RRepoGEO

REPOGEO REPORT · LITE

labmlai/inspectus

Default branch main · commit 28eed24d · scanned 5/29/2026, 8:47:51 AM

GitHub: 713 stars · 34 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 labmlai/inspectus, 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
  • highreadme#1
    Clarify README's opening statement to emphasize deep learning/LLM visualization

    Why:

    CURRENT
    Inspectus is a versatile visualization tool for machine learning. It runs smoothly in Jupyter notebooks via an easy-to-use Python API.
    COPY-PASTE FIX
    Inspectus is a versatile visualization tool for **deep learning models and Large Language Models (LLMs)**. It provides powerful, interactive visualizations for attention mechanisms, token flows, and distributions, running smoothly in Jupyter notebooks via an easy-to-use Python API.
  • mediumreadme#2
    Add a comparison section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison with Alternatives

Inspectus focuses on interactive, in-notebook visualization specifically for attention mechanisms and token flows in deep learning and LLMs, offering a streamlined API for quick insights. Unlike general-purpose visualization libraries (e.g., Matplotlib, Seaborn) or broad ML platforms (e.g., Weights & Biases, TensorBoard), Inspectus provides specialized, interactive components tailored for model interpretability. Compared to libraries like Captum, Inspectus emphasizes a direct, visual exploration of model internals within the Jupyter environment, particularly for sequence models.

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 labmlai/inspectus
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Captum
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Captum · recommended 2×
  2. TensorBoard · recommended 2×
  3. Transformers library (Hugging Face) · recommended 1×
  4. Matplotlib · recommended 1×
  5. Seaborn · recommended 1×
  • CATEGORY QUERY
    How can I visualize attention mechanisms in my deep learning models within a notebook?
    you: not recommended
    AI recommended (in order):
    1. Captum
    2. Transformers library (Hugging Face)
    3. Matplotlib
    4. Seaborn
    5. LIME (Local Interpretable Model-agnostic Explanations)
    6. SHAP (SHapley Additive exPlanations)
    7. TensorBoard
    8. ExBERT

    AI recommended 8 alternatives but never named labmlai/inspectus. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help analyze and visualize large language model components in Python?
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases (W&B)
    2. Hugging Face Transformers
    3. Hugging Face Datasets
    4. TensorBoard
    5. Captum
    6. LlamaIndex
    7. LangChain
    8. LangSmith
    9. SpaCy
    10. displaCy

    AI recommended 10 alternatives but never named labmlai/inspectus. 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 labmlai/inspectus?
    pass
    AI named labmlai/inspectus explicitly

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

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

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

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labmlai/inspectus — 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