RRepoGEO

REPOGEO REPORT · LITE

bkitano/llama-from-scratch

Default branch main · commit 42b6ee9a · scanned 6/11/2026, 9:57:41 AM

GitHub: 579 stars · 55 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 bkitano/llama-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

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm, transformer, from-scratch, educational, deep-learning, pytorch-tutorial, machine-learning-tutorial, llama-implementation
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file with the MIT License text.
  • mediumreadme#3
    Enhance the README's main heading for clearer positioning

    Why:

    CURRENT
    # Llama from scratch
    COPY-PASTE FIX
    # Llama from scratch: A Pedagogical Guide to Implementing LLMs

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 bkitano/llama-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 3 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 3×
  2. TensorFlow · recommended 3×
  3. NumPy · recommended 2×
  4. JAX · recommended 1×
  5. Haiku · recommended 1×
  • CATEGORY QUERY
    How to implement a large language model from scratch for educational purposes?
    you: not recommended
    AI recommended (in order):
    1. NumPy
    2. PyTorch
    3. TensorFlow
    4. PyTorch
    5. TensorFlow

    AI recommended 5 alternatives but never named bkitano/llama-from-scratch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a guide on building a transformer model to understand its internal workings.
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. JAX
    4. Haiku
    5. Flax
    6. Hugging Face Transformers
    7. NumPy

    AI recommended 7 alternatives but never named bkitano/llama-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
    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 bkitano/llama-from-scratch?
    pass
    AI did not name bkitano/llama-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 bkitano/llama-from-scratch in production, what risks or prerequisites should they evaluate first?
    pass
    AI named bkitano/llama-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 bkitano/llama-from-scratch solve, and who is the primary audience?
    pass
    AI named bkitano/llama-from-scratch explicitly

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

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bkitano/llama-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