RRepoGEO

REPOGEO REPORT · LITE

clu0/unet.cu

Default branch main · commit 1b59e9c0 · scanned 6/3/2026, 5:04:04 AM

GitHub: 658 stars · 33 forks

AI VISIBILITY SCORE
30 /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
3 / 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 clu0/unet.cu, 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
  • highlicense#1
    Add a LICENSE file to clarify usage terms

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that reflects the intended usage and contribution model for this project.
  • mediumreadme#2
    Clarify the project's unique positioning in the README

    Why:

    CURRENT
    TL;DR:
- UNet diffusion model training written in pure C++/CUDA (only unconditional diffusion right now).
    COPY-PASTE FIX
    TL;DR:
- This project is a highly optimized, from-scratch reference implementation of a UNet diffusion model in pure CUDA, designed to demonstrate high performance without relying on external deep learning frameworks. It provides a direct comparison to framework-based approaches.
- UNet diffusion model training written in pure C++/CUDA (only unconditional diffusion right now).

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 clu0/unet.cu
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorRT
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorRT · recommended 2×
  2. NVIDIA Apex · recommended 1×
  3. xFormers · recommended 1×
  4. PyTorch · recommended 1×
  5. torch.compile · recommended 1×
  • CATEGORY QUERY
    Seeking a high-performance UNet implementation for diffusion models in pure CUDA.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Apex
    2. xFormers
    3. PyTorch
    4. torch.compile
    5. Triton
    6. TensorRT

    AI recommended 6 alternatives but never named clu0/unet.cu. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are non-Python alternatives for training diffusion models for maximum speed?
    you: not recommended
    AI recommended (in order):
    1. CUDA Toolkit
    2. TensorRT
    3. ONNX Runtime (microsoft/onnxruntime)
    4. Apache TVM (apache/tvm)
    5. OpenVINO (openvinotoolkit/openvino)
    6. DirectML (microsoft/DirectML)

    AI recommended 6 alternatives but never named clu0/unet.cu. 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 clu0/unet.cu?
    pass
    AI named clu0/unet.cu explicitly

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

  • If a team adopts clu0/unet.cu in production, what risks or prerequisites should they evaluate first?
    pass
    AI named clu0/unet.cu 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 clu0/unet.cu solve, and who is the primary audience?
    pass
    AI named clu0/unet.cu explicitly

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite