RRepoGEO

REPOGEO REPORT · LITE

ELS-RD/kernl

Default branch main · commit 0347bec7 · scanned 5/25/2026, 12:58:05 PM

GitHub: 1,585 stars · 99 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 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 ELS-RD/kernl, 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
    Rephrase README's opening to clarify archived status and historical value

    Why:

    CURRENT
    Kernl is now archived
    COPY-PASTE FIX
    Kernl is now archived. While active development has ceased, Kernl remains a valuable historical reference for understanding how to run PyTorch transformer models faster on GPU using Triton, and as the origin of the Triton debugger.
  • mediumabout#2
    Update project description to reflect archived status

    Why:

    CURRENT
    Kernl lets you run PyTorch transformer models several times faster on GPU with a single line of code, and is designed to be easily hackable.
    COPY-PASTE FIX
    Archived: Kernl was an innovative project that demonstrated how to run PyTorch transformer models several times faster on GPU with a single line of code, and served as the origin of the Triton debugger.
  • lowreadme#3
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives
Kernl was designed as a JIT compiler for PyTorch models on NVIDIA GPUs, offering a simpler, more flexible, and often faster alternative to solutions like `torch.compile` (Dynamo) and `NVIDIA/Torch-TensorRT` by generating highly optimized Triton kernels.

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 ELS-RD/kernl
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA TensorRT
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA TensorRT · recommended 1×
  2. PyTorch `torch.compile` (Dynamo) · recommended 1×
  3. ONNX Runtime · recommended 1×
  4. DeepSpeed Inference · recommended 1×
  5. BetterTransformer · recommended 1×
  • CATEGORY QUERY
    How can I significantly speed up PyTorch transformer model inference on my GPU?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. PyTorch `torch.compile` (Dynamo)
    3. ONNX Runtime
    4. DeepSpeed Inference
    5. BetterTransformer
    6. FlashAttention
    7. xFormers

    AI recommended 7 alternatives but never named ELS-RD/kernl. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools simplify writing and optimizing custom GPU kernels for deep learning models?
    you: not recommended
    AI recommended (in order):
    1. CUDA C++
    2. Triton
    3. TVM
    4. OpenCL C
    5. ROCm
    6. SYCL
    7. JAX

    AI recommended 7 alternatives but never named ELS-RD/kernl. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 ELS-RD/kernl?
    pass
    AI named ELS-RD/kernl explicitly

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

  • If a team adopts ELS-RD/kernl in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ELS-RD/kernl 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 ELS-RD/kernl solve, and who is the primary audience?
    pass
    AI named ELS-RD/kernl explicitly

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

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ELS-RD/kernl — 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