RRepoGEO

REPOGEO REPORT · LITE

google-deepmind/penzai

Default branch main · commit aac7808a · scanned 5/26/2026, 11:07:28 PM

GitHub: 1,887 stars · 70 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 google-deepmind/penzai, 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 the README's first sentence to emphasize post-training model manipulation

    Why:

    CURRENT
    Penzai is a JAX library for writing models as legible, functional pytree data structures, along with tools for visualizing, modifying, and analyzing them.
    COPY-PASTE FIX
    Penzai is a JAX library for **inspecting, modifying, and visualizing neural networks after they have been trained**, representing models as legible, functional pytree data structures.
  • hightopics#2
    Add more specific topics related to model manipulation and post-training analysis

    Why:

    CURRENT
    fine-tuning, interpretability, jax, neural-networks, visualization
    COPY-PASTE FIX
    fine-tuning, interpretability, jax, neural-networks, visualization, model-surgery, model-debugging, post-training-analysis, pytree-manipulation, jax-models
  • mediumabout#3
    Refine the repository description to focus on post-training model interaction

    Why:

    CURRENT
    A JAX research toolkit for building, editing, and visualizing neural networks.
    COPY-PASTE FIX
    A JAX research toolkit for **inspecting, modifying, and visualizing** neural networks, especially **after they have been trained**.

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 google-deepmind/penzai
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorBoard
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorBoard · recommended 2×
  2. Netron · recommended 2×
  3. Flax · recommended 1×
  4. Optax · recommended 1×
  5. jax.tree_util · recommended 1×
  • CATEGORY QUERY
    How can I inspect and modify JAX neural network models after training?
    you: not recommended
    AI recommended (in order):
    1. Flax
    2. Optax
    3. jax.tree_util
    4. TensorBoard
    5. Netron

    AI recommended 5 alternatives but never named google-deepmind/penzai. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good JAX tools for visualizing and interpreting neural networks?
    you: not recommended
    AI recommended (in order):
    1. TensorBoard
    2. Captum
    3. SHAP
    4. LIME
    5. Netron
    6. JAX
    7. Matplotlib
    8. Plotly

    AI recommended 8 alternatives but never named google-deepmind/penzai. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 google-deepmind/penzai?
    pass
    AI named google-deepmind/penzai explicitly

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

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

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

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google-deepmind/penzai — 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