RRepoGEO

REPOGEO REPORT · LITE

apple/corenet

Default branch main · commit f9f83e61 · scanned 6/26/2026, 7:18:15 PM

GitHub: 6,997 stars · 541 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 apple/corenet, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    deep-learning, neural-networks, foundation-models, computer-vision, nlp, pytorch, machine-learning, large-scale-models, apple-research
  • highreadme#2
    Clarify the existing license in the README

    Why:

    CURRENT
    ## License
    COPY-PASTE FIX
    ## License

This project is licensed under [specify license(s) here, e.g., "a custom license based on X and Y"]. Please refer to the [LICENSE](LICENSE) file for full details.
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    [Insert official project homepage URL here, e.g., a dedicated project page or Apple's ML research page]

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 apple/corenet
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 2×
  2. TensorFlow · recommended 2×
  3. JAX · recommended 2×
  4. Hugging Face Transformers · recommended 2×
  5. DeepSpeed · recommended 1×
  • CATEGORY QUERY
    What are the best toolkits for training large-scale deep neural networks and foundation models?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. JAX
    4. DeepSpeed
    5. Megatron-LM
    6. Hugging Face Transformers

    AI recommended 6 alternatives but never named apple/corenet. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    I need a flexible deep learning library for various computer vision and NLP tasks.
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. torchvision
    3. torchtext
    4. Hugging Face Transformers
    5. TensorFlow
    6. Keras
    7. tf.keras.applications
    8. tf.data
    9. tf.text
    10. TensorFlow Serving
    11. TensorFlow Lite
    12. JAX
    13. XLA
    14. Flax
    15. Haiku
    16. Theano
    17. MXNet
    18. Amazon SageMaker

    AI recommended 18 alternatives but never named apple/corenet. 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 apple/corenet?
    pass
    AI named apple/corenet explicitly

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

  • If a team adopts apple/corenet in production, what risks or prerequisites should they evaluate first?
    pass
    AI named apple/corenet 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 apple/corenet solve, and who is the primary audience?
    pass
    AI named apple/corenet 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 apple/corenet. 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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apple/corenet — RepoGEO report