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 examines the evolution from intelligent, assistant‑style testing to fully autonomous, LLM‑driven test agents, outlining four core capabilities, real‑world implementations across unit, API, and UI layers, and the technical pillars that enable self‑learning, self‑healing, and multi‑modal testing.
Huawei has appointed Yu Chengdong as director of its Investment Review Board (IRB), a top‑level decision platform that controls product investment, resource allocation, risk management, and cross‑business coordination, signaling a strategic shift to prioritize AI development and deployment across the company.
Anthropic's Claude Sonnet 4.5 arrives as the strongest coding model yet, delivering over 30 hours of uninterrupted programming, major gains in reasoning, math and agent tasks, safety‑aligned training, new API features, benchmark‑leading performance, and pricing identical to Sonnet 4.
The article explains how massive language models achieve seemingly intelligent behavior through emergence at a critical scale, hierarchical planning, attention-driven global coherence, multimodal understanding, and progressive training techniques that turn simple token prediction into sophisticated reasoning and creativity.
The article dissects six common but harmful mindsets in software development—slacking as philosophy, KPI obsession, hollow testing, distorted agile, reality as an excuse, knowledge hoarding, and resume‑driven tech choices—showing how they erode quality and professional integrity.
The article examines the shift from AI as a passive tool to autonomous AI Agents that decompose objectives, execute tasks, and reshape industries, illustrated by Klarna's customer‑service agent, the AI software engineer Devin, CrewAI‑driven SaaS development, and cross‑application automation with Multi‑On.
The article analyzes how emerging large AI models are moving from simple code copying to intent‑driven programming, examines current tactical uses versus strategic design limits, presents real‑world examples like Vibe Coding, and forecasts both the opportunities and risks for software engineering over the next 2‑3 years.
The article compares Claude Code and Cursor, contrasting their philosophies, product strategies, technical implementations, user‑experience designs, business models, and attitudes toward AGI, to illustrate two distinct visions for the future of AI‑assisted programming and what they mean for developers.
In the age of large‑model AI tools, this essay examines hype‑driven predictions, real‑world experiences, and the evolving roles of programmers, urging developers to balance tool adoption with deep system thinking to retain agency over their future careers.
The article introduces Adaptive Intent-Driven Development (AIDD), a declarative methodology that lets developers state desired outcomes and lets autonomous control loops continuously reconcile reality with intent, illustrated through Kubernetes, SDN, and auto‑scaling examples, and highlights its resilience, productivity, and future AI integration benefits.
