RRepoGEO

REPOGEO REPORT · LITE

philipturner/metal-flash-attention

Default branch main · commit 8671cddc · scanned 6/12/2026, 10:11:48 PM

GitHub: 605 stars · 40 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 philipturner/metal-flash-attention, 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 opening to clarify its niche

    Why:

    CURRENT
    # FlashAttention (Metal Port)

This repository ports the official implementation of FlashAttention to Apple silicon. It is a minimal, maintainable set of source files that reproduces the FlashAttention algorithm.
    COPY-PASTE FIX
    # FlashAttention (Metal Port)

This repository provides a highly optimized, minimal, and maintainable port of the FlashAttention algorithm specifically for Apple silicon GPUs using the Metal API. Unlike general ML frameworks, this project focuses on reproducing and optimizing the core FlashAttention algorithm to achieve significant performance and memory efficiency gains for transformer models on Apple hardware.
  • mediumtopics#2
    Add more specific topics for FlashAttention and GPU acceleration

    Why:

    CURRENT
    artificial-intelligence, attention-mechanism, high-performance-computing, metal, software-engineering, stable-diffusion, transformer-models
    COPY-PASTE FIX
    flash-attention, gpu-acceleration, apple-gpu, metal-performance-shaders, transformer-inference, deep-learning-optimization, attention-mechanism, high-performance-computing, metal, stable-diffusion, transformer-models, artificial-intelligence
  • lowhomepage#3
    Add a homepage URL to the About section

    Why:

    COPY-PASTE FIX
    https://github.com/philipturner/metal-flash-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 philipturner/metal-flash-attention
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Core ML
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Core ML · recommended 1×
  2. apple/coremltools · recommended 1×
  3. pytorch/pytorch · recommended 1×
  4. MPS backend · recommended 1×
  5. tensorflow/tensorflow · recommended 1×
  • CATEGORY QUERY
    How can I accelerate transformer model inference on Apple silicon with efficient attention mechanisms?
    you: not recommended
    AI recommended (in order):
    1. Core ML
    2. Core ML Tools (apple/coremltools)
    3. PyTorch (pytorch/pytorch)
    4. MPS backend
    5. TensorFlow (tensorflow/tensorflow)
    6. Metal PluggableDevice
    7. tensorflow-metal (apple/tensorflow-metal)
    8. ONNX Runtime (microsoft/onnxruntime)
    9. Core ML Execution Provider
    10. Hugging Face optimum-intel (huggingface/optimum)
    11. llama.cpp (ggerganov/llama.cpp)
    12. MLX (ml-explore/mlx)

    AI recommended 12 alternatives but never named philipturner/metal-flash-attention. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking memory-efficient attention algorithms optimized for Apple's Metal graphics framework.
    you: not recommended
    AI recommended (in order):
    1. FlashAttention
    2. PagedAttention
    3. vLLM
    4. xFormers
    5. Longformer
    6. BigBird
    7. Performer
    8. Linformer

    AI recommended 8 alternatives but never named philipturner/metal-flash-attention. 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 philipturner/metal-flash-attention?
    pass
    AI did not name philipturner/metal-flash-attention — 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 philipturner/metal-flash-attention in production, what risks or prerequisites should they evaluate first?
    pass
    AI named philipturner/metal-flash-attention 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 philipturner/metal-flash-attention solve, and who is the primary audience?
    pass
    AI named philipturner/metal-flash-attention 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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philipturner/metal-flash-attention — 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