RRepoGEO

REPOGEO REPORT · LITE

OpenSenseNova/SenseNova-U1

Default branch main · commit 94e1451d · scanned 5/7/2026, 8:42:42 PM

GitHub: 1,134 stars · 63 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 OpenSenseNova/SenseNova-U1, 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
  • highhomepage#1
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://unify.light-ai.top/
  • mediumabout#2
    Refine the repository description for clarity

    Why:

    CURRENT
    SenseNova-U series: Native Unified Paradigm with NEO-Unify from the First Principles
    COPY-PASTE FIX
    SenseNova-U series: Native Unified Multimodal AI models for understanding and generation, built with NEO-Unify from first principles.

Category GEO backends resolved for this scan: google/gemini-2.0-flash-001, deepseek/deepseek-chat

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.0-flash-001. 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 OpenSenseNova/SenseNova-U1
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Flamingo
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Flamingo · recommended 2×
  2. Vision Transformer (ViT) · recommended 2×
  3. CLIP · recommended 2×
  4. GPT-4 with Vision · recommended 1×
  5. Gemini Pro · recommended 1×
  • CATEGORY QUERY
    Seeking a unified model for native multimodal understanding and generation across vision and language.
    you: not recommended
    AI recommended (in order):
    1. GPT-4 with Vision
    2. Gemini Pro
    3. LLaVA
    4. Flamingo
    5. BLIP-2
    6. Vision Transformer (ViT)
    7. CLIP

    AI recommended 7 alternatives but never named OpenSenseNova/SenseNova-U1. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best monolithic architectures for end-to-end multimodal reasoning from pixel to text?
    you: not recommended
    AI recommended (in order):
    1. Flamingo
    2. Vision Transformer (ViT)
    3. SimVLM
    4. VinVL
    5. VisualBERT
    6. LXMERT
    7. CLIP

    AI recommended 7 alternatives but never named OpenSenseNova/SenseNova-U1. 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 OpenSenseNova/SenseNova-U1?
    pass
    AI named OpenSenseNova/SenseNova-U1 explicitly

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

  • If a team adopts OpenSenseNova/SenseNova-U1 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named OpenSenseNova/SenseNova-U1 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 OpenSenseNova/SenseNova-U1 solve, and who is the primary audience?
    pass
    AI named OpenSenseNova/SenseNova-U1 explicitly

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

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OpenSenseNova/SenseNova-U1 — 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