What is daily-interview?

datawhalechina/daily-interview — explained in plain English

Analysis updated 2026-07-03

★ 3,669Audience · developerComplexity · 1/5Setup · easy

Mindmap

mindmap
  root((daily-interview))
    Content areas
      Algorithms
      Programming languages
      CS fundamentals
      AI and ML
      System design
      Behavioral interviews
    Languages covered
      Python
      Java
      JavaScript
    Job types
      AI engineering
      Software development
      Front-end and back-end
    Platform
      VitePress
      Chinese language support
      Local search

mindmap root((daily-interview)) Content areas Algorithms Programming languages CS fundamentals AI and ML System design Behavioral interviews Languages covered Python Java JavaScript Job types AI engineering Software development Front-end and back-end Platform VitePress Chinese language support Local search

Click or tap to explore — scroll the page freely

What do people build with it?

USE CASE 1

Review the most common technical interview topics in one structured guide the morning before an interview.

USE CASE 2

Study role-specific sections for AI engineering or general software development job applications at Chinese tech companies.

USE CASE 3

Look up algorithm patterns, database questions, or system design outlines in one place instead of hunting across scattered resources.

USE CASE 4

Contribute interview questions or corrections to the open community guide.

What is it built with?

VitePressMarkdown

How does it compare?

	datawhalechina/daily-interview	bgreenwell/doxx	cbamls/ai_tutorial
Stars	3,669	3,669	3,669
Language	—	Makefile	—
Setup difficulty	easy	easy	easy
Complexity	1/5	1/5	1/5
Audience	developer	developer	researcher

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 5min

The guide is a hosted website, just visit the URL, no installation needed to read it.

So what is it?

Daily Interview is a Chinese-language study guide for people preparing for technical job interviews, maintained by members of the Datawhale open learning community. The goal is not to be exhaustive but to cover only the topics that come up most frequently, so a candidate can review the material in half a day before an interview rather than spending weeks sifting through scattered resources. The content is organized into modules covering algorithm fundamentals, programming languages such as Python, Java, and JavaScript, computer science basics like operating systems and databases, AI and machine learning topics, system design, front-end and back-end development, and soft-skill areas like behavioral interviews and project experience. Each section is aimed at a specific job type, such as AI engineering roles or general software development positions. The guide is published as a website built with VitePress, a documentation tool that compiles text files into a fast-loading site. A major update in August 2025 migrated the site from an older tool, added local search with Chinese language support, and reorganized the content structure. The repository is open to contributions from anyone who wants to add questions, fix errors, or improve explanations.

Copy-paste prompts

Prompt 1

Using the daily-interview guide as a reference, write 10 algorithm questions that commonly appear in Chinese tech company interviews and provide Python solutions for each.

Prompt 2

Generate a half-day review schedule based on daily-interview topics for a Python backend developer role at a Chinese tech company.

Prompt 3

Set up a VitePress documentation site with Chinese language support and local search, similar to the daily-interview project setup after its August 2025 migration.

Prompt 4

Write 5 system design interview questions at the level of Alibaba or ByteDance interviews, with structured outline answers covering scale, storage, and bottlenecks.

Prompt 5

Create a Markdown file in the daily-interview format covering the top 20 SQL query patterns that appear in data engineering interviews.

Frequently asked questions