RRepoGEO

REPOGEO REPORT · LITE

Kaixhin/grokking-pytorch

Default branch master · commit 03a6c6ca · scanned 5/21/2026, 4:13:22 AM

GitHub: 1,198 stars · 72 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
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 Kaixhin/grokking-pytorch, 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
    Reposition the README's opening to clarify its purpose as a learning guide

    Why:

    CURRENT
    Grokking PyTorch PyTorch is a flexible deep learning framework that allows automatic differentiation through dynamic neural networks... These are some notes on how I think about using PyTorch, and don't encompass all parts of the library or every best practice, but may be helpful to others.
    COPY-PASTE FIX
    Grokking PyTorch: A Comprehensive Guide to Understanding and Using PyTorch Effectively.
This repository contains my personal notes and practical examples designed to help you deeply understand ('grok') PyTorch, a flexible deep learning framework. It focuses on how to effectively use PyTorch for automatic differentiation through dynamic neural networks, covering core concepts and best practices for beginners and intermediate users.
  • mediumtopics#2
    Add more specific topics to improve category visibility for learning resources

    Why:

    CURRENT
    deep-learning
    COPY-PASTE FIX
    deep-learning, pytorch-tutorial, machine-learning-education, automatic-differentiation, imperative-programming, deep-learning-guide
  • lowhomepage#3
    Add a homepage URL to complete repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/Kaixhin/grokking-pytorch

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 Kaixhin/grokking-pytorch
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. JAX · recommended 2×
  3. TensorFlow · recommended 2×
  4. Flax · recommended 1×
  5. Haiku · recommended 1×
  • CATEGORY QUERY
    How to effectively learn automatic differentiation for dynamic neural networks?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. JAX
    3. Flax
    4. Haiku
    5. TensorFlow
    6. Myia
    7. Julia
    8. Zygote.jl
    9. Flux.jl

    AI recommended 9 alternatives but never named Kaixhin/grokking-pytorch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a guide on building deep learning models with imperative programming style?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. JAX
    4. Keras
    5. MXNet

    AI recommended 5 alternatives but never named Kaixhin/grokking-pytorch. 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 Kaixhin/grokking-pytorch?
    pass
    AI named Kaixhin/grokking-pytorch explicitly

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

  • If a team adopts Kaixhin/grokking-pytorch in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Kaixhin/grokking-pytorch 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 Kaixhin/grokking-pytorch solve, and who is the primary audience?
    pass
    AI did not name Kaixhin/grokking-pytorch — 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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Kaixhin/grokking-pytorch — 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