Observe the world and oneself to seek knowledge, craft strategies and language to build trust, act thoughtfully to return to the source, and ultimately achieve unity of knowledge and action.
This article clarifies the hierarchical and evolutionary relationship among the Zachman framework, TOGAF, and Huawei 4A, showing how each serves as a concrete implementation of the overarching enterprise architecture (EA) mindset and why they complement rather than replace each other.
The article analyzes how the mobile collaboration market has stabilized, while AI is elevating PC‑based office portals into the core of enterprise productivity, creating a dual‑track competition between lightweight desktop solutions for SMEs and integrated, compliance‑focused portals for large government and corporate customers.
The article details Huawei’s 4A enterprise architecture—business, information, application, and technology layers—explaining its design process, governance model, and a real‑world MetaERP implementation that boosted efficiency by 40% and cut response time by half.
The article traces the evolution of enterprise architecture in China through three phases, showcases concrete cases in finance, telecom, state‑owned and internet firms, highlights distinctive local traits such as strong group control and lightweight adoption, and outlines common pitfalls and future trends toward agile, AI‑enabled, ecosystem‑wide architecture.
The article traces enterprise architecture from its 1980s origins addressing IT complexity, through 1990s standardization with TOGAF, early‑2000s maturity, and the current digital‑transformation era emphasizing lightweight, business‑value‑driven practices.
The article explains Enterprise Architecture as a systematic, organization‑wide method that aligns business capabilities, processes, data, applications, and technology with strategic goals, clarifies common misconceptions, and illustrates its practical application through a detailed e‑commerce case study.
The article analyzes how large‑model AI triggers a full‑stack paradigm shift in software engineering—from low‑level hardware and probabilistic mathematics to AI‑native data platforms, agent‑driven development, and result‑based business models—offering a deep, structured breakdown of each layer.
The article reveals that 9.9‑yuan coffee stores are a loss‑leading customer‑acquisition tactic by large chains, breaks down the hidden costs that make the model unprofitable for independents, and offers misaligned‑competition and MVP testing strategies to help small entrepreneurs avoid the inevitable loss trap.
The article examines how individuals leverage AI as an on‑demand team to launch high‑earning products—from a $1 million game built in three hours to a conference paper in seventeen—while highlighting both the massive opportunities, ethical pitfalls, and the importance of precise problem definition.
The 2026 AI Agent market is projected at $18.7 billion with rapid growth, but only programming and customer‑service agents generate sizable revenue, while most other tracks lag behind, and success hinges on clear task boundaries, quantifiable impact, and proper workflow redesign.
