REPOGEO REPORT · LITE

XueFuzhao/awesome-mixture-of-experts

Default branch main · commit 34c12aae · scanned 6/24/2026, 7:57:33 PM

GitHub: 1,280 stars · 88 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

23 /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

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 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highlicense#1
Add a LICENSE file for clarity and trust
Why:
COPY-PASTE FIX
```
Create a LICENSE file in the repository root containing the full text of the MIT License.
```

highreadme#2

Refine the README's opening sentence to clarify its role as a resource hub

Why:

CURRENT

A collection of AWESOME things about mixture-of-experts

COPY-PASTE FIX

This repository is a curated collection of AWESOME resources about Mixture-of-Experts (MoE) models, designed to help researchers and practitioners quickly find seminal papers, open-source implementations, and libraries for deep learning and LLMs.

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

Of all named tools, what % are you?

Top rival

Hugging Face Transformers Library

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Transformers Library · recommended 1×
Switch Transformers · recommended 1×
GLaM · recommended 1×
Gopher · recommended 1×
PyTorch FSDP · recommended 1×

CATEGORY QUERY
Where can I find comprehensive resources for understanding and implementing mixture-of-experts models?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers Library
2. Switch Transformers
3. GLaM
4. Gopher
5. PyTorch FSDP
6. OpenAI
7. Wikipedia
AI recommended 7 alternatives but never named XueFuzhao/awesome-mixture-of-experts. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the best open-source mixture-of-experts models or libraries for large language models?
you: not recommended
AI recommended (in order):
1. Mixtral 8x7B
2. DeepSpeed
3. Fairseq
4. Megatron-LM
5. Tutel
AI recommended 5 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 named XueFuzhao/awesome-mixture-of-experts 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 XueFuzhao/awesome-mixture-of-experts solve, and who is the primary audience?
pass
AI named XueFuzhao/awesome-mixture-of-experts explicitly
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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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/XueFuzhao/awesome-mixture-of-experts.svg)](https://repogeo.com/en/r/XueFuzhao/awesome-mixture-of-experts)

HTML

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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