RRepoGEO

REPOGEO REPORT · LITE

mindspore-ai/mindspore

Default branch master · commit bd3c5dd1 · scanned 6/24/2026, 11:26:57 AM

GitHub: 4,691 stars · 754 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 mindspore-ai/mindspore, 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
  • mediumreadme#1
    Strengthen the README's opening statement with core differentiators

    Why:

    CURRENT
    MindSpore is a new open source deep learning training/inference framework that
could be used for mobile, edge and cloud scenarios. MindSpore is designed to
provide development experience with friendly design and efficient execution for
the data scientists and algorithmic engineers, native support for Ascend AI
processor, and software hardware co-optimization. At the meantime MindSpore as
    COPY-PASTE FIX
    MindSpore is an open-source, all-scenario deep learning framework optimized for mobile, edge, and cloud, with native support for Ascend AI processors. It provides a friendly development experience for training and inference, featuring automatic differentiation and parallel computing for data scientists and algorithmic engineers.
  • lowreadme#2
    Add a 'Comparison with other frameworks' section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison with other frameworks

MindSpore differentiates itself from frameworks like TensorFlow and PyTorch through its deep optimization for Huawei's Ascend AI processors and its comprehensive 'all-scenario' design, enabling seamless deployment across device, edge, and cloud environments. While offering similar core features like automatic differentiation and parallel computing, MindSpore focuses on hardware-software co-optimization within the Ascend ecosystem to deliver efficient execution.

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 mindspore-ai/mindspore
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorFlow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow · recommended 2×
  2. PyTorch · recommended 2×
  3. TensorFlow Lite · recommended 1×
  4. TensorFlow Extended (TFX) · recommended 1×
  5. PyTorch Mobile · recommended 1×
  • CATEGORY QUERY
    What deep learning framework supports training and inference across mobile, edge, and cloud?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow
    2. TensorFlow Lite
    3. TensorFlow Extended (TFX)
    4. PyTorch
    5. PyTorch Mobile
    6. TorchServe
    7. ONNX Runtime
    8. Apache MXNet
    9. OpenVINO Toolkit

    AI recommended 9 alternatives but never named mindspore-ai/mindspore. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Which deep learning framework provides automatic differentiation and parallel computing features?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. JAX
    4. MXNet
    5. PaddlePaddle

    AI recommended 5 alternatives but never named mindspore-ai/mindspore. 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 mindspore-ai/mindspore?
    pass
    AI named mindspore-ai/mindspore explicitly

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

  • If a team adopts mindspore-ai/mindspore in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mindspore-ai/mindspore 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 mindspore-ai/mindspore solve, and who is the primary audience?
    pass
    AI named mindspore-ai/mindspore explicitly

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

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mindspore-ai/mindspore — 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