RRepoGEO

REPOGEO REPORT · LITE

EvolvingLMMs-Lab/Otter

Default branch main · commit 1e7eb9a6 · scanned 6/30/2026, 6:52:31 AM

GitHub: 3,416 stars · 210 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 EvolvingLMMs-Lab/Otter, 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
    Elevate the core value proposition to the README's opening

    Why:

    CURRENT
    The README currently starts with badges, project credits, and checkpoints before detailing the model's capabilities.
    COPY-PASTE FIX
    Place the following sentence at the very top of the README, immediately after any badges or title: "🦦 Otter is a multi-modal model based on OpenFlamingo (an open-sourced version of DeepMind's Flamingo), trained on MIMIC-IT and showcasing improved instruction-following and in-context learning ability."
  • mediumtopics#2
    Add more specific, solution-oriented topics

    Why:

    CURRENT
    artificial-inteligence, chatgpt, deep-learning, embodied-ai, foundation-models, gpt-4, instruction-tuning, large-scale-models, machine-learning, multi-modality, visual-language-learning
    COPY-PASTE FIX
    artificial-inteligence, chatgpt, deep-learning, embodied-ai, foundation-models, gpt-4, instruction-tuning, large-scale-models, machine-learning, multi-modality, visual-language-learning, in-context-learning, visual-instruction-following
  • lowcomparison#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README titled 'Comparison with Alternatives' or 'Why Otter?' that briefly outlines how Otter differentiates itself from models like BLIP-2, LLaVA, InstructBLIP, and Fuyu-8B, especially regarding its OpenFlamingo foundation, training data (MIMIC-IT), and specific strengths like high-resolution input (OtterHD).

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 EvolvingLMMs-Lab/Otter
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
BLIP-2
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. BLIP-2 · recommended 2×
  2. LLaVA · recommended 2×
  3. InstructBLIP · recommended 2×
  4. Hugging Face Transformers · recommended 1×
  5. OpenFlamingo · recommended 1×
  • CATEGORY QUERY
    How to implement a multi-modal AI model for visual and textual instruction following?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. BLIP-2
    3. LLaVA
    4. InstructBLIP
    5. OpenFlamingo
    6. PyTorch
    7. torchvision
    8. transformers
    9. einops
    10. TensorFlow
    11. tf.keras.applications
    12. keras_cv
    13. keras_nlp
    14. OpenAI API
    15. GPT-4V (GPT-4 with Vision)
    16. DeepMind's Flamingo

    AI recommended 16 alternatives but never named EvolvingLMMs-Lab/Otter. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking open-source visual language models with strong in-context learning and high-resolution capabilities.
    you: not recommended
    AI recommended (in order):
    1. LLaVA
    2. Fuyu-8B
    3. BLIP-2
    4. MiniGPT-4
    5. InstructBLIP

    AI recommended 5 alternatives but never named EvolvingLMMs-Lab/Otter. 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 EvolvingLMMs-Lab/Otter?
    pass
    AI named EvolvingLMMs-Lab/Otter explicitly

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

  • If a team adopts EvolvingLMMs-Lab/Otter in production, what risks or prerequisites should they evaluate first?
    pass
    AI named EvolvingLMMs-Lab/Otter 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 EvolvingLMMs-Lab/Otter solve, and who is the primary audience?
    pass
    AI named EvolvingLMMs-Lab/Otter 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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EvolvingLMMs-Lab/Otter — 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