RRepoGEO

REPOGEO REPORT · LITE

MarioSieg/magnetron

Default branch master · commit 59e03cb6 · scanned 6/14/2026, 6:37:01 PM

GitHub: 688 stars · 35 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 MarioSieg/magnetron, 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
    Reposition README's opening statement to clarify core identity

    Why:

    CURRENT
    A compact machine learning runtime for developers who want to understand, control, and optimize the full stack. Native C core, modern Python API, no runtime dependencies, no bloat.
    COPY-PASTE FIX
    Magnetron is a **zero-dependency machine learning framework built from scratch in C, featuring a modern Python API.** It offers developers full control over execution and memory, providing a compact, native C core for understanding and optimizing the entire ML stack without external framework bloat.
  • highhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/MarioSieg/magnetron
  • mediumreadme#3
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    This project uses a custom license. Please refer to the [LICENSE file](LICENSE) for full details on usage and distribution.

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 MarioSieg/magnetron
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorFlow
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow · recommended 1×
  2. PyTorch · recommended 1×
  3. MXNet · recommended 1×
  4. ONNX Runtime · recommended 1×
  5. Caffe2 · recommended 1×
  • CATEGORY QUERY
    Seeking a machine learning framework with a C core and Python API for full execution control.
    you: not recommended
    AI recommended (in order):
    1. TensorFlow
    2. PyTorch
    3. MXNet
    4. ONNX Runtime
    5. Caffe2

    AI recommended 5 alternatives but never named MarioSieg/magnetron. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are lightweight ML runtimes for high-performance computing, minimizing external dependencies?
    you: not recommended
    AI recommended (in order):
    1. ONNX Runtime (microsoft/onnxruntime)
    2. TVM (Apache TVM) (apache/tvm)
    3. TFLite (TensorFlow Lite) (tensorflow/tensorflow)
    4. OpenVINO Toolkit (Intel OpenVINO) (openvinotoolkit/openvino)
    5. GGML/llama.cpp (ggerganov/llama.cpp)
    6. Eigen (eigenteam/eigen-git-mirror)
    7. OpenBLAS (xianyi/OpenBLAS)
    8. Intel MKL

    AI recommended 8 alternatives but never named MarioSieg/magnetron. 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 MarioSieg/magnetron?
    pass
    AI named MarioSieg/magnetron explicitly

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

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

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

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MarioSieg/magnetron — 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