RRepoGEO

REPOGEO REPORT · LITE

gpu-mode/lectures

Default branch main · commit c41f9d02 · scanned 5/13/2026, 4:37:53 PM

GitHub: 6,071 stars · 610 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 gpu-mode/lectures, 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 specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    gpu-programming, cuda, pytorch, lectures, tutorials, high-performance-computing, python, deep-learning-optimization, machine-learning-optimization
  • highreadme#2
    Add an explicit introductory sentence to the README

    Why:

    COPY-PASTE FIX
    This repository provides practical, hands-on tutorials, code, and slides accompanying the GPU-MODE lecture series, focusing on GPU programming with Python, CUDA, and PyTorch optimization techniques.
  • mediumabout#3
    Enhance the repository description for clarity

    Why:

    CURRENT
    Material for gpu-mode lectures
    COPY-PASTE FIX
    Practical tutorials, code, and slides for learning GPU programming with Python, CUDA, and PyTorch optimization.

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 gpu-mode/lectures
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA CUDA Python
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA CUDA Python · recommended 1×
  2. Numba · recommended 1×
  3. PyCUDA · recommended 1×
  4. OpenACC Python Bindings · recommended 1×
  5. pyopenacc · recommended 1×
  • CATEGORY QUERY
    Where can I find practical tutorials for learning GPU programming with Python and CUDA?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA CUDA Python
    2. Numba
    3. PyCUDA
    4. OpenACC Python Bindings
    5. pyopenacc
    6. Anaconda
    7. Udemy
    8. Coursera
    9. GitHub

    AI recommended 9 alternatives but never named gpu-mode/lectures. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to optimize PyTorch model performance using advanced CUDA and GPU techniques?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Apex
    2. PyTorch `torch.compile` (Dynamo)
    3. NVIDIA DALI
    4. NVIDIA Nsight Systems
    5. Nsight Compute
    6. PyTorch `torch.nn.DataParallel`
    7. `torch.distributed.DistributedDataParallel` (DDP)
    8. FlashAttention
    9. `torch.utils.cpp_extension`
    10. Triton

    AI recommended 10 alternatives but never named gpu-mode/lectures. 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 gpu-mode/lectures?
    pass
    AI did not name gpu-mode/lectures — 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 gpu-mode/lectures in production, what risks or prerequisites should they evaluate first?
    pass
    AI named gpu-mode/lectures 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 gpu-mode/lectures solve, and who is the primary audience?
    pass
    AI named gpu-mode/lectures explicitly

    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 gpu-mode/lectures. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/gpu-mode/lectures.svg)](https://repogeo.com/en/r/gpu-mode/lectures)
HTML
<a href="https://repogeo.com/en/r/gpu-mode/lectures"><img src="https://repogeo.com/badge/gpu-mode/lectures.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

gpu-mode/lectures — 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