RRepoGEO

REPOGEO REPORT · LITE

HayatoHongo/EveryonesLLM

Default branch main · commit f1323705 · scanned 6/30/2026, 5:58:08 AM

GitHub: 500 stars · 82 forks

AI VISIBILITY SCORE
30 /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
3 / 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 HayatoHongo/EveryonesLLM, 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
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    A comprehensive, hands-on tutorial and framework for building large language models (LLMs) from scratch using Google Colab, designed for learners and developers.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm, large-language-models, google-colab, machine-learning, deep-learning, nlp, tutorial, from-scratch, ai-education
  • mediumreadme#3
    Add an introductory paragraph to the README

    Why:

    COPY-PASTE FIX
    EveryonesLLM provides a simplified, hands-on framework for building and understanding large language models (LLMs) from scratch, primarily for developers and researchers seeking an accessible tutorial experience on Google Colab. This project emphasizes ease of use and step-by-step implementation of LLM components, making complex concepts approachable for non-experts.

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 HayatoHongo/EveryonesLLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 1×
  2. NumPy · recommended 1×
  3. Hugging Face `datasets` · recommended 1×
  4. Hugging Face `tokenizers` · recommended 1×
  5. `re` · recommended 1×
  • CATEGORY QUERY
    How can I learn to build a large language model from scratch using Google Colab?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. NumPy
    3. Hugging Face `datasets`
    4. Hugging Face `tokenizers`
    5. `re`
    6. `tqdm`
    7. Matplotlib
    8. `seaborn`
    9. `tensorboardX`
    10. `torch.utils.tensorboard`
    11. Colab Pro/Pro+

    AI recommended 11 alternatives but never named HayatoHongo/EveryonesLLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are some good hands-on tutorials for understanding and implementing LLM components?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Course
    2. transformers library (huggingface/transformers)
    3. LangChain (langchain-ai/langchain)
    4. OpenAI Cookbook (openai/openai-cookbook)
    5. DeepLearning.AI's "Generative AI with Large Language Models" Specialization
    6. PyTorch (pytorch/pytorch)
    7. Keras (keras-team/keras)

    AI recommended 7 alternatives but never named HayatoHongo/EveryonesLLM. 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 HayatoHongo/EveryonesLLM?
    pass
    AI named HayatoHongo/EveryonesLLM explicitly

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

  • If a team adopts HayatoHongo/EveryonesLLM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named HayatoHongo/EveryonesLLM 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 HayatoHongo/EveryonesLLM solve, and who is the primary audience?
    pass
    AI named HayatoHongo/EveryonesLLM explicitly

    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 HayatoHongo/EveryonesLLM. 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)
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HayatoHongo/EveryonesLLM — 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
HayatoHongo/EveryonesLLM — RepoGEO report