RRepoGEO

REPOGEO REPORT · LITE

srush/LLM-Training-Puzzles

Default branch main · commit 644127ef · scanned 6/28/2026, 9:32:52 PM

GitHub: 1,180 stars · 73 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /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
3 / 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 srush/LLM-Training-Puzzles, 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

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

OVERALL DIRECTION
  • highreadme#1
    Clarify README's opening sentence to emphasize "puzzles" and "learning"

    Why:

    CURRENT
    This is a collection of 8 challenging puzzles about training large language models (or really any NN) on many, many GPUs.
    COPY-PASTE FIX
    This repository offers 8 challenging, hands-on coding puzzles designed to teach the core concepts of training large language models (or any neural network) on many GPUs, focusing on memory efficiency and compute pipelining.
  • mediumtopics#2
    Add more specific technical topics

    Why:

    CURRENT
    llm, puzzles
    COPY-PASTE FIX
    llm, puzzles, distributed-training, gpu-optimization, deep-learning, memory-management, compute-pipelining
  • lowhomepage#3
    Add the Colab notebook link as the repository homepage

    Why:

    COPY-PASTE FIX
    https://colab.research.google.com/github/srush/LLM-Training-Puzzles/blob/main/puzzles.ipynb

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 srush/LLM-Training-Puzzles
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Accelerate
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Accelerate · recommended 2×
  2. Colossal-AI · recommended 2×
  3. NVIDIA NeMo Megatron · recommended 1×
  4. Microsoft DeepSpeed · recommended 1×
  5. Google JAX · recommended 1×
  • CATEGORY QUERY
    How to optimize large language model training across thousands of GPUs effectively?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA NeMo Megatron
    2. Microsoft DeepSpeed
    3. Google JAX
    4. Hugging Face Accelerate
    5. Meta PyTorch FSDP
    6. Colossal-AI

    AI recommended 6 alternatives but never named srush/LLM-Training-Puzzles. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for hands-on exercises to learn distributed deep learning memory management and pipelining.
    you: not recommended
    AI recommended (in order):
    1. PyTorch Distributed Data Parallel (DDP)
    2. DeepSpeed
    3. Hugging Face Accelerate
    4. PyTorch FSDP
    5. Megatron-LM (NVIDIA/Megatron-LM)
    6. Colossal-AI

    AI recommended 6 alternatives but never named srush/LLM-Training-Puzzles. 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 srush/LLM-Training-Puzzles?
    pass
    AI named srush/LLM-Training-Puzzles explicitly

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

  • If a team adopts srush/LLM-Training-Puzzles in production, what risks or prerequisites should they evaluate first?
    pass
    AI named srush/LLM-Training-Puzzles 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 srush/LLM-Training-Puzzles solve, and who is the primary audience?
    pass
    AI named srush/LLM-Training-Puzzles explicitly

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

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srush/LLM-Training-Puzzles — 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