RRepoGEO

REPOGEO REPORT · LITE

ray-project/llm-numbers

Default branch main · commit 38fac457 · scanned 5/25/2026, 2:27:46 AM

GitHub: 4,302 stars · 140 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 ray-project/llm-numbers, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's introductory paragraph to clarify purpose

    Why:

    CURRENT
    At Google, there was a document put together by Jeff Dean, the legendary engineer, called Numbers every Engineer should know. It’s really useful to have a similar set of numbers for LLM developers to know that are useful for back-of-the envelope calculations. Here we share particular numbers we at Anyscale use, why the number is important and how to use it to your advantage.
    COPY-PASTE FIX
    This repository provides essential, practical numbers for LLM developers to perform quick back-of-the-envelope calculations, optimize costs, and estimate performance. Inspired by Jeff Dean's 'Numbers every Engineer should know,' we share key metrics and insights from Anyscale to help you make informed decisions when building and deploying large language model applications.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0).

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 ray-project/llm-numbers
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Llama 3
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Llama 3 · recommended 1×
  2. AWS EC2 · recommended 1×
  3. Google Cloud Compute Engine · recommended 1×
  4. Azure Virtual Machines · recommended 1×
  5. Mixtral 8x7B · recommended 1×
  • CATEGORY QUERY
    How to optimize costs when building applications with large language models?
    you: not recommended
    AI recommended (in order):
    1. Llama 3
    2. AWS EC2
    3. Google Cloud Compute Engine
    4. Azure Virtual Machines
    5. Mixtral 8x7B
    6. Gemma
    7. OpenAI API
    8. GPT-4o
    9. GPT-3.5 Turbo
    10. Anthropic Claude API
    11. Claude 3 Haiku
    12. Claude 3 Sonnet
    13. Claude 3 Opus
    14. Google Gemini API
    15. Gemini 1.5 Flash
    16. Gemini 1.5 Pro
    17. Redis
    18. Memcached
    19. Pinecone
    20. Weaviate
    21. Qdrant
    22. Hugging Face Optimum
    23. bitsandbytes

    AI recommended 23 alternatives but never named ray-project/llm-numbers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are essential metrics and benchmarks for evaluating large language model applications?
    you: not recommended
    AI recommended (in order):
    1. Argilla (argilla-io/argilla)
    2. Label Studio (heartexlabs/label-studio)
    3. MMLU
    4. HELM (stanford-crfm/helm)
    5. BIG-bench (google/BIG-bench)
    6. TruthfulQA (sylinrl/TruthfulQA)
    7. GSM8K (openai/grade-school-math)
    8. AlpacaEval (tatsu-lab/alpaca_eval)
    9. Perspective API

    AI recommended 9 alternatives but never named ray-project/llm-numbers. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 ray-project/llm-numbers?
    pass
    AI named ray-project/llm-numbers explicitly

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

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

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite