REPOGEO REPORT · LITE

afshinea/stanford-cme-295-transformers-large-language-models

Default branch main · commit 0457009a · scanned 7/1/2026, 4:22:35 AM

GitHub: 4,522 stars · 649 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

15 /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

0 / 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 afshinea/stanford-cme-295-transformers-large-language-models, 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

Reposition the README H1 to explicitly state it's a study guide/cheatsheet

Why:

CURRENT

# Transformers & LLMs cheatsheet for Stanford's CME 295

COPY-PASTE FIX

# Comprehensive Study Guide & Cheatsheet for Stanford's CME 295: Transformers & Large Language Models

mediumhomepage#2
Add a homepage URL to the repository
Why:
COPY-PASTE FIX
```
https://superstudy.guide
```

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 afshinea/stanford-cme-295-transformers-large-language-models

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

The Illustrated Transformer

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

The Illustrated Transformer · recommended 1×
Attention Is All You Need · recommended 1×
Hugging Face Course · recommended 1×
Deep Learning · recommended 1×
Stanford CS224N · recommended 1×

CATEGORY QUERY
Looking for a comprehensive study guide summarizing key concepts in transformer models and large language models.
you: not recommended
AI recommended (in order):
1. The Illustrated Transformer
2. Attention Is All You Need
3. Hugging Face Course
4. Deep Learning
5. Stanford CS224N
6. Neural Networks and Deep Learning
AI recommended 6 alternatives but never named afshinea/stanford-cme-295-transformers-large-language-models. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What resources explain LLM finetuning, RAG, and optimization techniques for practical application?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers (huggingface/transformers)
2. LangChain (langchain-ai/langchain)
3. DeepLearning.AI
4. OpenAI
5. Papers With Code
6. Weights & Biases (W&B)
7. Microsoft Azure AI
AI recommended 7 alternatives but never named afshinea/stanford-cme-295-transformers-large-language-models. 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 afshinea/stanford-cme-295-transformers-large-language-models?
pass
AI did not name afshinea/stanford-cme-295-transformers-large-language-models — 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 afshinea/stanford-cme-295-transformers-large-language-models in production, what risks or prerequisites should they evaluate first?
pass
AI did not name afshinea/stanford-cme-295-transformers-large-language-models — 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 afshinea/stanford-cme-295-transformers-large-language-models solve, and who is the primary audience?
pass
AI did not name afshinea/stanford-cme-295-transformers-large-language-models — 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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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite