RRepoGEO

REPOGEO REPORT · LITE

CalculatedContent/WeightWatcher

Default branch master · commit a5940f79 · scanned 5/25/2026, 1:17:21 PM

GitHub: 1,747 stars · 147 forks

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 CalculatedContent/WeightWatcher, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    deep-learning, neural-networks, model-analysis, diagnostics, random-matrix-theory, rmt, overtraining, overparameterization, generalization, pytorch, keras, machine-learning
  • highhomepage#2
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://weightwatcher.ai
  • mediumreadme#3
    Refine README opening to emphasize unique methodology

    Why:

    CURRENT
    **WeightWatcher** (WW) is an open-source, diagnostic tool for analyzing Deep Neural Networks (DNN), without needing access to training or even test data.
    COPY-PASTE FIX
    **WeightWatcher** (WW) is an open-source, diagnostic tool for analyzing Deep Neural Networks (DNN) *through their spectral properties*, without needing access to training or even test data.

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 CalculatedContent/WeightWatcher
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ImageNet
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ImageNet · recommended 1×
  2. CIFAR-10 · recommended 1×
  3. COCO (Common Objects in Context) · recommended 1×
  4. GLUE/SuperGLUE Benchmarks · recommended 1×
  5. SQuAD (Stanford Question Answering Dataset) · recommended 1×
  • CATEGORY QUERY
    How can I assess deep neural network model quality without access to training data?
    you: not recommended
    AI recommended (in order):
    1. ImageNet
    2. CIFAR-10
    3. COCO (Common Objects in Context)
    4. GLUE/SuperGLUE Benchmarks
    5. SQuAD (Stanford Question Answering Dataset)
    6. Foolbox (bethgelab/foolbox)
    7. Advertorch (BorealisAI/advertorch)
    8. IBM Adversarial Robustness Toolbox (ART) (IBM/adversarial-robustness-toolbox)
    9. LIME (Local Interpretable Model-agnostic Explanations) (marcotcr/lime)
    10. SHAP (SHapley Additive exPlanations) (shap/shap)
    11. Captum (pytorch/captum)
    12. TensorFlow Explain (TF-Explain) (tensorflow/tf-explain)
    13. PyTorch-OOD
    14. TensorFlow-OOD
    15. Scikit-learn (scikit-learn/scikit-learn)
    16. Albumentations (albumentations-team/albumentations)
    17. NLTK (nltk/nltk)
    18. SpaCy (explosion/spaCy)
    19. Augmentor (mdbloice/Augmentor)
    20. FiftyOne (voxel51/fiftyone)
    21. Label Studio (heartexlabs/label-studio)
    22. Matplotlib (matplotlib/matplotlib)
    23. Seaborn (mwaskom/seaborn)

    AI recommended 23 alternatives but never named CalculatedContent/WeightWatcher. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help diagnose over-training or over-parameterization in deep learning models?
    you: not recommended
    AI recommended (in order):
    1. TensorBoard
    2. Weights & Biases (W&B)
    3. MLflow
    4. Deepchecks
    5. SHAP (SHapley Additive exPlanations)
    6. LIME (Local Interpretable Model-agnostic Explanations)
    7. Scikit-learn's `validation_curve` and `learning_curve`
    8. Matplotlib
    9. Seaborn

    AI recommended 9 alternatives but never named CalculatedContent/WeightWatcher. 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 CalculatedContent/WeightWatcher?
    pass
    AI named CalculatedContent/WeightWatcher explicitly

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

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

    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 CalculatedContent/WeightWatcher. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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CalculatedContent/WeightWatcher — 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