RRepoGEO

REPOGEO REPORT · LITE

vivekkalyanarangan30/llm_from_scratch

Default branch main · commit a5ca62ca · scanned 6/6/2026, 9:27:58 PM

GitHub: 1,031 stars · 269 forks

AI VISIBILITY SCORE
23 /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
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 vivekkalyanarangan30/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, step-by-step curriculum to build a Large Language Model (LLM) from scratch using PyTorch, covering foundations, transformer architecture, and modern techniques.
  • mediumreadme#2
    Add an explicit introductory paragraph to the README

    Why:

    COPY-PASTE FIX
    This repository provides a comprehensive, hands-on curriculum for anyone looking to understand and build a Large Language Model (LLM) from the ground up using PyTorch. Designed for deep learning students and practitioners, it demystifies LLM architecture and training through practical, step-by-step implementation, without relying on high-level abstractions.

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 vivekkalyanarangan30/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. Hugging Face Transformers · recommended 1×
  3. Hugging Face Datasets · recommended 1×
  4. SentencePiece · recommended 1×
  5. tokenizers · recommended 1×
  • CATEGORY QUERY
    How can I learn to build a large language model from scratch in PyTorch?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. Hugging Face Transformers
    3. Hugging Face Datasets
    4. SentencePiece
    5. tokenizers
    6. PyTorch Lightning
    7. DeepSpeed
    8. Weights & Biases (W&B)
    9. MLflow

    AI recommended 9 alternatives but never named vivekkalyanarangan30/llm_from_scratch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a detailed PyTorch guide for implementing transformer architecture and LLM training?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. The Annotated Transformer
    3. Let's build GPT
    4. PyTorch
    5. fastai

    AI recommended 5 alternatives but never named vivekkalyanarangan30/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 vivekkalyanarangan30/llm_from_scratch?
    pass
    AI named vivekkalyanarangan30/llm_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 vivekkalyanarangan30/llm_from_scratch in production, what risks or prerequisites should they evaluate first?
    pass
    AI named vivekkalyanarangan30/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 vivekkalyanarangan30/llm_from_scratch solve, and who is the primary audience?
    pass
    AI did not name vivekkalyanarangan30/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?

Embed your GEO score

Drop this badge into the README of vivekkalyanarangan30/llm_from_scratch. 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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vivekkalyanarangan30/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