RRepoGEO

REPOGEO REPORT · LITE

danveloper/flash-moe

Default branch main · commit 3601d418 · scanned 5/25/2026, 9:02:27 PM

GitHub: 3,880 stars · 484 forks

AI VISIBILITY SCORE
17 /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
1 / 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 danveloper/flash-moe, 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 to improve categorization

    Why:

    COPY-PASTE FIX
    llm, inference, apple-silicon, metal, c-language, mixture-of-experts, moe, macos, local-llm, high-performance
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the repository root with your chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0).
  • highabout#3
    Update the 'About' description for clarity and keywords

    Why:

    CURRENT
    Running a big model on a small laptop
    COPY-PASTE FIX
    A pure C/Metal inference engine for running 397B+ Mixture-of-Experts LLMs on Apple Silicon laptops (MacBook Pro) with 48GB RAM, achieving 4.4+ tokens/second.

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 danveloper/flash-moe
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LM Studio
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LM Studio · recommended 2×
  2. llama.cpp · recommended 1×
  3. Ollama · recommended 1×
  4. Hugging Face `transformers` · recommended 1×
  5. `bitsandbytes` · recommended 1×
  • CATEGORY QUERY
    How can I run very large language models on a laptop with limited memory?
    you: not recommended
    AI recommended (in order):
    1. llama.cpp
    2. Ollama
    3. LM Studio
    4. Hugging Face `transformers`
    5. `bitsandbytes`
    6. `accelerate`
    7. GPT4All

    AI recommended 7 alternatives but never named danveloper/flash-moe. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a high-performance local inference solution for huge LLMs on Apple Silicon.
    you: not recommended
    AI recommended (in order):
    1. llama.cpp (ggerganov/llama.cpp)
    2. Ollama (ollama/ollama)
    3. MLX (ml-explore/mlx)
    4. Hugging Face transformers (huggingface/transformers)
    5. bitsandbytes (TimDettmers/bitsandbytes)
    6. accelerate (huggingface/accelerate)
    7. LM Studio
    8. Jan (janhq/jan)
    9. LlamaIndex (run-llama/llama_index)
    10. LangChain (langchain-ai/langchain)

    AI recommended 10 alternatives but never named danveloper/flash-moe. 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 danveloper/flash-moe?
    pass
    AI did not name danveloper/flash-moe — 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 danveloper/flash-moe in production, what risks or prerequisites should they evaluate first?
    pass
    AI named danveloper/flash-moe 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 danveloper/flash-moe solve, and who is the primary audience?
    pass
    AI did not name danveloper/flash-moe — 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

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danveloper/flash-moe — 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