RRepoGEO

REPOGEO REPORT · LITE

mratsim/Arraymancer

Default branch master · commit 195c75d4 · scanned 5/26/2026, 7:11:56 AM

GitHub: 1,402 stars · 100 forks

AI VISIBILITY SCORE
74 /100
Needs work
Category recall
1 / 2
Avg rank #1.0 when recommended
Rule findings
2 pass · 0 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 mratsim/Arraymancer, 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
    Emphasize general scientific computing and multi-backend capabilities in the README's opening

    Why:

    CURRENT
    Arraymancer is a tensor (N-dimensional array) project in Nim. The main focus is providing a fast and ergonomic CPU, Cuda and OpenCL ndarray library on which to build a scientific computing ecosystem.
    COPY-PASTE FIX
    Arraymancer is a fast, ergonomic, and portable N-dimensional tensor (ndarray) library in Nim, designed for high-performance scientific computing across CPU, GPU, and embedded devices. It supports multiple backends including OpenMP, Cuda, and OpenCL, with a strong focus on deep learning and general numerical computation.
  • mediumtopics#2
    Add 'scientific-computing' to topics

    Why:

    CURRENT
    autograd, automatic-differentiation, cuda, cudnn, deep-learning, gpgpu, gpu-computing, high-performance-computing, iot, linear-algebra, machine-learning, matrix-library, multidimensional-arrays, ndarray, neural-networks, nim, opencl, openmp, parallel-computing, tensor
    COPY-PASTE FIX
    autograd, automatic-differentiation, cuda, cudnn, deep-learning, gpgpu, gpu-computing, high-performance-computing, iot, linear-algebra, machine-learning, matrix-library, multidimensional-arrays, ndarray, neural-networks, nim, opencl, openmp, parallel-computing, scientific-computing, tensor
  • lowabout#3
    Refine the repository description for broader scientific computing emphasis

    Why:

    CURRENT
    A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends
    COPY-PASTE FIX
    A fast, ergonomic, and portable N-dimensional tensor library in Nim for high-performance scientific computing, supporting CPU, GPU, and embedded devices via OpenMP, Cuda, and OpenCL backends, with a deep learning focus.

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
1 / 2
50% of queries surface mratsim/Arraymancer
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
9%
Of all named tools, what % are you?
Top rival
TensorFlow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow · recommended 2×
  2. NimTorch · recommended 1×
  3. Nd4j · recommended 1×
  4. PyTorch · recommended 1×
  5. Keras 3 · recommended 1×
  • CATEGORY QUERY
    Need a Nim-based N-dimensional array library with automatic differentiation for ML.
    you: #1
    AI recommended (in order):
    1. Arraymancer ← you
    2. NimTorch
    3. Nd4j
    4. TensorFlow
    Show full AI answer
  • CATEGORY QUERY
    Seeking a high-performance multi-backend tensor library for scientific computing.
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. Keras 3
    4. JAX
    5. NumPy
    6. CuPy
    7. MXNet

    AI recommended 7 alternatives but never named mratsim/Arraymancer. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 mratsim/Arraymancer?
    pass
    AI named mratsim/Arraymancer explicitly

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

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

    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
  • Prioritized action items8 vs 3 in Lite