Focuses on AI-powered enterprise digitalization, sharing product and technology practices. Covers AI use cases, technical architecture, product design examples, and industry trends. Aimed at developers, product managers, and digital transformation professionals, providing practical solutions and insights. Uses technology to drive digitization and AI to enable business innovation.
The article outlines a systematic reverse‑engineering process for legacy systems that extracts factual system knowledge, organizes it into AI‑consumable context, and integrates the workflow into a continuous delivery loop to improve AI draft accuracy and team cognition.
The article reviews a year of production‑grade AI agent deployments, revealing that engineering challenges—data handling, rule governance, workflow integration, context quality, and clear boundaries—are far more critical than model performance for successful real‑world adoption.
The article explains how the Composed Method Pattern (CMP) and the Single Level of Abstraction Principle (SLAP) can keep AI‑generated code readable, maintainable, and team‑friendly by structuring entry methods as narratives and ensuring each method stays at a single abstraction level.
The article explains that AI accelerates code generation but does not automatically shrink the whole delivery cycle, analyzes why traditional effort estimates become misleading, and proposes a six‑stage delivery‑focused model with practical principles and engineering safeguards for accurate estimation.
The article recounts how a technically‑savvy manager used the AI‑driven Vibe Coding paradigm to create an end‑to‑end system—content generation, AI客服, ordering, shop management and token monitoring—solely through natural‑language prompts, highlighting the shift from traditional engineering to AI‑guided product development.
The article examines how AI can dramatically accelerate code generation for new projects while highlighting that, without robust automated quality‑control mechanisms, this speed boost can jeopardize reliability, especially in legacy systems where incomplete context hampers AI output, and proposes engineering practices to maintain quality.
The article explains how AI‑driven software is shifting from simple functional tools to result‑oriented autonomous systems, and argues that building production‑grade agents requires a dedicated engineering layer—called Harness—that provides task orchestration, state management, tool integration, observability, security, and governance.
This article walks through the complete process of creating a Code Review Skill for AI agents, covering skeleton definition, architecture‑ and coding‑rule derivation, business‑logic checks, unit‑test standards, context routing, token‑consumption analysis, cost‑optimisation tips, and how to extend the pattern to other skills.
The article examines OpenClaw’s rapid rise, outlines concrete security risks such as prompt injection and skill‑market poisoning, and provides a step‑by‑step framework for defining use cases, isolating environments, limiting permissions, and maintaining cost‑effective, long‑term operation.
The article explains how to define, abstract, and evaluate reusable AI Skills, integrate them into collaborative workflows, and evolve them into Sub‑Agents that own continuous responsibilities, providing concrete examples, design principles, metrics, and a step‑by‑step rollout plan for product development teams.
