RRepoGEO

REPOGEO REPORT · LITE

mit-han-lab/hart

Default branch main · commit e28a41fe · scanned 6/12/2026, 9:08:16 PM

GitHub: 648 stars · 45 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 mit-han-lab/hart, 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
  • highreadme#1
    Add a clarifying tagline to the README's opening

    Why:

    CURRENT
    The README starts with `# HART: Efficient Visual Generation with Hybrid Autoregressive Transformer` followed by links and news.
    COPY-PASTE FIX
    # HART: Efficient Visual Generation with Hybrid Autoregressive Transformer

**Generate high-resolution 1024x1024 images with an autoregressive model that rivals diffusion quality and efficiency.**
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    image-generation, autoregressive-models, visual-generation, deep-learning, transformers, ai-art, high-resolution-images, generative-ai
  • lowreadme#3
    Complete the setup instructions in README

    Why:

    CURRENT
    git clone h
    COPY-PASTE FIX
    git clone https://github.com/mit-han-lab/hart.git

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 mit-han-lab/hart
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Stable Diffusion XL (SDXL)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Stable Diffusion XL (SDXL) · recommended 1×
  2. DeepFloyd IF · recommended 1×
  3. Midjourney (v5.2/v6 Alpha) · recommended 1×
  4. DALL-E 3 · recommended 1×
  5. Kandinsky 2.2 · recommended 1×
  • CATEGORY QUERY
    Looking for efficient visual generation models capable of producing high-resolution images, rivaling diffusion quality.
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion XL (SDXL)
    2. DeepFloyd IF
    3. Midjourney (v5.2/v6 Alpha)
    4. DALL-E 3
    5. Kandinsky 2.2
    6. Playground AI (Turbo Model)

    AI recommended 6 alternatives but never named mit-han-lab/hart. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best autoregressive models for generating high-fidelity images, addressing common training cost challenges?
    you: not recommended
    AI recommended (in order):
    1. VQ-GAN
    2. DALL-E
    3. PixelCNN++
    4. VQ-VAE-2
    5. MADE

    AI recommended 5 alternatives but never named mit-han-lab/hart. 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 mit-han-lab/hart?
    pass
    AI named mit-han-lab/hart explicitly

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

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

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

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mit-han-lab/hart — 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