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Glossary7
Open the full glossary- HomebrewThe de-facto package manager on macOS (and Linux): one command to install, update and remove tools and applications.
- Node.jsThe program that lets JavaScript run outside a browser — and the thing many AI command-line tools are installed and launched with.
- AI agentAn LLM plus context, tools and a loop: the model decides the next step and can actually carry it out.
- HarnessThe layer of code wrapped around a model: assembling context, calling the model, parsing output, running tools, gating permissions, feeding results back.
- MCP (Model Context Protocol)An open standard for exposing tools, resources and prompt templates to AI clients — write the server once, and every MCP-compatible client can use it.
- ObservabilityObservability leaves inspectable records of Agent steps, tools, state, timing, usage, and results.
- Audit logA tamper-evident record of behaviour: when, driven by what input, the agent called which tools with which arguments and got which results — written by the execution layer, not narrated by the model.
Found 33 articles
Ontology Design from Scratch: Building a Reasoning-Ready Business Semantics Layer
Same model, same tools, same data — yet the agent is sometimes right and sometimes wrong. The difference is rarely the model; it is whether there is a shared, computable, constrainable layer of business semantics. This guide starts from zero and ends with a working minimal accounting ontology.
AI Supply Chain Poisoning: When the Risk Hides in Models, Data, and Tools
An AI system's supply chain is far more complex than traditional software: model weights, training data, dependencies, external tools (MCP), and even prompts themselves can be tampered with. This article breaks down five links, why detection is so hard, and five practical defensive principles.
Supply Chain and Third-Party Risk: When Your Attack Surface Is Not in Your Own Building
Much of a modern enterprise's attack surface sits outside its own control: open-source packages, outsourced providers, cloud dependencies, SaaS tools. This article unpacks the risk mechanics of four supply-chain links, shows how attackers borrow a path in, and includes a third-party risk checklist you can use directly.
Build Your First AI Agent in Seven Steps: from environment to a minimal production pass
Adapted from the MIT-licensed awesome-agentic-ai-zh integrated tutorial: one 'paper assistant' project wires up environment setup, your first LLM call, the four-part prompt, tool use, the agent loop with reflection, RAG memory, evals with observability, and the smallest interface. Every step ships minimal runnable code and safety rails; the local Ollama path costs 0 in API fees.
Automated Weekly-Report Generation System
Spending 3-4 hours every week collecting data, organizing progress and writing the report? This article shows how to use AI to pull data automatically from tools like Jira, Salesforce and Notion and generate a structured weekly-report draft, so you spend just 15 minutes confirming it and adding insight. The focus is on designing the balance between integrating data sources and adding human value.
The AI Coding Tools Map: Recognize the Form Factor First, Memorize Names Later
Cursor, Trae, Claude Code, Codex, OpenClaw, Hermes Agent, DeepSeek Harness, WorkBuddy and Doubao Work sorted into three categories by form factor: IDE-embedded, terminal agent, and orchestration platform. For each category: what it gives you, how much control and visibility it takes away, and how people actually mix them in practice — so you can decide which category your first tool should come from.
Three Levels of AI: Chat, Collaboration, and Agent
They are all called AI, yet their capabilities and cost structures differ enormously. This article breaks the tools on the market into three levels — chat, collaboration, and agent — and gives you a decision table to first clarify which level a need belongs to, then talk tool selection and budget, so you avoid spending money on the wrong tool.
A Framework for Choosing AI Tools: Five Questions That Eliminate 90% of Options
Facing a dizzying list of AI tools, trialing them one by one is the most expensive way to choose. This article gives you five screening questions — data sensitivity, error tolerance, integration needs, usage scale, and cost of failure — each mapped to which category of tool to pick, and ends with a decision-flow table that walks you through the whole judgment.
Enterprise Data Security Basics: What Data Should Never Be Fed to AI
Employees pasting customer personal data into free AI tools is one of the most common and hardest-to-prevent data leakage paths in enterprises. This article provides a four-tier data classification standard, a 'never upload' red-line checklist, a comparison of enterprise vs. consumer terms, local deployment options, and a question list for IT and legal teams.
Function Calling 101: Getting an LLM to Actually Use Tools
A clear account of the Function Calling mechanism in mainstream LLM APIs: how to write a tool JSON Schema, how the model decides whether to call a tool, how to use tool_choice for forced calls and parallel calls — with a complete runnable example, plus practical techniques for how the way you write tool descriptions decides call quality.
Git and GitHub for Beginners: Why AI Tools Keep Asking You to Clone
Why every AI programming tool's installation guide assumes you know Git. This article explains what repository, commit, branch, clone, fork, and pull request each mean, how they connect, and why non-programmers need to understand the first three.
Is My Data Safe? What Actually Leaves Your Computer When You Use AI
'Is it safe?' is too blunt a question to ever get a useful answer. This article breaks it apart: the four kinds of things that actually leave your machine when you use cloud tools, why local tools are different, what the sentence 'we don't train on your data' does and does not promise, and three rules you can actually enforce. No legal advice included.
Installation Failures on macOS: Gatekeeper, Quarantine Flags, and Missing Build Tools
When an AI-tool install stalls on a Mac, it's usually one of four things: the wrong chip's build was downloaded, Gatekeeper says it can't verify the developer, the download is flagged as coming from the internet, or the command-line build tools are missing. This article gives you one confirming action for each, in order — including the things no guide tells you, like the 'Open Anyway' button sticking around for only about an hour.
What an Agent Harness Is: The Ordinary Program Wrapped Around the Model
The horse-and-harness metaphor makes the concept clear: the layer of engineering code wrapped around the LLM — assembling context, executing tools, running the loop, managing permissions. It explains why the same model can perform worlds apart under a different harness, and why picking an AI tool is, to a large extent, picking a harness rather than a model.
What Is an AI Agent: From ChatGPT to AI That Acts on Its Own
Break down the essence of AI Agents with four components — LLM, context, tools, and loop — contrast chatbots with Agents, walk through daily, developer, and business examples plus real failure modes, and learn to judge whether your needs actually call for an Agent.
APIs, SDKs and All Those Abbreviations: What Each One Is and When You Will Need It
An API is a published agreement; an SDK is a toolkit in which someone has wrapped the details of calling that API for you. This piece spells out the difference between the two, why some tools require an API key and some do not, the distinction between REST and streaming, and whether you, as a user, ever need to write API calls yourself.
What Is DeepSeek Harness: The Open-Source Agent Runtime Where 'Everything Is a Plugin'
DeepSeek's officially open-sourced agent runtime (npm package @deepseek-ai/dsh) bundles sessions, tools, workspaces, sandboxing, approvals, the agent loop, and dual interfaces, under the design slogan 'Everything is a Plugin'. One article covering what is in the box, what it looks like running, what it is not, and the mindset the developer-preview stage calls for.
Connecting Your AI Agent to Feishu/Lark: A Beginner's First Enterprise Channel
Use Hermes Agent to connect an AI agent into Feishu or Lark: scan a QR code to create the app, a persistent WebSocket connection with no public IP needed, set the allowlist, send the first message. The one channel with every box ticked in the capability matrix — this is the beginner path; deeper configuration links out to the advanced article.
Connecting Your AI Agent to Telegram: The Simplest First Channel
Use the Hermes Agent gateway to connect a terminal-capable AI agent into Telegram: create the bot with BotFather, set the allowlist, install it as a persistent service, send the first message — and understand who can talk to your bot and where to look when something breaks.
Connecting Your AI Agent to WeChat: Capability Boundaries, Account Risk, and the Full Steps
Use the Hermes Agent weixin adapter to connect an AI agent into personal WeChat: iLink QR-code login, tightening the DM policy, collecting user IDs, starting the gateway. Includes the capability matrix, an honest assessment of the terms-of-service risk, and fixes for common errors.
Connecting Your AI Agent to WhatsApp: Two Paths, Bridge and Official Cloud API
Hermes Agent reaches WhatsApp two ways: the Baileys bridge (scan a QR, personal account, risk-control exposure) and Meta's official WhatsApp Cloud API (business account, webhook, no ban risk). This article teaches you how to choose, how to connect, and who gets to talk to your bot.
The First Ten Minutes After Installing an Agent Tool: A Seven-Item Verification Checklist
Just installed Cursor, Claude Code, Codex, OpenClaw, Hermes, or any AI Agent tool? Don't assign work yet. Spend ten minutes on seven verifications: it runs, it reaches the model, it reads files, it writes files, permission boundaries, one real task, and where the logs are. Tool-agnostic.
Your First Agent: A Hello World That Uses Tools
Hand-write an Agent that actually does things, without any framework: it checks the weather, does arithmetic, and loops on its own until the task is complete. A step-by-step breakdown of the six steps of the Agent loop, its termination conditions, and the common pitfalls — once you've written it, you'll understand what frameworks do for you.
Build a Working Prototype Website in a Weekend: From Idea to Shareable Link
To validate a product idea you do not have to wait for engineering capacity. Using AI generation tools like v0, Lovable, and Bolt, this article walks you through converging the idea into a one-page spec, producing the first version, wiring a database and login, deploying to a shareable link, and putting it in front of real users — and spells out which requirements mean you should stop and find people instead.
A Comprehensive Classification Matrix for the OpenClaw Skill Library: 227 Skills × Four Categories × a Full Business-Process Map
A complete classification matrix of the 227 installed skills in our OpenClaw system. Reorganized into four categories: everyday core tools, finance-related, coding-related, and other, with each category further arranged into 10 business stages. Includes skill usage analysis, overlap detection, and trimming recommendations.
Deep Research on oh-my-pi (omp): An Open-Source Terminal AI Coding Agent with 620K Lines of Code, a Full-Stack Dissection of 40+ Model Providers
can1357/oh-my-pi: forked from Mario Zechner's Pi, with 9K+ Stars, 333+ releases, and 150+ contributors. 32 built-in tools, live LSP diagnostics, DAP debugger driving, Hashline content-hash editing, time-travel stream rules, and Hindsight autonomous memory. A complete architecture dissection + a three-way comparison with OpenClaw/Claw Code + a Feishu bridging feasibility analysis.
OpenClaw Creator and Infrastructure Automation: From Full Podcast Workflow to Self-Healing Servers
An in-depth breakdown of the creative tools in Awesome OpenClaw Use Cases (Podcast, YouTube, AI video editing, game development) and infrastructure DevOps solutions (self-healing servers, n8n orchestration), with complete workflow architecture diagrams.
MCP (Model Context Protocol) Ecosystem Explained: The Three Core Primitives of Tool, Resource, and Prompt
A complete guide to Anthropic's MCP protocol: the design principles behind Tools, Resources, and Prompt Templates, practical configuration, and Claude Code integration.
A Deep Dive into Claude Code
From installation and configuration to advanced techniques, fully master the Claude Code CLI tool, actually powered by DeepSeek V4 / ModelStudio, to boost everyday development efficiency.
AI Developer Tools Worth Watching in 2026
An in-depth review of 6 AI developer tools, from code generation to data pipelines, covering each tool's technical architecture, use cases, installation steps, and hands-on experience.
Complete Development Environment Setup on Mac Studio M3 Ultra
Build an AI development environment from scratch on Mac Studio M3 Ultra (512GB RAM): system configuration, Homebrew installation, basic toolchain, and performance tuning.
Open Design Critique Skill Guide
5 dimension expert level design review: philosophical consistency, visual hierarchy, detail execution, functionality, innovation, each scored 0 to 10, with a radar chart and action list as output.
Open Design: Complete Categorized Catalog of 118 Skills
Complete index of the Open Design skill system: 118 skills, 146 design systems, covering presentations, web pages, dashboards, mobile apps, video, documents, and other full-scenario use cases.