RRepoGEO

REPOGEO REPORT · LITE

nebuly-ai/optimate

Default branch main · commit a6d302f9 · scanned 6/30/2026, 2:02:56 PM

GitHub: 8,332 stars · 620 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 nebuly-ai/optimate, 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 to clarify the specific utility of this legacy project

    Why:

    CURRENT
    This repository is now in a legacy phase and is no longer actively maintained. Although the source code is still available in the Git history, there will be no additional updates or official support.
    COPY-PASTE FIX
    While this repository is no longer actively maintained, it remains a valuable open-source collection of foundational libraries for AI model optimization. It serves as a historical reference and a resource for understanding techniques like Speedster, Nos, and ChatLLaMA, and can be a base for community-driven forks.
  • mediumreadme#2
    Emphasize core differentiators and specific problem-solving in README

    Why:

    CURRENT
    Optimate is a collection of libraries designed to help you optimize your AI models.
    COPY-PASTE FIX
    Optimate is a collection of libraries designed to help you optimize your AI models, with a particular focus on AI/ML-driven recommendations for optimizing resource configurations (CPU, memory, GPU) specifically for AI/ML workloads on Kubernetes. It aims to balance both performance and cost for deep learning and large language models.
  • lowtopics#3
    Add specific technical topics for better categorization

    Why:

    CURRENT
    ai, analytics, artificial-intelligence, deeplearning, large-language-models, llm
    COPY-PASTE FIX
    ai, analytics, artificial-intelligence, deeplearning, large-language-models, llm, kubernetes, mlops, cost-optimization

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 nebuly-ai/optimate
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pytorch/pytorch
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. pytorch/pytorch · recommended 3×
  2. tensorflow/tensorflow · recommended 2×
  3. TensorRT · recommended 1×
  4. openvinotoolkit/openvino · recommended 1×
  5. microsoft/onnxruntime · recommended 1×
  • CATEGORY QUERY
    How can I reduce inference costs and improve performance for my deep learning models?
    you: not recommended
    AI recommended (in order):
    1. TensorRT
    2. OpenVINO (openvinotoolkit/openvino)
    3. ONNX Runtime (microsoft/onnxruntime)
    4. PyTorch Quantization APIs (pytorch/pytorch)
    5. TensorFlow Lite (tensorflow/tensorflow)
    6. TensorFlow Model Optimization Toolkit (tensorflow/model-optimization)
    7. PyTorch Pruning APIs (pytorch/pytorch)
    8. Hugging Face Transformers (huggingface/transformers)
    9. TensorFlow/Keras (tensorflow/tensorflow)
    10. PyTorch (pytorch/pytorch)
    11. AWS Inferentia
    12. AWS Trainium
    13. Google TPUs (Tensor Processing Units)
    14. MobileNet
    15. EfficientNet
    16. SqueezeNet
    17. TinyBERT
    18. DistilBERT
    19. NVIDIA Triton Inference Server (triton-inference-server/server)
    20. Kubernetes (kubernetes/kubernetes)
    21. Docker (moby/moby)
    22. Redis (redis/redis)
    23. AWS Greengrass
    24. Azure IoT Edge (Azure/iotedge)

    AI recommended 24 alternatives but never named nebuly-ai/optimate. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help optimize large language models for GPU and CPU inference efficiency?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. OpenVINO
    3. ONNX Runtime
    4. DeepSpeed
    5. vLLM
    6. llama.cpp
    7. Optimum

    AI recommended 7 alternatives but never named nebuly-ai/optimate. 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 nebuly-ai/optimate?
    pass
    AI named nebuly-ai/optimate explicitly

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

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

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

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nebuly-ai/optimate — 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