RRepoGEO

REPOGEO REPORT · LITE

MoonshotAI/Moonlight

Default branch master · commit c2ad5b20 · scanned 5/20/2026, 8:07:39 PM

GitHub: 1,478 stars · 87 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
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 MoonshotAI/Moonlight, 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
  • hightopics#1
    Add specific topics for LLM optimizers and MoE training

    Why:

    COPY-PASTE FIX
    llm, large-language-models, optimizer, deep-learning-optimizer, muon-optimizer, moe, mixture-of-experts, llm-training, scalable-training, computational-efficiency
  • highreadme#2
    Add a concise, explicit project statement at the top of the README

    Why:

    CURRENT
    (The first textual content is currently the 'Abstract' section, preceded by links and a PDF icon.)
    COPY-PASTE FIX
    Moonlight is an open-source implementation of the Muon optimizer, specifically engineered for scalable and computationally efficient training of large language models (LLMs), including Mixture-of-Expert (MoE) architectures.
  • mediumhomepage#3
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://huggingface.co/moonshotai/Moonlight-16B-A3B

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 MoonshotAI/Moonlight
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
AdamW
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. AdamW · recommended 1×
  2. AdaFactor · recommended 1×
  3. Lion · recommended 1×
  4. SGD with Momentum · recommended 1×
  5. LAMB · recommended 1×
  • CATEGORY QUERY
    What optimizers improve computational efficiency for large language model training?
    you: not recommended
    AI recommended (in order):
    1. AdamW
    2. AdaFactor
    3. Lion
    4. SGD with Momentum
    5. LAMB

    AI recommended 5 alternatives but never named MoonshotAI/Moonlight. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking scalable training solutions for large-scale Mixture-of-Expert language models efficiently.
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed (microsoft/DeepSpeed)
    2. Megatron-LM (NVIDIA/Megatron-LM)
    3. FairScale (facebookresearch/fairscale)
    4. JAX/Flax with GSPMD
    5. Hugging Face Accelerate (huggingface/accelerate)
    6. Colossal-AI (hpcaitech/ColossalAI)

    AI recommended 6 alternatives but never named MoonshotAI/Moonlight. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    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 MoonshotAI/Moonlight?
    pass
    AI named MoonshotAI/Moonlight explicitly

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

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

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

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MoonshotAI/Moonlight — 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