RRepoGEO

REPOGEO REPORT · LITE

openai/grok

Default branch main · commit 3d64b1d8 · scanned 5/18/2026, 6:23:58 AM

GitHub: 4,250 stars · 584 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
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 openai/grok, 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
    Reposition README H1 and add a clarifying sentence

    Why:

    CURRENT
    # OpenAI Grok Curve Experiments
    COPY-PASTE FIX
    # Code for "Grokking: Generalization Beyond Overfitting" (OpenAI Research)

This repository contains the code for the paper exploring the "grokking" phenomenon, where deep learning models generalize long after overfitting on small algorithmic datasets.
  • mediumabout#2
    Add a concise description to the About section

    Why:

    COPY-PASTE FIX
    Code for the OpenAI paper "Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets", exploring the grokking phenomenon in deep learning.
  • mediumtopics#3
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    deep-learning, generalization, grokking, neural-networks, research, machine-learning, overfitting

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 openai/grok
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ResNet
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ResNet · recommended 1×
  2. VGG · recommended 1×
  3. Inception · recommended 1×
  4. EfficientNet · recommended 1×
  5. Vision Transformers (ViT) · recommended 1×
  • CATEGORY QUERY
    How to achieve strong generalization in deep learning models using small datasets?
    you: not recommended
    AI recommended (in order):
    1. ResNet
    2. VGG
    3. Inception
    4. EfficientNet
    5. Vision Transformers (ViT)
    6. BERT
    7. GPT-3/GPT-4
    8. RoBERTa
    9. T5
    10. DistilBERT
    11. Mixup
    12. CutMix
    13. Albumentations
    14. TensorFlow Keras
    15. PyTorch torchvision
    16. GPT-2
    17. Random Forest
    18. GANs
    19. StyleGAN
    20. DCGAN
    21. VAEs
    22. MAML (Model-Agnostic Meta-Learning)
    23. Prototypical Networks
    24. Matching Networks

    AI recommended 24 alternatives but never named openai/grok. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Research tools for understanding generalization phenomena in neural networks?
    you: not recommended
    AI recommended (in order):
    1. TensorBoard
    2. Weights & Biases (W&B)
    3. DeepMind's Launchpad
    4. Captum
    5. InterpretML
    6. PyTorch Lightning
    7. Keras
    8. SHAP (SHapley Additive exPlanations)

    AI recommended 8 alternatives but never named openai/grok. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 openai/grok?
    pass
    AI named openai/grok explicitly

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

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

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

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openai/grok — 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