RRepoGEO

REPOGEO REPORT · LITE

inclusionAI/cuLA

Default branch main · commit b6d8b2d8 · scanned 6/8/2026, 6:36:54 AM

GitHub: 518 stars · 63 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 inclusionAI/cuLA, 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 relevant topics for LLM/transformer acceleration

    Why:

    COPY-PASTE FIX
    cuda, linear-attention, llm, transformers, gpu-acceleration, cutlass, cute-dsl, deep-learning
  • highabout#2
    Clarify the 'About' description to emphasize LLM/transformer context

    Why:

    CURRENT
    CUDA kernels for linear attention variants, written in CuTe DSL and CUTLASS C++.
    COPY-PASTE FIX
    High-performance CUDA kernels for linear attention variants, optimized for long-context LLMs and transformers, written in CuTe DSL and CUTLASS C++.
  • mediumhomepage#3
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    https://github.com/Dao-AILab/flash-linear-attention

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 inclusionAI/cuLA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 2×
  2. FlashAttention-2 · recommended 2×
  3. DeepSpeed · recommended 2×
  4. xFormers · recommended 2×
  5. Hugging Face Transformers · recommended 1×
  • CATEGORY QUERY
    How to accelerate transformer models for very long sequences using linear attention?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Optimum
    3. PyTorch
    4. FlashAttention-2
    5. JAX
    6. Flax
    7. Trax
    8. DeepSpeed
    9. FairScale
    10. xFormers

    AI recommended 10 alternatives but never named inclusionAI/cuLA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking highly optimized GPU kernels for linear attention in large language models.
    you: not recommended
    AI recommended (in order):
    1. FlashAttention-2
    2. xFormers
    3. DeepSpeed
    4. Triton
    5. PyTorch

    AI recommended 5 alternatives but never named inclusionAI/cuLA. 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 inclusionAI/cuLA?
    pass
    AI did not name inclusionAI/cuLA — 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 inclusionAI/cuLA in production, what risks or prerequisites should they evaluate first?
    pass
    AI named inclusionAI/cuLA 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 inclusionAI/cuLA solve, and who is the primary audience?
    pass
    AI named inclusionAI/cuLA explicitly

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

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inclusionAI/cuLA — 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