REPOGEO REPORT · LITE

hans0809/MiniMind-in-Depth

Default branch main · commit f377fe40 · scanned 6/17/2026, 4:08:27 PM

GitHub: 1,078 stars · 89 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

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 hans0809/MiniMind-in-Depth, 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

highreadme#1

Add an explicit English statement to the README's opening

Why:

CURRENT

# MiniMind-in-Depth 🌌

> 🔍 深入浅出，重构理解 —— 基于 MiniMind 的 LLM 教程拆解系列
> 🌱 从 tokenizer 到 MoE，从 pretrain 到 distillation，一步步构建属于你的大模型框架

COPY-PASTE FIX

# MiniMind-in-Depth 🌌

This repository provides an in-depth tutorial and line-by-line code analysis of the MiniMind Large Language Model (LLM) architecture and training process.

> 🔍 深入浅出，重构理解 —— 基于 MiniMind 的 LLM 教程拆解系列
> 🌱 从 tokenizer 到 MoE，从 pretrain 到 distillation，一步步构建属于你的大模型框架

mediumlicense#2
Add a LICENSE file to the repository
Why:
COPY-PASTE FIX
```
Create a `LICENSE` file in the repository root with the text of the MIT License.
```

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 hans0809/MiniMind-in-Depth

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

huggingface/transformers

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

huggingface/transformers · recommended 1×
GPT-2 · recommended 1×
DistilBERT · recommended 1×
pytorch/pytorch · recommended 1×
tensorflow/tensorflow · recommended 1×

CATEGORY QUERY
How to understand the complete architecture and training flow of a small language model?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers Library (huggingface/transformers)
2. GPT-2
3. DistilBERT
4. PyTorch (pytorch/pytorch)
5. TensorFlow (tensorflow/tensorflow)
6. The Annotated Transformer (harvardnlp/annotated-transformer)
AI recommended 6 alternatives but never named hans0809/MiniMind-in-Depth. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking comprehensive explanations and code analysis for advanced LLM techniques like MoE and DPO.
you: not recommended
AI recommended (in order):
1. Hugging Face Blog
2. transformers library
3. DeepLearning.AI
4. OpenAI Research Blog
5. PyTorch
6. TensorFlow
7. arXiv
AI recommended 7 alternatives but never named hans0809/MiniMind-in-Depth. 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 hans0809/MiniMind-in-Depth?
pass
AI did not name hans0809/MiniMind-in-Depth — 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 hans0809/MiniMind-in-Depth in production, what risks or prerequisites should they evaluate first?
pass
AI named hans0809/MiniMind-in-Depth 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 hans0809/MiniMind-in-Depth solve, and who is the primary audience?
pass
AI did not name hans0809/MiniMind-in-Depth — 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

Drop this badge into the README of hans0809/MiniMind-in-Depth. 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)

[![RepoGEO](https://repogeo.com/badge/hans0809/MiniMind-in-Depth.svg)](https://repogeo.com/en/r/hans0809/MiniMind-in-Depth)

HTML

<a href="https://repogeo.com/en/r/hans0809/MiniMind-in-Depth"><img src="https://repogeo.com/badge/hans0809/MiniMind-in-Depth.svg" alt="RepoGEO" /></a>

Pro

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hans0809/MiniMind-in-Depth — 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