RRepoGEO

REPOGEO REPORT · LITE

RightNow-AI/autokernel

Default branch main · commit 78435821 · scanned 6/30/2026, 4:42:49 AM

GitHub: 1,433 stars · 146 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
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 RightNow-AI/autokernel, 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 core message to clarify its purpose

    Why:

    CURRENT
    **Autoresearch for GPU kernels.** Give it any PyTorch model, go to sleep, wake up to optimized Triton or CUDA C++ kernels.
Inspired by @karpathy/autoresearch -- which demonstrated autonomous AI agents for LLM training research. AutoKernel applies the same philosophy to GPU kernel optimization: agent modifies one file, runs a fixed evaluation, keeps or reverts, repeats forever.
    COPY-PASTE FIX
    **AutoKernel autonomously optimizes GPU kernels for PyTorch models.** Give it any PyTorch model, go to sleep, wake up to optimized Triton or CUDA C++ kernels. It applies the "autoresearch" philosophy (inspired by @karpathy) where an AI agent iteratively modifies, benchmarks, and refines kernels to achieve peak performance.
  • mediumcomparison#2
    Add a 'How is AutoKernel different?' section to the README

    Why:

    COPY-PASTE FIX
    ## How is AutoKernel different?
AutoKernel is a specialized tool for autonomous GPU kernel optimization, not a general-purpose AI agent framework or an operating system for agents. Unlike tools like LangChain, LlamaIndex, or AutoGPT, AutoKernel's agent is focused solely on finding and applying performance improvements to Triton and CUDA C++ kernels. It complements, rather than replaces, existing kernel development tools like OpenAI Triton, Apache TVM, or TorchInductor by automating the iterative optimization process.
  • lowtopics#3
    Add more specific topics for performance and hardware acceleration

    Why:

    CURRENT
    autoresearch, cuda, gpu, kernel-optimization, pytorch, triton
    COPY-PASTE FIX
    autoresearch, cuda, gpu, kernel-optimization, pytorch, triton, performance-optimization, hardware-acceleration

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 RightNow-AI/autokernel
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
openai/triton
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. openai/triton · recommended 2×
  2. apache/tvm · recommended 2×
  3. torch.compile · recommended 1×
  4. NVIDIA/apex · recommended 1×
  5. NVIDIA Nsight Systems · recommended 1×
  • CATEGORY QUERY
    How to automatically optimize GPU kernel performance for PyTorch deep learning models?
    you: not recommended
    AI recommended (in order):
    1. torch.compile
    2. NVIDIA Apex (NVIDIA/apex)
    3. NVIDIA Nsight Systems
    4. NVIDIA Nsight Compute
    5. Triton (openai/triton)
    6. ONNX Runtime (microsoft/onnxruntime)
    7. NVIDIA TensorRT
    8. Apache TVM (apache/tvm)

    AI recommended 8 alternatives but never named RightNow-AI/autokernel. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an autonomous tool to generate optimized Triton or CUDA kernels for PyTorch.
    you: not recommended
    AI recommended (in order):
    1. OpenAI Triton (openai/triton)
    2. Apache TVM (apache/tvm)
    3. TorchInductor
    4. Tensor Comprehensions (facebookresearch/TensorComprehensions)
    5. DaCe (spcl/dace)

    AI recommended 5 alternatives but never named RightNow-AI/autokernel. 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 RightNow-AI/autokernel?
    pass
    AI named RightNow-AI/autokernel explicitly

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

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

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

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RightNow-AI/autokernel — 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