RRepoGEO

REPOGEO REPORT · LITE

XueFuzhao/awesome-mixture-of-experts

Default branch main · commit 34c12aae · scanned 5/14/2026, 7:34:58 AM

GitHub: 1,275 stars · 86 forks

AI VISIBILITY SCORE
10 /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
0 / 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 XueFuzhao/awesome-mixture-of-experts, 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
  • highlicense#1
    Add a LICENSE file with the MIT license

    Why:

    COPY-PASTE FIX
    Create a file named `LICENSE` in the repository root with the full content of the MIT License. (The README's MIT badge suggests this is the intended license.)
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ["awesome-list", "mixture-of-experts", "moe", "deep-learning", "machine-learning", "llm", "language-models", "research-papers", "sparse-models"]
  • mediumhomepage#3
    Set the repository URL as its homepage

    Why:

    COPY-PASTE FIX
    Set the repository's homepage URL to `https://github.com/XueFuzhao/awesome-mixture-of-experts` in the repository settings.

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 XueFuzhao/awesome-mixture-of-experts
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. OpenMoE/OpenMoE · recommended 1×
  3. microsoft/DeepSpeed · recommended 1×
  4. NVIDIA/Megatron-LM · recommended 1×
  5. facebookresearch/fairseq · recommended 1×
  • CATEGORY QUERY
    Where can I find open-source implementations of Mixture-of-Experts architectures?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library (huggingface/transformers)
    2. OpenMoE (OpenMoE/OpenMoE)
    3. DeepSpeed (microsoft/DeepSpeed)
    4. Megatron-LM (NVIDIA/Megatron-LM)
    5. Fairseq (facebookresearch/fairseq)
    6. Tensor2Tensor (tensorflow/tensor2tensor)

    AI recommended 6 alternatives but never named XueFuzhao/awesome-mixture-of-experts. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a comprehensive overview of recent advancements in Mixture-of-Experts research and applications.
    you: not recommended
    AI recommended (in order):
    1. Google's Switch Transformers
    2. Google's GLaM (Generalist Language Model)
    3. Meta's LLaMA-MoE
    4. Mistral AI's Mixtral 8x7B
    5. DeepSpeed
    6. Fairseq
    7. vLLM
    8. Triton Inference Server
    9. FlashAttention-2

    AI recommended 9 alternatives but never named XueFuzhao/awesome-mixture-of-experts. 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 XueFuzhao/awesome-mixture-of-experts?
    pass
    AI did not name XueFuzhao/awesome-mixture-of-experts — 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 XueFuzhao/awesome-mixture-of-experts in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name XueFuzhao/awesome-mixture-of-experts — 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?

  • In one sentence, what problem does the repo XueFuzhao/awesome-mixture-of-experts solve, and who is the primary audience?
    pass
    AI did not name XueFuzhao/awesome-mixture-of-experts — 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?

Embed your GEO score

Drop this badge into the README of XueFuzhao/awesome-mixture-of-experts. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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XueFuzhao/awesome-mixture-of-experts — 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