RRepoGEO

REPOGEO REPORT · LITE

FoundationVision/UniTok

Default branch main · commit bb8012e8 · scanned 6/4/2026, 8:18:25 AM

GitHub: 525 stars · 12 forks

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 FoundationVision/UniTok, 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
    Refine the 'about' description to emphasize foundational role

    Why:

    CURRENT
    A Unified Tokenizer for Visual Generation and Understanding
    COPY-PASTE FIX
    A foundational unified visual tokenizer designed as a versatile building block for both visual generation and understanding tasks in large foundation models.
  • mediumtopics#2
    Add more specific topics to reinforce foundational and multimodal positioning

    Why:

    CURRENT
    autoregressive-models, generative, generative-ai, generative-model, image-generation, image-tokenizer, large-language-models, text-to-image, tokenizer
    COPY-PASTE FIX
    autoregressive-models, generative, generative-ai, generative-model, image-generation, image-tokenizer, large-language-models, text-to-image, tokenizer, multimodal-ai, foundation-models, visual-representation-learning
  • lowreadme#3
    Add a sentence to the README explicitly differentiating UniTok from common alternatives

    Why:

    COPY-PASTE FIX
    Unlike end-to-end generative models (e.g., DALL-E, Parti) or vision-language models (e.g., CLIP, DINOv2), UniTok serves as a foundational, unified visual tokenizer, providing the essential building blocks for both generation and understanding tasks.

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 FoundationVision/UniTok
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ViT
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ViT · recommended 2×
  2. VQGAN · recommended 1×
  3. DALL-E · recommended 1×
  4. Parti · recommended 1×
  5. Perceiver IO · recommended 1×
  • CATEGORY QUERY
    What unified visual tokenization approach works for both image creation and understanding?
    you: not recommended
    AI recommended (in order):
    1. VQGAN
    2. DALL-E
    3. Parti
    4. ViT
    5. Perceiver IO
    6. VQ-Diffusion

    AI recommended 6 alternatives but never named FoundationVision/UniTok. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a versatile visual tokenizer compatible with various generative and multimodal LLMs.
    you: not recommended
    AI recommended (in order):
    1. CLIP
    2. DINOv2
    3. BLIP-2
    4. OpenCLIP
    5. ImageBind
    6. ViT

    AI recommended 6 alternatives but never named FoundationVision/UniTok. 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 FoundationVision/UniTok?
    pass
    AI named FoundationVision/UniTok explicitly

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

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

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

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FoundationVision/UniTok — 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