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Software Engineering 3.0 Era
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Software Engineering 3.0 Era

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.

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Recent Articles

Latest from Software Engineering 3.0 Era

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Software Engineering 3.0 Era
Software Engineering 3.0 Era
Sep 7, 2026 · Artificial Intelligence

AI Agent Evaluation Guide: Building Observable, Evaluable, Self-Evolving Quality Systems

This comprehensive guide synthesizes 2026 industry practices from Xiaohongshu and Alipay to build production-ready AI Agent evaluation systems, covering metrics (Quality/Cost/Safety), three-tier evaluation granularities, Judge system design, OpenTelemetry-based observability, platform architecture with contract-driven test generation, dual flywheel offline/online loops, and self-evolving prompt optimization — moving evaluation from post-hoc verification to embedded engineering guardrails.

AI Agent EvaluationAgentOpsEvaluation Methodology
0 likes · 37 min read
AI Agent Evaluation Guide: Building Observable, Evaluable, Self-Evolving Quality Systems
Software Engineering 3.0 Era
Software Engineering 3.0 Era
Aug 30, 2026 · R&D Management

AI-Era Quality Shift-Left: JD Health's 6-Phase AI Agent System Cuts Production Bugs 65%

JD Health's Li Xun reveals how 80% of production issues originate in requirements, costing 100x more to fix later, and demonstrates a six-phase AI agent system that intercepts 70% of defects at requirements stage, cuts test design time by 50%, and reduces production escapes by 65% through business knowledge-fed AI agents.

AI AgentsAI in TestingJD Health
0 likes · 21 min read
AI-Era Quality Shift-Left: JD Health's 6-Phase AI Agent System Cuts Production Bugs 65%
Software Engineering 3.0 Era
Software Engineering 3.0 Era
Aug 25, 2026 · R&D Management

Why 10x AI Coding Speed Doesn't Scale: 5 Steps to Organizational Throughput

Despite 10x AI coding speed gains, organizational delivery remains stagnant due to unaddressed bottlenecks in handoffs, verification, and context sharing; the article outlines a five-step framework from ByteDance TRAE to transform individual AI productivity into organizational throughput via delivery contracts, shared memory, process loops, and evidence-based verification.

AI codingAI-Assisted DevelopmentByteDance TRAE
0 likes · 17 min read
Why 10x AI Coding Speed Doesn't Scale: 5 Steps to Organizational Throughput
Software Engineering 3.0 Era
Software Engineering 3.0 Era
Aug 22, 2026 · Artificial Intelligence

Codex Harness: How OpenAI Built an Embeddable Agent OS in 135 Rust Crates

This article dissects OpenAI's Codex Harness, a 146K-line Rust workspace of 135 crates that powers ChatGPT, CLI, and IDE extensions as an embeddable agent platform, detailing its agent loop, context engineering, JSON-RPC protocol, multi-OS sandboxing, and multi-agent architecture with measurable benchmark gains on ARC-AGI-3.

Agent ArchitectureAgent LoopCodex Harness
0 likes · 33 min read
Codex Harness: How OpenAI Built an Embeddable Agent OS in 135 Rust Crates
Software Engineering 3.0 Era
Software Engineering 3.0 Era
Aug 16, 2026 · Artificial Intelligence

How Cordis Enables DeepSeek Harness’s Plugin Architecture – A Deep Dive

This article examines how the 2,000‑line Cordis micro‑kernel underpins DeepSeek Harness’s “everything is a plugin” architecture, detailing its five plugin primitives, six architectural practices, vendor‑embedding strategy, typed events, service injection, effects, and runtime self‑modification, and evaluates the resulting modularity, replaceability, hot‑plugability, extensibility, testability, evolvability, and auditability.

Agent FrameworkCordisDeepSeek Harness
0 likes · 24 min read
How Cordis Enables DeepSeek Harness’s Plugin Architecture – A Deep Dive
Software Engineering 3.0 Era
Software Engineering 3.0 Era
Aug 15, 2026 · Artificial Intelligence

DeepSeek Harness’s Five Innovations and What They Reveal About Next‑Gen AI Infrastructure

DeepSeek Harness, an MIT‑licensed open‑source agent framework, introduces five innovations—self‑modifying runtime, event‑sourced replayable sessions, heterogeneous sub‑agent protocols, portable sandbox with fail‑closed design, and strict engineering discipline—that shift focus from model capability to execution reliability, enabling controllable, auditable, and scalable AI agents for production.

Agent OSDeepSeek HarnessEvent replay
0 likes · 13 min read
DeepSeek Harness’s Five Innovations and What They Reveal About Next‑Gen AI Infrastructure
Software Engineering 3.0 Era
Software Engineering 3.0 Era
Aug 13, 2026 · Industry Insights

Why Not Using AI Is the Biggest Cost for Companies

The article argues that avoiding AI incurs hidden costs far beyond money—lost time, missed opportunities, talent shortages, and weakened competitiveness—by showing how AI reshapes cost structures, delivers exponential business value, and creates efficiency, talent, and capital gaps across industries.

AI adoptioncost analysisdigital transformation
0 likes · 16 min read
Why Not Using AI Is the Biggest Cost for Companies
Software Engineering 3.0 Era
Software Engineering 3.0 Era
Aug 13, 2026 · Artificial Intelligence

Experiment Shows AI Nearing Fully Autonomous Testing—Implications for Future Software Development

In a recent experiment, the author gave only a prompt to OpenCode, an AI system that autonomously planned six testing tasks, performed analysis, designed 46 test cases, generated and executed scripts—fixing errors on the fly—and produced a complete test report within about 26 minutes, highlighting the imminent shift toward AI-driven autonomous testing and prompting questions about future software development processes and organizational structures.

AI testingAI‑driven testingOpenCode
0 likes · 3 min read
Experiment Shows AI Nearing Fully Autonomous Testing—Implications for Future Software Development
Software Engineering 3.0 Era
Software Engineering 3.0 Era
Aug 11, 2026 · R&D Management

How Kuaishou Crossed Three Chasms and Discovered Four Rules to Build an AI Productivity System

Kuaishou’s analysis reveals that increasing developer count reduces AI efficiency gains, identifies three layers of human friction, three organizational gaps, and four AI‑native principles, and demonstrates a four‑quadrant framework that boosted AI code generation and demand delivery by up to fourfold.

AI adoptionAI productivityAI-native
0 likes · 12 min read
How Kuaishou Crossed Three Chasms and Discovered Four Rules to Build an AI Productivity System