RRepoGEO

REPOGEO REPORT · LITE

michaelfeil/infinity

Default branch main · commit 1eb4396b · scanned 5/16/2026, 9:07:11 AM

GitHub: 2,798 stars · 188 forks

AI VISIBILITY SCORE
33 /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
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 michaelfeil/infinity, 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 the README H1 to specify its function as an inference server

    Why:

    CURRENT
    # Infinity ♾️
    COPY-PASTE FIX
    # Infinity: High-Throughput Inference Server for Embeddings & Reranking ♾️
  • mediumtopics#2
    Expand repository topics to include broader model serving and inference categories

    Why:

    CURRENT
    bert-embeddings, llm, text-embeddings
    COPY-PASTE FIX
    bert-embeddings, llm, text-embeddings, inference-server, model-serving, deep-learning-inference, onnx, pytorch-inference, gpu-acceleration, rest-api, low-latency, high-throughput, multimodal
  • lowreadme#3
    Add a dedicated section or integrate explicit comparisons to alternatives in the README

    Why:

    COPY-PASTE FIX
    Add the following section heading and introductory sentence to the README: `## Infinity's Differentiators
    Infinity stands out from other model serving solutions by offering...` and then elaborate on specific advantages over competitors like NVIDIA Triton Inference Server, ONNX Runtime, or TensorFlow Serving.

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 michaelfeil/infinity
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ONNX Runtime
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ONNX Runtime · recommended 2×
  2. NVIDIA Triton Inference Server · recommended 1×
  3. FastAPI · recommended 1×
  4. Gunicorn · recommended 1×
  5. TensorFlow Serving · recommended 1×
  • CATEGORY QUERY
    What's the best way to deploy high-throughput text embedding and reranking models?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Triton Inference Server
    2. ONNX Runtime
    3. FastAPI
    4. Gunicorn
    5. TensorFlow Serving
    6. TorchServe
    7. Ray Serve
    8. KServe
    9. AWS SageMaker Endpoints
    10. Google Cloud AI Platform Prediction
    11. Azure Machine Learning Endpoints

    AI recommended 11 alternatives but never named michaelfeil/infinity. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I achieve low-latency inference for multimodal models on diverse hardware accelerators?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. OpenVINO
    3. ONNX Runtime
    4. TVM
    5. Qualcomm AI Engine Direct (QNN)
    6. Core ML

    AI recommended 6 alternatives but never named michaelfeil/infinity. 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 michaelfeil/infinity?
    pass
    AI did not name michaelfeil/infinity — 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?

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

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

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michaelfeil/infinity — 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