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 analyzes how AI‑driven test generation at Kuaishou evolved from an 8% to a 70% success rate, explains why testing is shifting from simple execution to system design, and offers concrete steps for test engineers to upgrade their capabilities and stay indispensable.
Interviews with test engineers across internet, telecom, and tool‑software firms reveal that AI is automating routine test case creation and script generation, pushing developers toward full‑stack roles and shrinking manual testing work, while the remaining value lies in human judgment, risk modeling, and evidence‑based verification.
This article analyzes the two‑layer architecture of Agent Teams, compares leading Chinese governance and execution products, presents real‑world results from Kuaishou's three‑year deployment, and offers a step‑by‑step maturity framework for teams seeking measurable AI‑driven productivity gains.
The WAIC 2026 robot exhibition showcased record numbers of robots and exhibitors, but only a minority demonstrated clear industrial value, while the analysis breaks down four scenarios—industrial, commercial service, special‑emergency, and consumer—to reveal which segments are truly thriving and which remain mere showmanship.
The article explains the concept of SKILL—a rule‑based, evidence‑driven “work instruction package” that orchestrates multiple AI agents such as Claude, Codex, and Cursor through three layers (orchestration, execution, domain) to automate requirement decomposition, code generation, testing, deployment, and monitoring, showing how Quark achieved 3000+ automated tasks with a 75% code‑output rate.
The article analyzes Kuaishou’s four‑stage evolution—from a feasibility‑only LLM prompt to a self‑evolving multi‑agent system—showing how AI‑driven test case generation, knowledge injection, and automated review dramatically improve coverage, adoption, and maintenance efficiency in UI testing.
The article explains the evolution from Prompt to Loop Engineering, outlines the three‑layer nested loop model, details core components such as Spec and Eval, presents production‑grade design patterns, risk controls, and practical steps for building autonomous AI‑driven development loops.
This article outlines a comprehensive enterprise‑grade FDE knowledge system covering AI deployment roles, business insight, large‑model fundamentals, prompt and context engineering, ontology modeling, agent‑based workflows, production‑grade engineering, quality assurance, governance, and organizational asset management.
ByteDance reveals that AI‑generated code now exceeds 50 % of all new code across the group, highlighting massive investment, cultural shifts, and a full‑stack AI ecosystem that turns AI from a productivity boost into a core engineering paradigm.
The article explains that software engineering’s recurring problems stem from three inherent contradictions—state‑space explosion versus human cognition, inevitable iteration versus entropy, and collective production versus information loss—and presents a four‑layer failure model and a concrete first‑principle framework to guide sustainable system design, even in the AI era.
