RRepoGEO

REPOGEO REPORT · LITE

minyoungg/platonic-rep

Default branch main · commit dcd76ba3 · scanned 6/1/2026, 4:32:41 PM

GitHub: 701 stars · 66 forks

AI VISIBILITY SCORE
23 /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
2 / 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 minyoungg/platonic-rep, 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
  • highabout#1
    Add a concise description and relevant topics to the repository

    Why:

    COPY-PASTE FIX
    Description: Official code for 'The Platonic Representation Hypothesis,' exploring how to analyze and extract disentangled, causally-interpretable representations from deep learning models, particularly LLMs and vision models.
Topics: representation-learning, causal-inference, deep-learning, llm-interpretability, feature-extraction, pytorch, machine-learning, ai-research
  • highlicense#2
    Add an MIT License file

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the root directory with the MIT License text.
  • mediumreadme#3
    Add an introductory paragraph to the README

    Why:

    COPY-PASTE FIX
    Add the following paragraph immediately after the author links: 'This repository provides the official code for 'The Platonic Representation Hypothesis,' a framework for analyzing and extracting disentangled, causally-interpretable representations from deep learning models. Unlike methods relying on predefined augmentations, our approach focuses on identifying fundamental, invariant structures within model representations, offering a novel perspective on understanding and improving model generalization.'

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 minyoungg/platonic-rep
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. TransformerLens · recommended 1×
  3. neuron2graph · recommended 1×
  4. Interpret-LM · recommended 1×
  5. Activation Atlases · recommended 1×
  • CATEGORY QUERY
    How can I analyze the internal representations learned by large language models?
    you: not recommended
    AI recommended (in order):
    1. TransformerLens
    2. neuron2graph
    3. Captum
    4. Interpret-LM
    5. Activation Atlases
    6. Circuits
    7. EvoGrad

    AI recommended 7 alternatives but never named minyoungg/platonic-rep. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python libraries are available for extracting features from deep learning models?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. Keras
    4. Hugging Face Transformers
    5. Captum
    6. TorchVision
    7. TensorFlow Hub

    AI recommended 7 alternatives but never named minyoungg/platonic-rep. 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 minyoungg/platonic-rep?
    pass
    AI did not name minyoungg/platonic-rep — likely talking about a different project

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

  • If a team adopts minyoungg/platonic-rep in production, what risks or prerequisites should they evaluate first?
    pass
    AI named minyoungg/platonic-rep 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 minyoungg/platonic-rep solve, and who is the primary audience?
    pass
    AI named minyoungg/platonic-rep 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 minyoungg/platonic-rep. 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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minyoungg/platonic-rep — 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