REPOGEO REPORT · LITE

zkywsg/Daily-LLM

Default branch master · commit cb336b6a · scanned 5/30/2026, 9:58:17 AM

GitHub: 843 stars · 159 forks

AI VISIBILITY SCORE

28 /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

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 zkywsg/Daily-LLM, 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

highabout#1

Write a clear, concise repository description

Why:

CURRENT

🔥机器学习/深度学习/Python/大模型/多模态/LLM/deeplearning/Python/Algorithm interview/NLP Tutorial

COPY-PASTE FIX

A comprehensive roadmap for Deep Learning & LLM mastery, covering historical evolution, engineering principles, and interview preparation materials for machine learning, deep learning, and NLP.

mediumtopics#2

Add specific topics for roadmap and interview prep

Why:

CURRENT

cv, deep-learning, leetcode, leetcode-python, leetcode-solutions, llm, machine-learning, nlp, python, pytorch, pytorch-nlp, pytorch-tutorial, pytorch-tutorials, tensorflow, tensorflow-examples, tensorflow-tutorials

COPY-PASTE FIX

cv, deep-learning, leetcode, leetcode-python, leetcode-solutions, llm, machine-learning, nlp, python, pytorch, pytorch-nlp, pytorch-tutorial, pytorch-tutorials, tensorflow, tensorflow-examples, tensorflow-tutorials, llm-roadmap, deep-learning-roadmap, interview-preparation, algorithm-interview, nlp-interview

lowhomepage#3
Add a homepage URL
Why:
COPY-PASTE FIX
```
https://github.com/zkywsg/Daily-LLM
```

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 zkywsg/Daily-LLM

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

WordNet

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

WordNet · recommended 1×
NLTK · recommended 1×
Keras · recommended 1×
TensorFlow · recommended 1×
PyTorch · recommended 1×

CATEGORY QUERY
Seeking a comprehensive roadmap to understand the evolution and engineering of large language models.
you: not recommended
AI recommended (in order):
1. WordNet
2. NLTK
3. Keras
4. TensorFlow
5. PyTorch
6. GPT-1/GPT-2
7. BERT
8. RoBERTa
9. XLNet
10. DistilBERT
11. Hugging Face Transformers Library
12. GPT-3
13. LaMDA
14. PaLM
15. Gemini
16. Llama/Llama 2
17. Mistral AI Models
18. bitsandbytes
19. DeepSpeed
20. Accelerate
21. LangChain
22. LlamaIndex
23. Pinecone
24. Weaviate
25. ChromaDB
26. ONNX Runtime
27. TensorRT
28. AWS SageMaker
29. Google Cloud Vertex AI
30. Azure Machine Learning
AI recommended 30 alternatives but never named zkywsg/Daily-LLM. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Where can I find deep learning and NLP algorithm interview preparation materials in Python?
you: not recommended
AI recommended (in order):
1. Grokking Deep Learning Interviews
2. NeetCode.io
3. LeetCode
4. HackerRank
5. Deep Learning Interviews: Hundreds of fully solved job interview questions from FAANG, startups and academia
6. Hugging Face Transformers Library (huggingface/transformers)
7. Scikit-Learn (scikit-learn/scikit-learn)
8. Keras (keras-team/keras)
9. TensorFlow (tensorflow/tensorflow)
10. Kaggle
AI recommended 10 alternatives but never named zkywsg/Daily-LLM. 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 zkywsg/Daily-LLM?
pass
AI did not name zkywsg/Daily-LLM — 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 zkywsg/Daily-LLM in production, what risks or prerequisites should they evaluate first?
pass
AI named zkywsg/Daily-LLM 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 zkywsg/Daily-LLM solve, and who is the primary audience?
pass
AI named zkywsg/Daily-LLM 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)

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HTML

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zkywsg/Daily-LLM — 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