RRepoGEO

REPOGEO REPORT · LITE

tile-ai/tilelang

Default branch main · commit 33b63686 · scanned 5/17/2026, 8:37:16 PM

GitHub: 6,228 stars · 570 forks

AI VISIBILITY SCORE
35 /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
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 tile-ai/tilelang, 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 to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    domain-specific-language, dsl, gpu-programming, cpu-optimization, accelerator-programming, kernel-optimization, high-performance-computing, ai-workloads, tvm, compilers, machine-learning-compilers
  • highreadme#2
    Strengthen README's opening statement to emphasize AI kernel optimization

    Why:

    CURRENT
    Tile Language (**tile-lang**) is a concise domain-specific language designed to streamline the development of high-performance GPU/CPU kernels (e.g., GEMM, Dequant GEMM, FlashAttention, LinearAttention). By employing a Pythonic syntax with an underlying compiler infrastructure on top of TVM, tile-lang allows developers to focus on productivity without sacrificing the low-level optimizations necessary for state-of-the-art performance.
    COPY-PASTE FIX
    Tile Language (**tile-lang**) is a concise domain-specific language (DSL) designed to streamline the development of high-performance **AI kernels** for GPU/CPU/Accelerators (e.g., GEMM, Dequant GEMM, FlashAttention, LinearAttention). By employing a Pythonic syntax with an underlying compiler infrastructure on top of TVM, tile-lang allows developers to focus on productivity without sacrificing the low-level optimizations necessary for state-of-the-art performance in **AI workloads**.
  • mediumreadme#3
    Clarify the project's license in the README

    Why:

    CURRENT
    (no explicit license statement in README excerpt)
    COPY-PASTE FIX
    ## License
This project is licensed under the terms specified in the [LICENSE](LICENSE) file. Please refer to that file for full details on usage and distribution.

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 tile-ai/tilelang
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Python
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Python · recommended 2×
  2. C++ · recommended 2×
  3. Julia · recommended 1×
  4. CUDA.jl · recommended 1×
  5. KernelAbstractions.jl · recommended 1×
  • CATEGORY QUERY
    How to write optimized GPU and CPU kernels with a high-level language?
    you: not recommended
    AI recommended (in order):
    1. Julia
    2. CUDA.jl
    3. KernelAbstractions.jl
    4. Python
    5. Numba
    6. OpenCL C++
    7. SYCL
    8. DPC++
    9. oneAPI
    10. C++
    11. Kokkos
    12. C++
    13. RAJA
    14. Python
    15. PyTorch
    16. TensorFlow
    17. cuDNN
    18. cuBLAS
    19. Rust
    20. cust
    21. accel

    AI recommended 21 alternatives but never named tile-ai/tilelang. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a Pythonic DSL for accelerating custom compute operations on various hardware.
    you: not recommended
    AI recommended (in order):
    1. Taichi (taichi-dev/taichi)
    2. Numba (numba/numba)
    3. Jax (google/jax)
    4. Apache TVM (apache/tvm)
    5. PyTorch (pytorch/pytorch)
    6. TensorFlow (tensorflow/tensorflow)
    7. PyOpenCL (pyopencl/pyopencl)
    8. PyCUDA (inducer/pycuda)

    AI recommended 8 alternatives but never named tile-ai/tilelang. 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 tile-ai/tilelang?
    pass
    AI named tile-ai/tilelang explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite