RRepoGEO

REPOGEO REPORT · LITE

SakanaAI/self-adaptive-llms

Default branch main · commit 03a41aed · scanned 5/9/2026, 5:27:50 AM

GitHub: 1,213 stars · 141 forks

AI VISIBILITY SCORE
28 /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
2 / 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 SakanaAI/self-adaptive-llms, 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
    Reposition README H1 to clarify its nature as an in-model adaptation framework

    Why:

    CURRENT
    <h1>Transformer<sup>2</sup>: Self-adaptive LLMs 🐙 </h1>
    COPY-PASTE FIX
    <h1>Transformer<sup>2</sup>: A Framework for Dynamic, In-Model LLM Adaptation 🐙 </h1>
  • mediumtopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    llm, large-language-models, self-adaptation, real-time-adaptation, inference-optimization, dynamic-llm, ai-research, transformer-models
  • lowhomepage#3
    Add the project's official homepage URL

    Why:

    COPY-PASTE FIX
    https://sakana.ai/transformer-squared

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 SakanaAI/self-adaptive-llms
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Gemini
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Gemini · recommended 2×
  2. OpenAI API · recommended 1×
  3. Anthropic Claude API · recommended 1×
  4. Google Gemini API · recommended 1×
  5. langchain-ai/langchain · recommended 1×
  • CATEGORY QUERY
    How to make large language models adapt to new tasks without extensive fine-tuning?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Anthropic Claude API
    3. Google Gemini API
    4. LangChain (langchain-ai/langchain)
    5. LlamaIndex (run-llama/llama_index)
    6. GPT-4
    7. Claude 3
    8. Google Gemini
    9. Pinecone
    10. Weaviate (weaviate/weaviate)
    11. Chroma DB (chromadb/chroma)
    12. Hugging Face Transformers (huggingface/transformers)
    13. Hugging Face PEFT library (huggingface/peft)
    14. Axolotl (OpenAccess-AI-Collective/axolotl)
    15. gpt-3.5-turbo
    16. Claude
    17. Gemini
    18. Hugging Face Hub
    19. Llama-2-7b-chat-hf
    20. Mistral-7B-Instruct-v0.2

    AI recommended 20 alternatives but never named SakanaAI/self-adaptive-llms. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a framework to dynamically adjust LLM behavior for diverse prompts at inference time.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. OpenAI Functions
    5. Google Gemini
    6. DSPy
    7. PromptLayer
    8. LiteLLM

    AI recommended 8 alternatives but never named SakanaAI/self-adaptive-llms. 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 SakanaAI/self-adaptive-llms?
    pass
    AI named SakanaAI/self-adaptive-llms explicitly

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

  • If a team adopts SakanaAI/self-adaptive-llms in production, what risks or prerequisites should they evaluate first?
    pass
    AI named SakanaAI/self-adaptive-llms 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 SakanaAI/self-adaptive-llms solve, and who is the primary audience?
    pass
    AI did not name SakanaAI/self-adaptive-llms — likely talking about a different project

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

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SakanaAI/self-adaptive-llms — 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