RRepoGEO

REPOGEO REPORT · LITE

apple/corenet

Default branch main · commit f9f83e61 · scanned 5/15/2026, 11:48:36 PM

GitHub: 7,003 stars · 541 forks

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
  • highreadme#1
    Strengthen the README's opening paragraph to highlight CoreNet's unique focus

    Why:

    CURRENT
    # CoreNet: A library for training deep neural networks

CoreNet is a deep neural network toolkit that allows researchers and engineers to train standard and novel small and large-scale models for variety of tasks, including foundation models (e.g., CLIP and LLM), object classification, object detection, and semantic segmentation.
    COPY-PASTE FIX
    # CoreNet: A library for training deep neural networks

CoreNet is an Apple-developed deep neural network toolkit, specifically designed for researchers and engineers to train standard and novel small and large-scale models. It provides a streamlined, performant, and deployment-ready framework optimized for tasks including foundation models (e.g., CLIP and LLM), object classification, object detection, and semantic segmentation.
  • hightopics#2
    Add relevant topics to improve categorization and searchability

    Why:

    COPY-PASTE FIX
    deep-learning, neural-networks, machine-learning, computer-vision, nlp, foundation-models, llm, object-detection, semantic-segmentation, apple-ml
  • mediumreadme#3
    Clarify the project's license directly in the README

    Why:

    CURRENT
    ## License
    COPY-PASTE FIX
    ## License

CoreNet is licensed under [describe the actual license terms, e.g., 'a custom Apple license' or 'a combination of X and Y licenses']. Please refer to the [LICENSE](./LICENSE) file for full details.

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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 1×
  2. TensorFlow · recommended 1×
  3. Keras 3 · recommended 1×
  4. JAX · recommended 1×
  5. Flax · recommended 1×
  • CATEGORY QUERY
    What's a good library for training large-scale deep neural networks, including foundation models?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. Keras 3
    4. JAX
    5. Flax
    6. Haiku
    7. DeepSpeed
    8. Megatron-LM
    9. Hugging Face Accelerate
    10. Hugging Face Transformers

    AI recommended 10 alternatives but never named apple/corenet. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a toolkit for researchers to train various computer vision and large language models.
    you: not recommended
    AI recommended (in order):
    1. PyTorch (pytorch/pytorch)
    2. TensorFlow (tensorflow/tensorflow)
    3. Hugging Face Transformers (huggingface/transformers)
    4. JAX (google/jax)
    5. fastai (fastai/fastai)

    AI recommended 5 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

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apple/corenet — 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