RRepoGEO

REPOGEO REPORT · LITE

PrismML-Eng/Bonsai-demo

Default branch main · commit 751b86af · scanned 6/9/2026, 1:57:50 PM

GitHub: 855 stars · 96 forks

AI VISIBILITY SCORE
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 PrismML-Eng/Bonsai-demo, 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 the README H1 to specify category

    Why:

    CURRENT
    # Bonsai Demo
    COPY-PASTE FIX
    # Bonsai Demo: Run 1-bit & Ternary Quantized LLMs Locally
  • mediumabout#2
    Enhance the repository description

    Why:

    CURRENT
    Bonsai Demo
    COPY-PASTE FIX
    Demonstrates local inference of highly quantized (1-bit & Ternary) Bonsai LLMs on various devices (Mac, Linux/Windows, CPU/GPU).
  • mediumtopics#3
    Add more specific topics for quantized LLMs

    Why:

    CURRENT
    bonsai, llamacpp, llm, mlx, prism-ml, small-models
    COPY-PASTE FIX
    bonsai, llamacpp, llm, mlx, prism-ml, small-models, quantized-llm, 1-bit-llm, edge-inference, efficient-llm

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 PrismML-Eng/Bonsai-demo
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
microsoft/onnxruntime
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. microsoft/onnxruntime · recommended 2×
  2. tensorflow/tensorflow · recommended 1×
  3. apache/tvm · recommended 1×
  4. openvinotoolkit/openvino · recommended 1×
  5. NVIDIA/TensorRT · recommended 1×
  • CATEGORY QUERY
    How can I run highly quantized large language models efficiently on resource-constrained edge devices?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite Micro (tensorflow/tensorflow)
    2. ONNX Runtime (microsoft/onnxruntime)
    3. Apache TVM (apache/tvm)
    4. OpenVINO (openvinotoolkit/openvino)
    5. NVIDIA TensorRT (NVIDIA/TensorRT)
    6. Edge Impulse (edgeimpulse/edgeimpulse)

    AI recommended 6 alternatives but never named PrismML-Eng/Bonsai-demo. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking solutions for local or web-based inference of compact LLMs using frameworks like MLX.
    you: not recommended
    AI recommended (in order):
    1. MLX (apple/mlx)
    2. llama.cpp (ggerganov/llama.cpp)
    3. Ollama (ollama/ollama)
    4. Transformers.js (xenova/transformers.js)
    5. ONNX Runtime (microsoft/onnxruntime)
    6. TensorRT

    AI recommended 6 alternatives but never named PrismML-Eng/Bonsai-demo. 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 PrismML-Eng/Bonsai-demo?
    pass
    AI named PrismML-Eng/Bonsai-demo explicitly

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

  • If a team adopts PrismML-Eng/Bonsai-demo in production, what risks or prerequisites should they evaluate first?
    pass
    AI named PrismML-Eng/Bonsai-demo 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 PrismML-Eng/Bonsai-demo solve, and who is the primary audience?
    pass
    AI did not name PrismML-Eng/Bonsai-demo — likely talking about a different project

    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 PrismML-Eng/Bonsai-demo. 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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MARKDOWN (README)
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  • Brand-free category queries5 vs 2 in Lite
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