RRepoGEO

REPOGEO REPORT · LITE

weiruihhh/cs336_note_and_hw

Default branch main · commit cefe7e79 · scanned 6/1/2026, 11:27:43 PM

GitHub: 877 stars · 27 forks

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface weiruihhh/cs336_note_and_hw, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Update the repository's short description

    Why:

    CURRENT
    记录我在cs336学习时的笔记和作业
    COPY-PASTE FIX
    Stanford CS336: Building Large Language Models course notes and completed assignments, covering LLM architecture, optimization (Flash Attention), scaling laws, data cleaning, and RLHF (DPO, GRPO).
  • mediumhomepage#2
    Add the course homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://stanford-cs336.github.io/spring2024/

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface weiruihhh/cs336_note_and_hw
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Dao-AILab/flash-attention
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Dao-AILab/flash-attention · recommended 2×
  2. huggingface/trl · recommended 2×
  3. Natural Language Processing with Deep Learning (CS224N) · recommended 1×
  4. Deep Learning Specialization by Andrew Ng (Coursera) · recommended 1×
  5. Hugging Face Transformers Course · recommended 1×
  • CATEGORY QUERY
    Resources for learning to build large language models from scratch, including practical assignments?
    you: not recommended
    AI recommended (in order):
    1. Natural Language Processing with Deep Learning (CS224N)
    2. Deep Learning Specialization by Andrew Ng (Coursera)
    3. Hugging Face Transformers Course
    4. The Annotated Transformer by Alexander Rush
    5. Let's build GPT: from scratch, in code, spelled out. by Andrej Karpathy
    6. Deep Learning for Coders with fastai & PyTorch
    7. fastai library

    AI recommended 7 alternatives but never named weiruihhh/cs336_note_and_hw. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking practical examples for implementing advanced LLM techniques like Flash Attention and DPO?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library (huggingface/transformers)
    2. `flash-attn` package (Dao-AILab/flash-attention)
    3. `trl` (Transformer Reinforcement Learning) library (huggingface/trl)
    4. `flash-attn` GitHub Repository (Dao-AILab/flash-attention)
    5. OpenAI Cookbook (openai/openai-cookbook)
    6. `trl` library (huggingface/trl)
    7. Lit-GPT (Lightning-AI/lit-gpt)
    8. DeepSpeed (microsoft/DeepSpeed)
    9. Megatron-LM (NVIDIA/Megatron-LM)

    AI recommended 9 alternatives but never named weiruihhh/cs336_note_and_hw. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of weiruihhh/cs336_note_and_hw?
    pass
    AI did not name weiruihhh/cs336_note_and_hw — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts weiruihhh/cs336_note_and_hw in production, what risks or prerequisites should they evaluate first?
    pass
    AI named weiruihhh/cs336_note_and_hw explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo weiruihhh/cs336_note_and_hw solve, and who is the primary audience?
    pass
    AI did not name weiruihhh/cs336_note_and_hw — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of weiruihhh/cs336_note_and_hw. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/weiruihhh/cs336_note_and_hw.svg)](https://repogeo.com/en/r/weiruihhh/cs336_note_and_hw)
HTML
<a href="https://repogeo.com/en/r/weiruihhh/cs336_note_and_hw"><img src="https://repogeo.com/badge/weiruihhh/cs336_note_and_hw.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

weiruihhh/cs336_note_and_hw — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite
weiruihhh/cs336_note_and_hw — RepoGEO report