WeThinkIn/AIGC-Interview-Book

What is this project?

WeThinkIn/AIGC-Interview-Book (Chinese title: 《三年面试五年模拟》— "Three Years of Interviews, Five Years of Practice") is a free, open-source study and career platform for people pursuing jobs in AI — specifically in AIGC (AI-generated content), large language models (LLMs), and AI agents. It positions itself as a comprehensive guide covering interview prep, technical knowledge, career planning, and industry insight.

The core idea: not every new AI technology is worth investing time in. The project claims to filter for technologies that have proven long-term value across industry, academia, competitions, investment, and real applications — and to help readers decide what to learn and why.

What does it contain?

The project is organized into several parts:

1. Core knowledge sections (17 areas)

These cover the technical foundations needed for AI roles, including:

• Interview & job-hunting guides (resume templates, strategies, tips)
• AIGC image creation & AI painting basics
• AI video basics
• Large model (LLM) basics
• AI multimodal basics
• AI Agent basics
• AI digital human basics
• Math & physics fundamentals
• Deep learning & machine learning basics
• Model deployment basics
• Classic models
• Programming basics (Python, C/C++)
• High-frequency interview questions (updated in real time)
• Data structures & algorithms
• Computer fundamentals
• Career open-ended questions

2. 15 curated study routes (hot tutorials)

These are structured learning paths for specific goals, for example:

• 2026 AI Agent interview — 50 questions
• Multimodal algorithm role — 30-day review plan
• LLM interview — top 100 questions
• AI salary & tech-stack map
• Transitioning from traditional CV to AIGC
• 30-day AI algorithm role sprint
• 60-day LLM algorithm role systematic route
• AI Agent engineering interview route
• Model deployment & inference optimization route
• Career transition routes (from traditional CV/NLP, from development roles, for campus hires, for experienced hires)

3. Job direction coverage

The project addresses three broad job categories:

• Algorithm roles: AIGC, LLM, AI Agent, embodied intelligence, traditional deep learning, autonomous driving, computer vision, NLP, machine learning, reinforcement learning, big data, world models, metaverse, AGI
• Development roles: FDE, Python, Java, C/C++, Go, embedded, frontend, backend, testing, operations
• Application roles: product, operations, design, business, marketing

4. AI technology value judgment

A dedicated section where the project evaluates which frontier AI technologies have genuine long-term ("cross-cycle") value, based on technical trends, real engineering deployment, industry changes, job requirements, and community feedback. This is maintained by the lead editor and updated continuously.

Who is it for?

The README states the project is useful for:

• Job seekers targeting AIGC/LLM/AI Agent algorithm or development positions
• Interviewers who need structured question banks
• University students and researchers in related fields
• Junior/mid-level technicians in AIGC, LLM, traditional deep learning, and autonomous driving

Community and contribution system

The project has a structured community contribution system with six contribution types:

1. Interview experience submissions — real interview processes, questions, and reflections
2. Company question banks — questions from big tech, AI startups, state research institutes
3. Chapter corrections — fixing errors in technical content, formulas, links, etc.
4. Chapter content updates — updating models, frameworks, algorithms, deployment practices
5. Course/tutorial building — new learning routes for different backgrounds
6. 2026 recruitment info updates — hiring timelines, official links, job descriptions

There is also:

• A monthly contributor leaderboard tracking contributions by accuracy, completeness, timeliness, and reuse value
• A quarterly AIGC interview trend report summarizing changes in interview questions, company requirements, and learning priorities
• A paid community (AIGCmagic) offering Q&A, referrals, resume review, mock interviews, project guidance, and career planning

Who maintains it?

The project is led by Rocky Ding (主编 / chief editor), described as an AI industry technical blogger, founder of the AIGCmagic community, and a senior AIGC algorithm expert with experience at internet giants, AI unicorns, and research institutes. He has won top competition awards (CVPR, AAAI, Kaggle) and holds patents.

There are six co-editors (副主编):

• 猫先生 (Mr. Cat) — AI algorithm & engineering, focused on model deployment and compute platforms
• 张一凡 — senior AIGC algorithm expert, now researching LLMs and embodied intelligence at a research institute
• 徐晨轩 — PhD student bridging traditional engineering and AI
• 刘一手 — senior algorithm engineer in AI education and finance
• 玉箫然 — senior algorithm engineer at a GPU company, working on LLM deployment and performance optimization
• Plus two additional contributors: Elliot Qi (AIGC algorithm engineer at a big tech company) and 初街夜话 (PhD student in computer vision)

Sponsor note

The README includes a sponsored section for RunningHub API, a commercial platform for calling large models and AI creation tools. It advertises features like video generation APIs, ComfyUI tools, 400+ models, and claims of cost savings. This is a paid sponsor, not part of the core educational content.

Key takeaway

In plain terms: this is a free, community-driven study and interview-prep repository for AI jobs, with a strong emphasis on separating "fad" technologies from durable ones. It combines structured knowledge bases, curated study routes, real interview questions, career guidance, and a contribution system that rewards community members for sharing verified, reusable content. The project explicitly promises to keep updating throughout readers' entire careers.