Lean Analytics for Product Engineers

Posted on 2026-10-14 Edited on 2026-10-15 In Product

Goal

Continuing the product engineer career path discussion, this article helps software engineers build a basic product thinking framework:

What validation stages does product innovation usually go through?
What qualitative judgment should be made at each stage?
What quantitative metric can be used to validate that judgment?

This article draws on Lean Analytics, a book on validating innovation that also helps engineers develop product thinking.

Recommended by Kevin Weil, Dan Martell, Fan Bing, Zhang Ximeng, and Ding Lei.

Product Engineering: An SDE Career Path in the AI Era

Posted on 2026-10-12 Edited on 2026-10-15 In Product

Goal

Chinese version: 产品工程：传统 SDE 在 AI 时代的可能职业方向

Explore one judgment: product engineering may be a new direction for traditional SDEs in the AI era.

How AI affects the traditional SDLC
Where SDEs can move their capability focus in the AI era
How to build product engineering capability

Backend Architecture and Technology Stack Map

Posted on 2026-10-09 Edited on 2026-10-15 In BackEnd

Goal

Chinese version: 全栈视角下的后端技术栈地图

Get a fast overview of full-stack architecture, with a focus on backend systems.

A high-level architecture and technology stack map for full-stack projects.
A flash-sale e-commerce case study to connect the architecture with key backend technologies.
Practical references for project technology selection and architecture design.

Agent Engineering: SDK Requirements and Runtime Architecture

Posted on 2026-01-12 Edited on 2026-10-24 In AI , Engineering

Background

Chinese version: Agent 工程：SDK 需求拆解与运行时架构

This article uses an agent SDK as a hands-on introduction to AI application engineering through the following runtime and orchestration requirements:

Runtime execution
API boundaries
Orchestration and execution control
Context management
Tool integration
Model integration
Permissions
Observability

Getting Started with AI: A Four-Layer Technology Map

Posted on 2025-12-07 Edited on 2026-10-24 In AI

Goal

Chinese version: 大模型入门总览：四层技术框架

This guide maps the large language model (LLM) stack across four layers: applications, models, inference infrastructure, and GPU computing. Each section outlines the main concepts and links to explanations, implementations, and engineering case studies.

Layer	Main Question	Core Modules
Application and Product	How do you build applications with models?	Agents and Workflows; Data and Knowledge Integration; Tools and External Systems
Model	How do models work and learn?	Transformer Architecture and Tokenizers; Pretraining; Post-Training; Model Evaluation
Inference and Infrastructure	What systems support training and inference?	Training Systems; Runtime Infrastructure; Inference Systems (the focus here)
CUDA and GPU	How do model operations run on GPUs?	GPU Execution; Memory and Performance