Neo Kim writes The System Design Newsletter, which explains software architecture and AI engineering through concepts and case studies. His educational work also includes a system-design interview cheat sheet and a collection of learning resources.[1][2][4]
Start with System Design Interview Cheat Sheet →
Contributions
Explaining software architecture
Kim’s newsletter aims to help developers understand system design, prepare for interviews and learn AI engineering. His interview cheat sheet moves from basic database operations into worked architectures such as a rate limiter, job scheduler, web crawler and chat service. The examples organize a design around requirements, storage and the paths followed by reads and writes. This gives learners a structure for reasoning about a system rather than a list of technologies to memorize.[1][2]
From a query to semantic search
How Vector Databases Work, co-authored with guest writer Maxine Meurer, explains how semantic search retrieves information by meaning rather than exact keyword matches. Text becomes numerical representations called embeddings; a search compares those representations and uses indexes to avoid checking every stored item individually. The article follows the idea into query execution, filtering and operational tradeoffs, including when a general database extension may be sufficient.[3]
Tools, writing & talks
Writing, research & talks
Start here
System Design Interview Cheat Sheetsystemdesign.oneWorked system-design examples organized around requirements, data storage and request paths.
Start with one small architecture, such as the rate limiter, and trace its read and write paths. Basic database and web-service knowledge helps; the opening SQL examples can refresh the database foundations.
Author[2]- LLM Course
x.comA comprehensive course for getting into Large Language Models (LLMs), featuring structured roadmaps and hands-on Google Colab notebooks. It walks learners through the full LLM stack from fundamentals to advanced topics. Ideal for developers and researchers looking to build practical skills in modern AI.
Catalog workOriginal source - How Vector Databases Worknewsletter.systemdesign.one
A guided explanation of embeddings, similarity indexes and production choices for semantic search.
Co-author with Maxine Meurer[3]
Tools
- System Design Academy
github.comA collection of architecture and system-design learning resources.
