RRepoGEO

REPOGEO REPORT · LITE

rasbt/reasoning-from-scratch

Default branch main · commit f79c9cd0 · scanned 5/9/2026, 10:03:12 AM

GitHub: 4,300 stars · 621 forks

AI VISIBILITY SCORE
40 /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
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 rasbt/reasoning-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
  • highreadme#1
    Clarify the repository's primary role as a code-based implementation guide

    Why:

    CURRENT
    This repository contains the code for developing an LLM reasoning model and is the official code repository for the book *Build a Reasoning Model (From Scratch)*.
    COPY-PASTE FIX
    This repository provides a practical, step-by-step code implementation guide for building a reasoning large language model (LLM) in PyTorch, serving as the official code companion for the book *Build a Reasoning Model (From Scratch)*. You will learn to add reasoning capabilities to a pre-trained base LLM from scratch.
  • mediumreadme#2
    Add a dedicated section highlighting the repository's practical code content

    Why:

    COPY-PASTE FIX
    ## What You'll Find in This Repository
This repository is designed as a hands-on learning resource, providing:
*   **Step-by-step PyTorch implementations** for adding reasoning capabilities to LLMs.
*   **Modular code examples** demonstrating core reasoning concepts from first principles.
*   **Code for loading weights** of existing, pretrained models to build upon.
*   **Practical exercises** to deepen your understanding of LLM reasoning architectures.
  • lowtopics#3
    Augment topics with terms emphasizing practical implementation and tutorials

    Why:

    CURRENT
    ai, artificial-intelligence, deep-learning, deep-neural-networks, large-language-models, llms, machine-learning, python, pytorch, reasoning, reinforcement-learning
    COPY-PASTE FIX
    ai, artificial-intelligence, deep-learning, deep-neural-networks, large-language-models, llms, machine-learning, python, pytorch, reasoning, reinforcement-learning, llm-implementation, pytorch-tutorial, code-examples, machine-learning-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 rasbt/reasoning-from-scratch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Datasets
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Datasets · recommended 1×
  2. GSM8K · recommended 1×
  3. CommonsenseQA · recommended 1×
  4. ARC (AI2 Reasoning Challenge) · recommended 1×
  5. HotpotQA · recommended 1×
  • CATEGORY QUERY
    How can I implement a reasoning large language model in PyTorch step-by-step?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Datasets
    2. GSM8K
    3. CommonsenseQA
    4. ARC (AI2 Reasoning Challenge)
    5. HotpotQA
    6. spaCy
    7. NLTK
    8. Hugging Face Transformers Tokenizers
    9. Hugging Face Transformers
    10. T5ForConditionalGeneration
    11. GPT2LMHeadModel
    12. T5-large
    13. GPT-J
    14. GPT-NeoX
    15. FlanT5ForConditionalGeneration
    16. LlamaForCausalLM
    17. Alpaca
    18. Vicuna
    19. PyTorch
    20. Hugging Face Accelerate
    21. PyTorch Lightning
    22. Hugging Face Evaluate
    23. ROUGE
    24. BLEU
    25. Exact Match
    26. Hugging Face Transformers `pipeline`
    27. ONNX Runtime
    28. TorchScript

    AI recommended 28 alternatives but never named rasbt/reasoning-from-scratch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a practical guide to understand the internal workings of LLM reasoning.
    you: not recommended
    AI recommended (in order):
    1. The Illustrated Transformer
    2. What Does GPT-3 Really Know? A Closer Look at the Knowledge and Reasoning Abilities of Large Language Models
    3. Emergent Abilities of Large Language Models
    4. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
    5. Transformers from Scratch
    6. Language Models are Few-Shot Learners
    7. Mechanistic Interpretability, Explained

    AI recommended 7 alternatives but never named rasbt/reasoning-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
    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 rasbt/reasoning-from-scratch?
    pass
    AI named rasbt/reasoning-from-scratch explicitly

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

  • If a team adopts rasbt/reasoning-from-scratch in production, what risks or prerequisites should they evaluate first?
    pass
    AI named rasbt/reasoning-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 rasbt/reasoning-from-scratch solve, and who is the primary audience?
    pass
    AI named rasbt/reasoning-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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rasbt/reasoning-from-scratch — RepoGEO report