system-design

Java 面试 & 后端通用面试指南，覆盖计算机基础、数据库、分布式、高并发、系统设计与 AI 应用开发

Curated coding interview preparation materials for busy software engineers

The Patterns of Scalable, Reliable, and Performant Large-Scale Systems

Learn System Design concepts and prepare for interviews using free resources.

Free, simple, fast interactive diagrams for any GitHub repository

Learn Domain-Driven Design, software architecture, design patterns, best practices. Code examples included

✍🏻 Source Code Deep Dives, System Design & Engineering Blogs | Halfrost-Field 冰霜之地：源码解析、系统设计与工程实践笔记

Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.

This repo is meant to serve as a guide for Machine Learning/AI technical interviews.

Roadmap to becoming an iOS developer in 2018.

A guideline for building practical production-level deep learning systems to be deployed in real world applications.

《大厂面试指北》——包括Java基础、JVM、数据库、mysql、redis、计算机网络、算法、数据结构、操作系统、设计模式、系统设计、框架原理。

.NET 10, Angular 22, Clean Architecture, Clean Code, SOLID, KISS, DRY, Mediator Pattern, Folder-by-Feature Structure

🧭 Architecture-first system design: 26 bilingual tutorials, 25 architecture templates, and 6 end-to-end cases covering distributed systems, AI-native systems, RAG, coding Agents, and production trade-offs.

Create architecture diagrams from code automatically using large language models (LLMs).

公开的 Java 后端 / AI Agent / 系统设计 / 算法面试复习资料库

Use agent to learn agent - A skeleton course on how to design, build, and operate production AI agents

System Design explained from first principles to senior engineer-level thinking, with a focus on AI-assisted (vibecoding) development. Learn how to reason about systems, understand trade-offs, and validate AI-generated designs beyond just working code.

🧭 给 Claude Code / Cursor / Codex 用的『架构副驾』skill —— 开新项目时用持续深度提问引导你在写代码前想清楚架构(产出架构图 / ADR / 演进路线)。知识源自 awesome-architecture。中英双语。

A curated, hands-on library of notebooks, demos, and resources for AI/ML, Deep Learning, Generative AI, AI-Agents, fine-tuning, and modern tooling.