent Tools, explaining why AI agents need structured playbooks rather than just toolkits, outlining five key problems Skill.md solves, and showing how progressive disclosure and portable skill packages enhance context, compliance, and efficiency.
This step‑by‑step guide shows how to prepare an Ubuntu (or Mac) VM, install an agent tool, set up Ollama, install OpenClaw via npm, configure the daemon, launch the GLM‑4.7:cloud model, and connect OpenClaw to Telegram for AI‑agent interactions.
This article explores how AutoDev can be implemented as an MCP service and client, enabling any AI Agent tool—such as Cursor, Cline, or GitHub Copilot—to invoke IDE capabilities, integrate external tools like Git, Docker, and Kubernetes, and streamline the full software development lifecycle.
