RRepoGEO

REPOGEO REPORT · LITE

ayaka14732/tpu-starter

Default branch main · commit b71f130a · scanned 6/11/2026, 12:07:00 PM

GitHub: 570 stars · 30 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 ayaka14732/tpu-starter, 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 initial description to clarify its purpose as a guide/starter kit

    Why:

    CURRENT
    Everything you want to know about Google Cloud TPU
    COPY-PASTE FIX
    This repository is a comprehensive guide and starter kit for ML practitioners and researchers to understand, access, and effectively utilize Google Cloud TPUs for deep learning.
  • mediumtopics#2
    Add specific topics to indicate the repository's nature as a guide or starter kit

    Why:

    CURRENT
    cloud-tpu, deep-learning, gcp, google-cloud-platform, jax, machine-learning, tpu
    COPY-PASTE FIX
    cloud-tpu, deep-learning, gcp, google-cloud-platform, jax, machine-learning, tpu, tpu-guide, tpu-tutorial, ml-starter-kit, getting-started
  • lowhomepage#3
    Add a homepage URL to complete the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/ayaka14732/tpu-starter

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 ayaka14732/tpu-starter
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA GeForce RTX 4090
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA GeForce RTX 4090 · recommended 1×
  2. NVIDIA A100 Tensor Core GPU · recommended 1×
  3. NVIDIA H100 Tensor Core GPU · recommended 1×
  4. Google Cloud TPU v3 Pods · recommended 1×
  5. Google Cloud TPU v4 Pods · recommended 1×
  • CATEGORY QUERY
    How do I begin working with specialized processing units for accelerated deep learning?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA GeForce RTX 4090
    2. NVIDIA A100 Tensor Core GPU
    3. NVIDIA H100 Tensor Core GPU
    4. Google Cloud TPU v3 Pods
    5. Google Cloud TPU v4 Pods
    6. AMD Instinct MI250X
    7. AMD Radeon RX 7900 XTX
    8. Intel Gaudi2
    9. Google Cloud Platform (GCP)
    10. Amazon Web Services (AWS)
    11. Microsoft Azure
    12. TensorFlow
    13. PyTorch
    14. CUDA Toolkit
    15. ROCm

    AI recommended 15 alternatives but never named ayaka14732/tpu-starter. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What resources explain how to access and deploy powerful cloud-based AI training infrastructure?
    you: not recommended
    AI recommended (in order):
    1. AWS Machine Learning Blog
    2. Google Cloud AI Platform Documentation
    3. Azure Machine Learning Documentation
    4. NVIDIA NGC (NVIDIA GPU Cloud)
    5. Paperspace Gradient Documentation
    6. Lambda Labs Blog & Tutorials
    7. Hugging Face Accelerate Documentation

    AI recommended 7 alternatives but never named ayaka14732/tpu-starter. 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 ayaka14732/tpu-starter?
    pass
    AI named ayaka14732/tpu-starter explicitly

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

  • If a team adopts ayaka14732/tpu-starter in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ayaka14732/tpu-starter 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 ayaka14732/tpu-starter solve, and who is the primary audience?
    pass
    AI named ayaka14732/tpu-starter explicitly

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

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ayaka14732/tpu-starter — 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