RRepoGEO

REPOGEO REPORT · LITE

SkalskiP/vlms-zero-to-hero

Default branch master · commit 42c04d20 · scanned 5/9/2026, 9:48:16 PM

GitHub: 1,166 stars · 102 forks

AI VISIBILITY SCORE
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 SkalskiP/vlms-zero-to-hero, 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
    Reposition the README's opening paragraph to clarify its nature as an educational series

    Why:

    CURRENT
    Welcome to VLMs Zero to Hero! This series will take you on a journey from the fundamentals of NLP and Computer Vision to the cutting edge of Vision-Language Models.
    COPY-PASTE FIX
    Welcome to VLMs Zero to Hero! This comprehensive educational series, delivered through Jupyter notebooks and video tutorials, will take you on a journey from the fundamentals of NLP and Computer Vision to the cutting edge of Vision-Language Models.
  • mediumtopics#2
    Add topics that describe the repo's format and educational purpose

    Why:

    CURRENT
    bert-model, clip, computer-vision, embeddings, gpt, gpt-2, lora, natural-language-processing, seq2seq, vision-language-model, word2vec
    COPY-PASTE FIX
    bert-model, clip, computer-vision, embeddings, gpt, gpt-2, lora, natural-language-processing, seq2seq, vision-language-model, word2vec, learning-path, educational-series, jupyter-notebooks, video-tutorials, machine-learning-course
  • mediumabout#3
    Enhance the repository description to explicitly mention its format

    Why:

    CURRENT
    This series will take you on a journey from the fundamentals of NLP and Computer Vision to the cutting edge of Vision-Language Models.
    COPY-PASTE FIX
    This comprehensive educational series, delivered through Jupyter notebooks and video tutorials, guides you from the fundamentals of NLP and Computer Vision to the cutting edge of Vision-Language 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 SkalskiP/vlms-zero-to-hero
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 3×
  2. Coursera · recommended 3×
  3. deeplearning.ai · recommended 2×
  4. fastai/fastai · recommended 2×
  5. Stanford's CS231n · recommended 2×
  • CATEGORY QUERY
    Where can I find resources to understand vision-language models from basic concepts?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library (huggingface/transformers)
    2. CLIP (openai/CLIP)
    3. BLIP (salesforce/BLIP)
    4. ViLT (dandelin/vilt)
    5. Stanford CS231N
    6. Papers With Code
    7. DeepLearning.AI
    8. AI Coffee Break with Letitia
    9. Yannic Kilcher
    10. Towards Data Science

    AI recommended 10 alternatives but never named SkalskiP/vlms-zero-to-hero. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best learning paths for mastering NLP, CV, and modern embedding techniques?
    you: not recommended
    AI recommended (in order):
    1. NLTK (nltk/nltk)
    2. Coursera
    3. deeplearning.ai
    4. Stanford's CS224N
    5. Hugging Face Transformers (huggingface/transformers)
    6. fast.ai (fastai/fastai)
    7. Udacity
    8. Coursera
    9. Stanford's CS231n
    10. PyTorch (pytorch/pytorch)
    11. torchvision (pytorch/vision)
    12. TensorFlow (tensorflow/tensorflow)
    13. Keras (keras-team/keras)
    14. fast.ai (fastai/fastai)
    15. Word2Vec
    16. GloVe
    17. Coursera
    18. ELMo
    19. Transformers
    20. BERT
    21. Hugging Face Transformers (huggingface/transformers)
    22. BERT
    23. RoBERTa
    24. GPT
    25. Stanford's CS231n
    26. SimCLR
    27. MoCo
    28. CLIP
    29. OpenAI
    30. DALL-E
    31. Google AI
    32. Meta AI
    33. Kaggle
    34. Twitter
    35. arXiv
    36. The Batch
    37. deeplearning.ai
    38. PyTorch (pytorch/pytorch)
    39. TensorFlow (tensorflow/tensorflow)
    40. Keras (keras-team/keras)
    41. Hugging Face
    42. OpenCV (opencv/opencv)
    43. Khan Academy
    44. 3Blue1Brown
    45. MIT OpenCourseware

    AI recommended 45 alternatives but never named SkalskiP/vlms-zero-to-hero. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 SkalskiP/vlms-zero-to-hero?
    pass
    AI did not name SkalskiP/vlms-zero-to-hero — 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 SkalskiP/vlms-zero-to-hero in production, what risks or prerequisites should they evaluate first?
    pass
    AI named SkalskiP/vlms-zero-to-hero 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 SkalskiP/vlms-zero-to-hero solve, and who is the primary audience?
    pass
    AI did not name SkalskiP/vlms-zero-to-hero — 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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SkalskiP/vlms-zero-to-hero — 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