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.
The article explores AI as a brilliant yet unpredictable teammate, urging professionals to shift from pure coding to a conductor role, adopt a generation‑validation workflow, and use an "autonomy slider" to balance creative freedom with human oversight.
The article argues that the AI‑driven Software Engineering 3.0 era has quietly begun, detailing how large‑model agents now understand requirements and business logic, accelerating productivity and reshaping development practices far earlier than the anticipated 2030 milestone.
The article argues that software engineering is fundamentally a discipline for managing complexity, entropy, and information flow, and that understanding its first‑principles—trade‑offs, abstraction, process, feedback, and automation—is essential to avoid ritualistic practices and deliver sustainable value.
The article argues that to unlock the disruptive potential of large language models in software engineering, they should be integrated from the requirements stage through the whole software development lifecycle, providing full semantic context, boosting overall efficiency, and creating an evolving project knowledge graph.
The guide analyzes how three waves of technological revolutions reshape the value of university majors, presents a two‑dimensional value map, ranks S‑, A‑, B‑ and C‑tier professions with concrete salary and school data, and offers a three‑dimensional decision framework plus a four‑year capability‑building plan for 2025 high‑school graduates.
The article evaluates four large language models—DeepSeek, Doubao, OpenAI o3, Gemini 2.5 Pro Think, and Claude Sonnet 4—by prompting them to forecast 2025 Gaokao essay topics, analyses their outputs, compares trends across national and regional exams, and discusses how future results will reveal the most reliable predictor.
An in‑depth analysis of China’s domestic management software market reveals why soaring cloud revenues have not translated into profits, outlines three major market barriers, and proposes a pragmatic “survival triangle” strategy focused on niche tools, SaaS first, and metric‑driven governance.
The article traces software engineering from the waterfall era to agile and DevOps, then explains how large language models reshape development into a "program‑plus‑model" paradigm, outlining new human‑AI collaboration patterns, quality‑assurance challenges, and strategic considerations for the emerging SE 3.0 era.
The article analyzes the convergence of four pivotal AI-driven turning points—cognitive shift, compute breakthrough, data abundance, and engineering tools—and explains how large‑model evolution, autonomous agents, and a model‑driven development paradigm are redefining software engineering for enterprises.
The article examines why Claude 4.0 suddenly generates large amounts of code, evaluates the strategic value of training vertical AI models, forecasts visual large‑model adoption in automated testing, and proposes a phased AI‑engineering capability roadmap for teams of different sizes.
