RRepoGEO

REPOGEO REPORT · LITE

AviSoori1x/makeMoE

Default branch main · commit 0d68228a · scanned 6/11/2026, 8:27:54 AM

GitHub: 807 stars · 96 forks

AI VISIBILITY SCORE
22 /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
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 AviSoori1x/makeMoE, 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
  • highreadme#1
    Strengthen README's opening to emphasize 'from scratch educational guide' for MoE

    Why:

    CURRENT
    # makeMoE

<div align="center">
    
</div>

#### Sparse mixture of experts language model from scratch inspired by (and largely based on) Andrej Karpathy's makemore (https://github.com/karpathy/makemore) :)
    COPY-PASTE FIX
    # makeMoE: A From-Scratch Educational Guide to Sparse Mixture of Experts Language Models

<div align="center">
    
</div>

#### This repository provides a complete, from-scratch implementation of a sparse mixture of experts (MoE) language model, inspired by Andrej Karpathy's makemore project. It serves as an educational guide to understanding MoE architectures without relying on large frameworks or conversion tools.
  • mediumhomepage#2
    Add the HuggingFace blog post as the repository homepage

    Why:

    COPY-PASTE FIX
    https://huggingface.co/blog/AviSoori1x/makemoe-from-scratch
  • lowabout#3
    Clarify the 'from scratch educational implementation' aspect in the repository description

    Why:

    CURRENT
    From scratch implementation of a sparse mixture of experts language model inspired by Andrej Karpathy's makemore :)
    COPY-PASTE FIX
    An educational, from-scratch implementation of a sparse mixture of experts (MoE) language model, inspired by Andrej Karpathy's makemore. This project focuses on building MoE from fundamentals, not converting existing models.

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 AviSoori1x/makeMoE
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers Library
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers Library · recommended 1×
  2. Fairseq · recommended 1×
  3. DeepSpeed · recommended 1×
  4. Custom PyTorch Implementation · recommended 1×
  5. Megatron-LM · recommended 1×
  • CATEGORY QUERY
    How can I implement a sparse mixture of experts architecture for language models in PyTorch?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. Fairseq
    3. DeepSpeed
    4. Custom PyTorch Implementation
    5. Megatron-LM

    AI recommended 5 alternatives but never named AviSoori1x/makeMoE. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a from-scratch guide to build a mixture of experts deep learning model.
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. Keras API
    4. JAX
    5. Flax
    6. Hugging Face Transformers
    7. DeepMind's Haiku

    AI recommended 7 alternatives but never named AviSoori1x/makeMoE. 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 AviSoori1x/makeMoE?
    pass
    AI did not name AviSoori1x/makeMoE — 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 AviSoori1x/makeMoE in production, what risks or prerequisites should they evaluate first?
    pass
    AI named AviSoori1x/makeMoE 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 AviSoori1x/makeMoE solve, and who is the primary audience?
    pass
    AI did not name AviSoori1x/makeMoE — 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?

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AviSoori1x/makeMoE — 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