With large models (LLMs) reshaping countless industries, software engineering is leading the charge into the Software Engineering 3.0 era—model-driven development and operations. This account focuses on the new paradigms, theories, and methods of SE 3.0, and showcases its tools and practices.
This article examines how to evolve AI prompt engineering into full‑stack context and environment engineering, detailing six practical design patterns from the Manus system, the limits of Vibe Coding, the Spec‑Driven development workflow, and concrete steps to give models a persistent, controllable world for long‑term tasks.
The 2025 AI+ R&D survey reveals that 89.2% of Chinese software teams have embraced large language models, with 62.8% actively using them, driving vertical adoption, significant cost and productivity gains, while also highlighting eight key challenges and a shift toward AI agents.
Over the past 365 days the author chronicles the rapid rise of AI‑driven development tools, a shift from product‑centric to model‑centric software, and measurable gains in code generation, test coverage and delivery speed, while reflecting on personal milestones, publications, and community building.
This article presents a step‑by‑step practical guide to AI context engineering, introducing the CRISP principle, structured templates, onion‑style hierarchical injection, and detailed personal, conversation, project, and team‑level practices such as context anchors, explicit management, snapshot/rollback, codebase indexing, ADRs, semantic search, and shared knowledge bases.
The article explains how AI assistants often fall short because of missing or poor context, traces the philosophical roots of context, maps its pervasive role in software engineering, and proposes a three‑level context‑engineering framework to turn context into a production asset for large‑model AI.
The article analyzes how leading Chinese tech firms are moving AI agents from simple code‑completion tools to comprehensive digital teammates that assist across the entire software development lifecycle, highlighting real‑world implementations, multi‑agent collaboration, challenges, and future trends.
The article analyzes the evolution from single‑agent coding assistants like GitHub Copilot to multi‑agent, AI‑native development ecosystems, detailing architectural designs, practical techniques, enterprise case studies, tool‑form choices, and a five‑layer capability model that together illustrate how AI is shifting from a mere tool to a collaborative partner in software engineering.
The AI Quality & Efficiency Manifesto, unveiled at QECon, outlines six core principles—collaborative symbiosis, long‑term quality, adaptive evolution, value‑driven creation, data‑centric decision‑making, and open sharing—to guide the future of software engineering in the age of AI.
The article analyzes why most platform engineering efforts fail to deliver value, explains how AI agents transform platform architecture into an operating‑system‑like ecosystem, outlines seven core capability pillars, presents a four‑stage rollout roadmap, and discusses practical challenges, security, observability, and future directions.
The article analyzes how the DORA AI Capability Model can guide enterprises to transform software engineering 3.0 by integrating LLMs across strategy, data, platforms, and processes, offering a step‑by‑step roadmap to develop a sustainable AI‑driven competitive advantage.
