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what is llm180 fr?

dl-m9/llm180 — explained in plain English

Analysis updated 2026-05-18

23HTMLAudience · developerComplexity · 1/5Setup · easy

tl;dr

A single-page interview prep tool with 180 conceptual questions and 40 coding questions for large language model and AI engineering interviews.

vibe map

mindmap
  root((repo))
    What it does
      Interview prep flashcards
      180 concept questions
      40 coding questions
    Tech stack
      Single HTML page
      Local storage progress
    Use cases
      Practice timed answers
      Track mastery
      Study before interviews
    Audience
      ML job seekers
      AI engineers

Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

what do people make with this?

VIBE 1

Practice answering large language model interview questions out loud within a 60 to 90 second time limit.

VIBE 2

Work through hand written coding problems covering attention mechanisms, optimizers, and classic data structures.

VIBE 3

Track which topics you have mastered and which need more review using the built in progress tracker.

what's the stack?

HTMLJavaScriptCSS

how it stacks up fr

dl-m9/llm180idlehandsdev/pps-watchjensen-yao/mdre
Stars232323
LanguageHTMLHTMLHTML
Setup difficultyeasyhard
Complexity1/55/5
Audiencedevelopergeneralwriter

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

how do i run it?

Difficulty · easy time til it works · 5min

Just open the HTML file directly, or serve it with any static file server.

in plain english

LLM180 is a study tool built for people preparing for machine learning and large language model job interviews, especially internship interviews at Chinese AI companies. It packages interview preparation into a single web page you can open directly in your browser, with no backend or build step required. The page contains two separate question sets: 180 conceptual interview questions covering large language model theory, and 40 coding style questions where you write out an implementation by hand. The conceptual questions are designed to be answered out loud in about 60 to 90 seconds, and each one comes with a full answer explaining the underlying mechanism, how it is actually used in real systems, and the risks involved in evaluating it. The coding questions include the problem, a suggested approach, a reference solution in Python or SQL, complexity analysis, and common mistakes. The interface works like a slide deck. Only one question shows at a time, and you flip through with arrow keys or on screen buttons. You can switch between the two question sets from the top navigation, and reference code stays hidden until you have tried to answer yourself. Your progress, including which questions you marked as not understood, unclear, or mastered, is saved locally in your browser so it persists across sessions. Topics covered include reinforcement learning methods used to fine tune language models, retrieval augmented generation and AI agents, inference systems like KV caching and attention optimizations, training infrastructure such as distributed training frameworks, and multimodal models that combine vision and language. There is also a section aimed at specific companies' hiring processes. The author states this is a training tool built from real interview experiences but cross checked against papers and official documentation, not a research survey, so the standard answers prioritize accuracy over trends.

prompts (copy fr)

prompt 1
Quiz me on reinforcement learning techniques used to fine tune large language models like the questions in LLM180.
prompt 2
Explain KV cache and attention optimizations the way an interviewer would expect me to answer in 90 seconds.
prompt 3
Help me practice implementing multi head attention from scratch in Python with complexity analysis.
prompt 4
Create a study plan for a large model algorithm interview using the topics covered in LLM180.

Frequently asked questions

what is llm180 fr?

A single-page interview prep tool with 180 conceptual questions and 40 coding questions for large language model and AI engineering interviews.

What language is llm180 written in?

Mainly HTML. The stack also includes HTML, JavaScript, CSS.

How hard is llm180 to set up?

Setup difficulty is rated easy, with roughly 5min to a first successful run.

Who is llm180 for?

Mainly developer.

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