RRepoGEO

REPOGEO REPORT · LITE

gpu-mode/lectures

Default branch main · commit b4df16e2 · scanned 6/24/2026, 3:37:37 AM

GitHub: 6,213 stars · 627 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 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 for GPU programming, PyTorch, and CUDA

    Why:

    COPY-PASTE FIX
    gpu-programming, pytorch, cuda, deep-learning, high-performance-computing, lectures, tutorials, education, machine-learning, gpu-optimization
  • highreadme#2
    Reposition the README's main heading to clearly state the content

    Why:

    CURRENT
    # Supplementary Material for Lectures
    COPY-PASTE FIX
    # GPU Programming Lectures: PyTorch, CUDA, and HPC Tutorials
  • mediumreadme#3
    Add a concise opening paragraph to the README

    Why:

    COPY-PASTE FIX
    This repository provides supplementary materials, notebooks, and slides for lectures on GPU programming, high-performance computing (HPC), and optimizing deep learning models with PyTorch and CUDA. It's designed for students and developers looking to deepen their understanding of parallel processing on GPUs.

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
pytorch/pytorch
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. pytorch/pytorch · recommended 3×
  2. openai/triton · recommended 1×
  3. ctypes · recommended 1×
  4. OpenACC · recommended 1×
  5. OpenMP · recommended 1×
  • CATEGORY QUERY
    How can I integrate custom GPU kernels into my existing PyTorch deep learning projects?
    you: not recommended
    AI recommended (in order):
    1. torch.cuda.amp.custom_fwd and torch.cuda.amp.custom_bwd (pytorch/pytorch)
    2. torch.utils.cpp_extension (pytorch/pytorch)
    3. torch.compile (pytorch/pytorch)
    4. Triton (openai/triton)
    5. ctypes
    6. OpenACC
    7. OpenMP

    AI recommended 7 alternatives but never named gpu-mode/lectures. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What resources help optimize PyTorch model performance and understand GPU memory architecture?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Documentation
    2. NVIDIA Nsight Systems
    3. NVIDIA Nsight Compute
    4. PyTorch Profiler
    5. CUDA C++ Programming Guide
    6. Efficient PyTorch by NVIDIA
    7. Stanford CS249r
    8. CMU 15-819

    AI recommended 8 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

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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