Content Overview

Here is a content map of the entire site. The articles follow a practical learning sequence and match the four stages in the navigation: beginner, intermediate, advanced, and mastery.

Status description:

Published: The article has been completed and can be clicked to read.
Under planning: The article structure has been established, the main text is still being written, and no reading link is provided yet.

All 22 planned topic articles have now been published.

Beginner stage — complete an AI Coding feedback loop first

Sequence	Articles	Main Focus	Status
1	How to Choose an AI Coding Tool: A Plain-Language Guide for Beginners	Execution, model management, verification	Published
2	Mainstream AI Coding Tools in 2026: Install and Compare Qoder, TRAE, CodeBuddy, Cursor, Claude Code, and Codex	Execution, tool selection, security boundaries	Published
3	AI Coding Quick Start: Develop a web application in 10 minutes	Input, execution, output	Published
4	From Localhost to a Global Website: Deploy with Codex, GitHub, and Cloudflare	Execution, deployment, verification	Published

Stage goal: Understand mainstream AI Coding workflows, install one suitable tool, complete the loop from requirements to manual acceptance, and deploy the result as a globally reachable website.

Intermediate stage - establishing a stable delivery closed loop

Sequence	Articles	Main Focus	Status
1	Let AI Coding Agent truly understand the project: from Rules to AGENTS.md	Input, execution, verification	Published
2	Why Does AI Coding Get Messier with Every Change? From Requirements to Spec-Driven Development	Input, execution, verification	Published
3	How Can AI Coding Maintain Code Quality? From Code Generation to Verification Engineering	Output, verification, quality evaluation	Published
4	MCP Panorama Guide: How AI can securely connect the real world	Execution, permission boundaries	Released
5	Let the AI Coding Agent open the browser and troubleshoot by itself: Chrome DevTools MCP practice	Execution, verification	Published

Main line of the stage: Project rules → Requirement specifications → Result verification → External capability expansion → Browser debugging closed loop.

Advanced stage - accumulation of reusable abilities

Sequence	Articles	Main Focus	Status
1	Context Engineering: How AI Coding Agents Find Their Way Through a Large Codebase	Input, execution, verification	Published
2	From Prompt to Agent Skill: Package Expert Knowledge as a Reusable Capability	Execution, capability reuse, quality evaluation	Published
3	From Rules and Skills to Plugins: Engineering Composable Agent Capabilities	Execution, capability reuse, security boundaries	Published
4	How to Break Down and Collaborate on Complex Tasks: The Multi-Agent Workflow for AI Coding	Execution, verification, collaboration boundaries	Published
5	Multica in Practice: Turning Multi-Agent AI Coding into a Managed Team Workflow	Execution, collaboration boundaries, team reuse	Published

Main line of the stage: Context Engineering → Single Capability Encapsulation → Capability Combination → Complex Task Collaboration → Managed Collaboration Platform.

Master stage - Promoting large-scale team management

Sequence	Articles	Main Focus	Status
1	AI Coding Token Usage: Understand the Burn Before Reaching for ccusage	Usage observation, cost analysis, model management	Published
2	Why Does AI Coding Ask for Your API Key? A Plain-Language Guide to BYOK	Model access, billing ownership, security boundaries	Published
3	Tired of Reconfiguring AI Coding Tools? Build a Local Control Plane with CC Switch	Local configuration, model switching, credential management	Published
4	AI Coding Token and Cost Analysis: What exactly is consumed by a task	Cost analysis, model management, verification, governance	Published
5	AI Coding Costs Out of Control? From API Relays to AI Gateways	Gateway management, model management, cost, security	Published
6	How Good Is an AI Coding Model? From Benchmarks to a Real Project Trial	Model management, quality assessment, cost analysis	Published
7	How Does AI Coding Scale Across a Team? Build Standards That Survive People, Tools, and Time	Canonical project docs, multi-tool adapters, capability-based autonomy, executable guardrails	Published
8	Managing the AI Coding Project Lifecycle: An EMED Framework from Exploration to Delivery	Lifecycle management, cost estimation, risk management, iteration control	Published

Main line of the stage: Visible usage → Understood BYOK → Understandable local controls → Attributable costs → Controllable calls → Evaluable capabilities → Sustainable team → Manageable lifecycle.

Supporting navigation

Growth Route: Understand the progressive relationship between the four stages.
Capability Map: View the capability structure from seven dimensions: input, execution, output, model, gateway, cost and quality.
Agent Engineering: Go deeper into agent foundations, runtimes, capability orchestration, and production systems.

Unified writing requirements

Every formal article should contain:

A real engineering problem, rather than starting from a conceptual definition.
A set of reusable judgment framework or operating procedures.
A minimal example or project case.
Risks, applicable boundaries and common misunderstandings.
Verifiable reader learning outcomes.