RRepoGEO

REPOGEO REPORT · LITE

XuezheMax/megalodon

Default branch main · commit cff8ba5f · scanned 6/3/2026, 9:39:13 PM

GitHub: 525 stars · 53 forks

AI VISIBILITY SCORE
28 /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
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 XuezheMax/megalodon, 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
  • highabout#1
    Clarify the repository's 'About' description

    Why:

    CURRENT
    Reference implementation of Megalodon 7B model
    COPY-PASTE FIX
    Megalodon: An efficient framework for LLM pretraining and inference with unlimited context length.
  • hightopics#2
    Add relevant GitHub topics

    Why:

    COPY-PASTE FIX
    llm, large-language-models, deep-learning, machine-learning, pretraining, inference, long-context, attention, transformer
  • mediumhomepage#3
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2309.13658

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 XuezheMax/megalodon
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Dao-AILab/flash-attention
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Dao-AILab/flash-attention · recommended 1×
  2. facebookresearch/xformers · recommended 1×
  3. microsoft/DeepSpeed · recommended 1×
  4. NVIDIA/Megatron-LM · recommended 1×
  5. huggingface/transformers · recommended 1×
  • CATEGORY QUERY
    How to achieve efficient large language model pretraining and inference for long contexts?
    you: not recommended
    AI recommended (in order):
    1. FlashAttention / FlashAttention-2 (Dao-AILab/flash-attention)
    2. xFormers (facebookresearch/xformers)
    3. DeepSpeed (microsoft/DeepSpeed)
    4. Megatron-LM (NVIDIA/Megatron-LM)
    5. LongFormer / BigBird (huggingface/transformers)
    6. Hugging Face Optimum (huggingface/optimum)
    7. vLLM (vllm-project/vllm)

    AI recommended 7 alternatives but never named XuezheMax/megalodon. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks enable large language models to process extremely long or unlimited context?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Chroma
    3. Pinecone
    4. Weaviate
    5. FAISS
    6. LlamaIndex
    7. Haystack (deepset)
    8. Elasticsearch
    9. OpenSearch
    10. Microsoft Guidance
    11. Transformers (Hugging Face)
    12. GPT-4-32k
    13. Claude 2.1
    14. Mistral Large
    15. Gemini 1.5 Pro
    16. FlashAttention
    17. xFormers
    18. AutoGPT
    19. BabyAGI
    20. SuperAGI

    AI recommended 20 alternatives but never named XuezheMax/megalodon. 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 XuezheMax/megalodon?
    pass
    AI named XuezheMax/megalodon explicitly

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

  • If a team adopts XuezheMax/megalodon in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name XuezheMax/megalodon — 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 XuezheMax/megalodon solve, and who is the primary audience?
    pass
    AI named XuezheMax/megalodon explicitly

    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 XuezheMax/megalodon. 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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<a href="https://repogeo.com/en/r/XuezheMax/megalodon"><img src="https://repogeo.com/badge/XuezheMax/megalodon.svg" alt="RepoGEO" /></a>
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XuezheMax/megalodon — 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