RRepoGEO

REPOGEO REPORT · LITE

SafeAILab/EAGLE

Default branch main · commit cb7e0841 · scanned 5/25/2026, 12:03:29 AM

GitHub: 2,357 stars · 281 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 SafeAILab/EAGLE, 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 opening to highlight category and competitive edge

    Why:

    CURRENT
    EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency) is a new baseline for fast decoding of Large Language Models (LLMs) with provable performance maintenance. This approach involves extrapolating the second-top-layer contextual feature vectors of LLMs, enabling a significant boost in generation efficiency.
    COPY-PASTE FIX
    EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency) is the **fastest speculative decoding method** for Large Language Models (LLMs) with provable performance maintenance. By extrapolating second-top-layer contextual feature vectors, EAGLE significantly boosts generation efficiency, outperforming alternatives like Medusa and Lookahead.
  • mediumtopics#2
    Expand repository topics for broader LLM acceleration queries

    Why:

    CURRENT
    large-language-models, llm-inference, speculative-decoding
    COPY-PASTE FIX
    large-language-models, llm-inference, speculative-decoding, llm-acceleration, inference-optimization, deep-learning-inference, ai-performance, model-acceleration
  • mediumreadme#3
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    ## License
This project is primarily licensed under the Apache 2.0 License. Please refer to the `LICENSE` file for full details, as it may include additional terms.

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 SafeAILab/EAGLE
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA TensorRT
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA TensorRT · recommended 1×
  2. OpenVINO · recommended 1×
  3. ONNX Runtime · recommended 1×
  4. DeepSpeed · recommended 1×
  5. Hugging Face Optimum · recommended 1×
  • CATEGORY QUERY
    How can I accelerate large language model inference without sacrificing accuracy?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. OpenVINO
    3. ONNX Runtime
    4. DeepSpeed
    5. Hugging Face Optimum
    6. PyTorch Compile (TorchDynamo)
    7. FasterTransformer

    AI recommended 7 alternatives but never named SafeAILab/EAGLE. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the most efficient methods for speculative decoding to speed up LLM generation?
    you: not recommended
    AI recommended (in order):
    1. Medusa
    2. SpecInfer
    3. DeepSpeed-FastGen
    4. Google's Lookahead Decoding
    5. Self-Speculative Decoding
    6. Block-Recurrent Transformers

    AI recommended 6 alternatives but never named SafeAILab/EAGLE. 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 SafeAILab/EAGLE?
    pass
    AI named SafeAILab/EAGLE explicitly

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

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

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

Embed your GEO score

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SafeAILab/EAGLE — 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