RRepoGEO

REPOGEO REPORT · LITE

dbolya/tomesd

Default branch main · commit 27a14a37 · scanned 6/18/2026, 9:02:40 PM

GitHub: 1,405 stars · 84 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
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 dbolya/tomesd, 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 specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    stable-diffusion, generative-ai, deep-learning, pytorch, inference-optimization, token-merging, computer-vision, machine-learning
  • highreadme#2
    Clarify the specialized nature of ToMe for SD in the opening sentence

    Why:

    CURRENT
    Using nothing but pure python and pytorch, ToMe for SD speeds up diffusion by merging _redundant_ tokens.
    COPY-PASTE FIX
    ToMe for SD is a specialized library for accelerating Stable Diffusion inference. Using nothing but pure python and pytorch, it speeds up diffusion by merging _redundant_ tokens.
  • mediumhomepage#3
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    Add a relevant URL, e.g., to the associated paper or a project page.

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 dbolya/tomesd
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenVINO
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenVINO · recommended 2×
  2. TensorRT · recommended 1×
  3. microsoft/onnxruntime · recommended 1×
  4. PyTorch Quantization · recommended 1×
  5. NVIDIA AMM (Automatic Mixed Precision) · recommended 1×
  • CATEGORY QUERY
    Seeking ways to make my deep learning image synthesis faster without retraining.
    you: not recommended
    AI recommended (in order):
    1. TensorRT
    2. OpenVINO
    3. ONNX Runtime (microsoft/onnxruntime)
    4. PyTorch Quantization
    5. NVIDIA AMM (Automatic Mixed Precision)
    6. Apache TVM (apache/tvm)
    7. NVIDIA GPUs
    8. Intel Arc GPUs
    9. PyTorch DataLoader
    10. TensorFlow tf.data
    11. NumPy (numpy/numpy)
    12. PIL-SIMD (uploadcare/pillow-simd)

    AI recommended 12 alternatives but never named dbolya/tomesd. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I optimize transformer-based generative models for faster inference?
    you: not recommended
    AI recommended (in order):
    1. ONNX Runtime
    2. NVIDIA TensorRT
    3. PyTorch
    4. Hugging Face transformers
    5. DistilBERT
    6. TinyBERT
    7. Hugging Face accelerate
    8. PyTorch Lightning
    9. OpenVINO
    10. Apache TVM
    11. AutoGPTQ
    12. Hugging Face optimum
    13. DeepSpeed
    14. vLLM
    15. FlashAttention
    16. xFormers
    17. NVIDIA Triton Inference Server

    AI recommended 17 alternatives but never named dbolya/tomesd. 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 dbolya/tomesd?
    pass
    AI named dbolya/tomesd explicitly

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

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

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

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dbolya/tomesd — 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