RRepoGEO

REPOGEO REPORT · LITE

Visionary-Laboratory/visionary

Default branch main · commit e50f3f6c · scanned 6/10/2026, 11:29:06 AM

GitHub: 509 stars · 32 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
40 /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
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 Visionary-Laboratory/visionary, 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
  • highabout#1
    Update repository description to emphasize real-time neural rendering

    Why:

    CURRENT
    Visionary: The World Model Carrier Built on WebGPU-Powered Gaussian Splatting Platform
    COPY-PASTE FIX
    Visionary: A WebGPU Platform for Real-time Gaussian Splatting and Neural World Model Rendering
  • highreadme#2
    Update README H1 to match the clarified repository description

    Why:

    CURRENT
    # Visionary: The World Model Carrier Built on WebGPU-Powered Gaussian Splatting Platform
    COPY-PASTE FIX
    # Visionary: A WebGPU Platform for Real-time Gaussian Splatting and Neural World Model Rendering
  • mediumcomparison#3
    Add a 'Comparison to Alternatives' section in README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Comparison to Alternatives' or 'Why Visionary?' that highlights how Visionary differs from general-purpose 3D engines (like Three.js, Babylon.js) and other Gaussian Splatting viewers (like splat.js) by emphasizing its web-native, WebGPU-powered platform for diverse GS variants, neural avatars, and ONNX-based post-processing.

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 Visionary-Laboratory/visionary
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Three.js
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Three.js · recommended 1×
  2. splat.js · recommended 1×
  3. PlayCanvas · recommended 1×
  4. Babylon.js · recommended 1×
  5. A-Frame · recommended 1×
  • CATEGORY QUERY
    How to render real-time 3D Gaussian Splatting and neural avatars directly in a web browser?
    you: not recommended
    AI recommended (in order):
    1. Three.js
    2. splat.js
    3. PlayCanvas
    4. Babylon.js
    5. A-Frame
    6. web-splat

    AI recommended 6 alternatives but never named Visionary-Laboratory/visionary. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best WebGPU platforms for visualizing complex 3D world models and neural scenes?
    you: not recommended
    AI recommended (in order):
    1. Three.js (mrdoob/three.js)
    2. Babylon.js (BabylonJS/Babylon.js)
    3. PlayCanvas (playcanvas/engine)
    4. wgpu (gfx-rs/wgpu)
    5. Filament (google/filament)
    6. Lume (lume/lume)

    AI recommended 6 alternatives but never named Visionary-Laboratory/visionary. 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 Visionary-Laboratory/visionary?
    pass
    AI named Visionary-Laboratory/visionary explicitly

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

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

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

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MARKDOWN (README)
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Visionary-Laboratory/visionary — 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