RRepoGEO

REPOGEO REPORT · LITE

kakaobrain/mindall-e

Default branch main · commit e5480076 · scanned 6/3/2026, 1:18:03 AM

GitHub: 631 stars · 65 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 kakaobrain/mindall-e, 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
    Clarify the project's core purpose and audience in the README's opening

    Why:

    CURRENT
    # minDALL-E on Conceptual Captions
    COPY-PASTE FIX
    # minDALL-E: A PyTorch Text-to-Image Generation Model for Non-Commercial Use
  • mediumreadme#2
    Add a clear statement about the project's license in the README

    Why:

    COPY-PASTE FIX
    ## License
This project is licensed under [Specify License(s) here, e.g., 'a custom non-commercial license as detailed in the LICENSE file.']. Please refer to the [LICENSE](LICENSE) file for full details.

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 kakaobrain/mindall-e
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Diffusers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Diffusers · recommended 1×
  2. KerasCV · recommended 1×
  3. DALL-E 2 · recommended 1×
  4. CompVis/latent-diffusion · recommended 1×
  5. GLIDE · recommended 1×
  • CATEGORY QUERY
    PyTorch library to generate high-quality images from text descriptions.
    you: not recommended
    AI recommended (in order):
    1. Diffusers
    2. KerasCV
    3. DALL-E 2
    4. latent-diffusion (CompVis/latent-diffusion)
    5. GLIDE

    AI recommended 5 alternatives but never named kakaobrain/mindall-e. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a performant text-to-image generation model for non-commercial projects.
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion XL
    2. Civitai
    3. Stable Diffusion 1.5/2.1
    4. Fooocus
    5. InvokeAI
    6. Automatic1111
    7. ComfyUI
    8. Kandinsky 2.2

    AI recommended 8 alternatives but never named kakaobrain/mindall-e. 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 kakaobrain/mindall-e?
    pass
    AI named kakaobrain/mindall-e explicitly

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

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

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

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kakaobrain/mindall-e — 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