RRepoGEO

REPOGEO REPORT · LITE

raiyanyahya/how-to-train-your-gpt

Default branch master · commit ed4858f2 · scanned 5/8/2026, 6:48:02 AM

GitHub: 709 stars · 91 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)

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

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 raiyanyahya/how-to-train-your-gpt, 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 README H1 to specify "interactive textbook for building LLMs from scratch"

    Why:

    CURRENT
    # 🧠 How to Train Your GPT
    COPY-PASTE FIX
    # 🧠 How to Train Your GPT: An Interactive Textbook to Build LLMs From Scratch
  • highhomepage#2
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://github.com/raiyanyahya/how-to-train-your-gpt
  • mediumtopics#3
    Refine repository topics to emphasize "LLM development" and "learning" more explicitly

    Why:

    CURRENT
    attention-mechanism, deep-learning, educational, from-scratch, gpt, language-model, llama, llm, machine-learning, natural-language-processing, python, pytorch, tokenisation, transformers, tutorial
    COPY-PASTE FIX
    attention-mechanism, build-llm, deep-learning, educational, from-scratch, gpt, language-model, learn-llm, llm, llm-development, machine-learning, natural-language-processing, python, pytorch, tokenisation, transformers, tutorial

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 raiyanyahya/how-to-train-your-gpt
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
apache/arrow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. apache/arrow · recommended 2×
  2. Hugging Face Transformers · recommended 2×
  3. pytorch/pytorch · recommended 1×
  4. tensorflow/tensorflow · recommended 1×
  5. keras-team/keras · recommended 1×
  • CATEGORY QUERY
    How can I learn to build a large language model from the ground up?
    you: not recommended
    AI recommended (in order):
    1. PyTorch (pytorch/pytorch)
    2. TensorFlow (tensorflow/tensorflow)
    3. Keras (keras-team/keras)
    4. Hugging Face Transformers Library (huggingface/transformers)
    5. Hugging Face Datasets Library (huggingface/datasets)
    6. Pandas (pandas-dev/pandas)
    7. Apache Arrow (apache/arrow)
    8. Parquet
    9. pyarrow (apache/arrow)
    10. NVIDIA CUDA Toolkit
    11. cuDNN
    12. AWS EC2
    13. Google Cloud TPUs
    14. Azure NC-series VMs
    15. PyTorch Lightning (Lightning-AI/pytorch-lightning)
    16. Weights & Biases (W&B) (wandb/wandb)
    17. TensorBoard (tensorflow/tensorboard)

    AI recommended 17 alternatives but never named raiyanyahya/how-to-train-your-gpt. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a practical guide to implement attention mechanisms and tokenization in Python.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. NLTK
    3. SpaCy
    4. BPEmb
    5. PyTorch
    6. TensorFlow / Keras
    7. Hugging Face Transformers

    AI recommended 7 alternatives but never named raiyanyahya/how-to-train-your-gpt. 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 raiyanyahya/how-to-train-your-gpt?
    pass
    AI named raiyanyahya/how-to-train-your-gpt explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts raiyanyahya/how-to-train-your-gpt in production, what risks or prerequisites should they evaluate first?
    pass
    AI named raiyanyahya/how-to-train-your-gpt 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 raiyanyahya/how-to-train-your-gpt solve, and who is the primary audience?
    pass
    AI did not name raiyanyahya/how-to-train-your-gpt — 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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raiyanyahya/how-to-train-your-gpt — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite