RRepoGEO

REPOGEO REPORT · LITE

rasbt/reasoning-from-scratch

Default branch main · commit 0080408e · scanned 6/19/2026, 5:16:53 AM

GitHub: 4,537 stars · 669 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)

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

AI VISIBILITY SCORE
33 /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
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 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

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

OVERALL DIRECTION
  • highreadme#1
    Clarify README's opening to emphasize 'from scratch' educational implementation

    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 hands-on, step-by-step codebase for implementing large language model (LLM) reasoning capabilities from scratch in PyTorch. It serves as the official code for the book *Build a Reasoning Model (From Scratch)*, guiding learners through the fundamental algorithms and techniques, distinct from high-level LLM frameworks or production-ready libraries.
  • mediumtopics#2
    Add specific topics to highlight educational and 'from scratch' nature

    Why:

    CURRENT
    ai, artificial-intelligence, chain-of-thought, deep-learning, distillation, grpo, inference-time-scaling, large-language-models, llm, llms, machine-learning, math-reasoning, python, pytorch, reasoning, reasoning-models, reinforcement-learning, rlhf, test-time-compute
    COPY-PASTE FIX
    ai, artificial-intelligence, chain-of-thought, deep-learning, distillation, grpo, inference-time-scaling, large-language-models, llm, llms, machine-learning, math-reasoning, python, pytorch, reasoning, reasoning-models, reinforcement-learning, rlhf, test-time-compute, llm-implementation, pytorch-tutorial, deep-learning-from-scratch, educational-codebase

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 Transformers Library
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers Library · recommended 1×
  2. PyTorch Lightning · recommended 1×
  3. DeepSpeed · recommended 1×
  4. FSDP (Fully Sharded Data Parallel) · recommended 1×
  5. bitsandbytes · recommended 1×
  • CATEGORY QUERY
    How to build a custom large language model with reasoning capabilities using PyTorch?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. PyTorch Lightning
    3. DeepSpeed
    4. FSDP (Fully Sharded Data Parallel)
    5. bitsandbytes
    6. PEFT (Parameter-Efficient Fine-Tuning) library
    7. FlashAttention
    8. xFormers
    9. Optimum

    AI recommended 9 alternatives but never named rasbt/reasoning-from-scratch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a step-by-step guide to implement LLM reasoning techniques?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Hugging Face Transformers Library (huggingface/transformers)
    4. DeepLearning.AI
    5. OpenAI Cookbook (openai/openai-cookbook)
    6. Microsoft's Guidance Library (microsoft/guidance)

    AI recommended 6 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 did not name rasbt/reasoning-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 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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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite