RRepoGEO

REPOGEO REPORT · LITE

angelos-p/llm-from-scratch

Default branch main · commit 63069f99 · scanned 5/8/2026, 4:22:40 AM

GitHub: 1,508 stars · 83 forks

AI VISIBILITY SCORE
17 /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
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 angelos-p/llm-from-scratch, 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
  • highabout#1
    Add a concise 'About' description

    Why:

    COPY-PASTE FIX
    A hands-on workshop to build a small GPT model from scratch, covering tokenizer, transformer architecture, training loop, and text generation, designed for learning LLM fundamentals on a laptop.
  • mediumlicense#2
    Add a LICENSE file

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the text of the MIT License.

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 angelos-p/llm-from-scratch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 2×
  2. TensorFlow · recommended 2×
  3. JAX · recommended 1×
  4. NumPy · recommended 1×
  5. Julia · recommended 1×
  • CATEGORY QUERY
    How to learn LLM fundamentals by implementing a transformer model from scratch?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. JAX
    4. NumPy
    5. Julia

    AI recommended 5 alternatives but never named angelos-p/llm-from-scratch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good resources for building a small GPT model on a laptop to learn AI?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. PyTorch
    3. minGPT (karpathy/minGPT)
    4. nanoGPT (karpathy/nanoGPT)
    5. TensorFlow
    6. Keras
    7. Jupyter Notebooks
    8. Google Colab

    AI recommended 8 alternatives but never named angelos-p/llm-from-scratch. 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 angelos-p/llm-from-scratch?
    pass
    AI did not name angelos-p/llm-from-scratch — 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 angelos-p/llm-from-scratch in production, what risks or prerequisites should they evaluate first?
    pass
    AI named angelos-p/llm-from-scratch 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 angelos-p/llm-from-scratch solve, and who is the primary audience?
    pass
    AI did not name angelos-p/llm-from-scratch — 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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angelos-p/llm-from-scratch — 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