RRepoGEO

REPOGEO REPORT · LITE

matthewvowels1/Awesome-VAEs

Default branch master · commit 68e9db54 · scanned 6/9/2026, 4:27:58 AM

GitHub: 843 stars · 74 forks

AI VISIBILITY SCORE
28 /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
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 matthewvowels1/Awesome-VAEs, 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
  • mediumlicense#1
    Clarify licensing for the list content in the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, e.g., `## License` followed by a statement like: `The content of this list is licensed under the Creative Commons Attribution 4.0 International License (CC-BY-4.0).`
  • lowhomepage#2
    Add the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/matthewvowels1/Awesome-VAEs

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 matthewvowels1/Awesome-VAEs
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Auto-Encoding Variational Bayes
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Auto-Encoding Variational Bayes · recommended 1×
  2. Variational Autoencoders · recommended 1×
  3. Deep Learning Book · recommended 1×
  4. Generative Deep Learning · recommended 1×
  5. PyTorch Examples Repository · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive resources on variational autoencoders and their applications?
    you: not recommended
    AI recommended (in order):
    1. Auto-Encoding Variational Bayes
    2. Variational Autoencoders
    3. Deep Learning Book
    4. Generative Deep Learning
    5. PyTorch Examples Repository
    6. TensorFlow Tutorials
    7. Understanding Variational Autoencoders (VAEs)

    AI recommended 7 alternatives but never named matthewvowels1/Awesome-VAEs. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best papers and models for disentangled representation learning?
    you: not recommended
    AI recommended (in order):
    1. β-VAE
    2. FactorVAE
    3. DIP-VAE (Disentangled Inferred Prior VAE)
    4. Disentanglement Challenge
    5. Independent Component Analysis (ICA) Loss
    6. DSN (Disentangling by Subspace Diffusion)
    7. InfoGAN

    AI recommended 7 alternatives but never named matthewvowels1/Awesome-VAEs. 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 matthewvowels1/Awesome-VAEs?
    pass
    AI named matthewvowels1/Awesome-VAEs explicitly

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

  • If a team adopts matthewvowels1/Awesome-VAEs in production, what risks or prerequisites should they evaluate first?
    pass
    AI named matthewvowels1/Awesome-VAEs 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 matthewvowels1/Awesome-VAEs solve, and who is the primary audience?
    pass
    AI did not name matthewvowels1/Awesome-VAEs — 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?

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matthewvowels1/Awesome-VAEs — 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