RRepoGEO

REPOGEO REPORT · LITE

nightrome/really-awesome-gan

Default branch master · commit 19b5b1af · scanned 5/28/2026, 10:42:57 PM

GitHub: 3,774 stars · 703 forks

AI VISIBILITY SCORE
17 /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
1 / 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 nightrome/really-awesome-gan, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT or Apache-2.0) in the repository root to clearly state the terms of use.
  • mediumreadme#2
    Explicitly position the repo as an "awesome list" in the README opening

    Why:

    CURRENT
    # really-awesome-gan
A list of papers and other resources on Generative Adversarial (Neural) Networks.
    COPY-PASTE FIX
    # really-awesome-gan
An awesome list and curated collection of papers and other resources on Generative Adversarial (Neural) Networks (GANs).

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 nightrome/really-awesome-gan
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Towards Data Science (Medium)
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Towards Data Science (Medium) · recommended 2×
  2. GANs Zoo · recommended 1×
  3. Papers With Code - Generative Adversarial Networks · recommended 1×
  4. Awesome GANs · recommended 1×
  5. Google Scholar · recommended 1×
  • CATEGORY QUERY
    Looking for a curated collection of academic papers on generative adversarial neural networks.
    you: not recommended
    AI recommended (in order):
    1. GANs Zoo
    2. Papers With Code - Generative Adversarial Networks
    3. Awesome GANs
    4. Google Scholar
    5. arXiv.org
    6. Towards Data Science (Medium)
    7. Distill.pub

    AI recommended 7 alternatives but never named nightrome/really-awesome-gan. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find comprehensive resources and tutorials to learn about GAN architectures?
    you: not recommended
    AI recommended (in order):
    1. DeepLearning.AI's Generative Adversarial Networks (GANs) Specialization on Coursera
    2. Generative Deep Learning by David Foster (O'Reilly)
    3. PyTorch GANs Examples (pytorch/examples)
    4. GANs in Action by Jakub Langr and Vladimir Bok (Manning Publications)
    5. Papers With Code
    6. Towards Data Science (Medium)
    7. Arxiv Insights
    8. Two Minute Papers
    9. StatQuest with Josh Starmer

    AI recommended 9 alternatives but never named nightrome/really-awesome-gan. 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 nightrome/really-awesome-gan?
    pass
    AI did not name nightrome/really-awesome-gan — 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 nightrome/really-awesome-gan in production, what risks or prerequisites should they evaluate first?
    pass
    AI named nightrome/really-awesome-gan 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 nightrome/really-awesome-gan solve, and who is the primary audience?
    pass
    AI did not name nightrome/really-awesome-gan — 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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  • Brand-free category queries5 vs 2 in Lite
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