RRepoGEO

REPOGEO REPORT · LITE

multimodal-art-projection/MAP-NEO

Default branch main · commit 81f3cad6 · scanned 6/5/2026, 6:02:59 AM

GitHub: 986 stars · 91 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 multimodal-art-projection/MAP-NEO, 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
  • highreadme#1
    Reposition README H1 and opening paragraph to clarify actual purpose

    Why:

    CURRENT
    # MAP-NEO: A fully open-sourced Large Language Model
<div align="center">
    <p>
    <b>MAP-NEO</b> is a <b>fully open-sourced</b> Large Language Model that includes the pretraining data, a data processing pipeline (<b>Matrix</b>), pretraining scripts, and alignment code.
    COPY-PASTE FIX
    # MAP-NEO: Real-time AI Art Projection System
<div align="center">
    <p>
    <b>MAP-NEO</b> is a <b>real-time projection mapping system</b> that enables artists and VJs to create and project interactive, AI-generated art. It addresses the need for dynamic visual experiences in live performances and installations by unifying real-time AI content generation (e.g., Stable Diffusion, ControlNet) with interactive projection onto physical spaces.
  • mediumlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Add a LICENSE file to the repository root with a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0).

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 multimodal-art-projection/MAP-NEO
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Qwen2
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Qwen2 · recommended 1×
  2. Yi · recommended 1×
  3. DeepSeek-V2 · recommended 1×
  4. Llama 3 · recommended 1×
  5. Baichuan 2 · recommended 1×
  • CATEGORY QUERY
    What open-source large language models perform well in English and Chinese reasoning?
    you: not recommended
    AI recommended (in order):
    1. Qwen2
    2. Yi
    3. DeepSeek-V2
    4. Llama 3
    5. Baichuan 2

    AI recommended 5 alternatives but never named multimodal-art-projection/MAP-NEO. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find complete open-source resources for training a large language model from scratch?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library (huggingface/transformers)
    2. Hugging Face Datasets Library (huggingface/datasets)
    3. Hugging Face Accelerate Library (huggingface/accelerate)
    4. PEFT (Parameter-Efficient Fine-Tuning) Library (huggingface/peft)
    5. Hugging Face Hub
    6. Lit-GPT (karpathy/lit-gpt)
    7. OpenLM Research's OpenLLaMA (openlm-research/open_llama)
    8. EleutherAI's GPT-NeoX (EleutherAI/gpt-neox)
    9. DeepSpeed (microsoft/DeepSpeed)
    10. FairScale (facebookresearch/fairscale)
    11. Megatron-LM (NVIDIA/Megatron-LM)

    AI recommended 11 alternatives but never named multimodal-art-projection/MAP-NEO. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 multimodal-art-projection/MAP-NEO?
    pass
    AI named multimodal-art-projection/MAP-NEO explicitly

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

  • If a team adopts multimodal-art-projection/MAP-NEO in production, what risks or prerequisites should they evaluate first?
    pass
    AI named multimodal-art-projection/MAP-NEO 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 multimodal-art-projection/MAP-NEO solve, and who is the primary audience?
    pass
    AI named multimodal-art-projection/MAP-NEO 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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MARKDOWN (README)
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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite