RRepoGEO

REPOGEO REPORT · LITE

tinygrad/teenygrad

Default branch main · commit b911d3c8 · scanned 6/8/2026, 5:18:24 PM

GitHub: 810 stars · 100 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 tinygrad/teenygrad, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • mediumreadme#1
    Refine the README's opening sentence for clearer AI categorization

    Why:

    CURRENT
    teenygrad is <1000 line MNIST trainer
    COPY-PASTE FIX
    teenygrad is an ultra-minimalist deep learning framework and MNIST trainer, designed for understanding core concepts.
  • lowhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/tinygrad/tinygrad

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 tinygrad/teenygrad
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
numpy/numpy
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. numpy/numpy · recommended 1×
  2. karpathy/micrograd · recommended 1×
  3. google/jax · recommended 1×
  4. pytorch/pytorch · recommended 1×
  5. tensorflow/tensorflow · recommended 1×
  • CATEGORY QUERY
    What's the most minimal deep learning framework for understanding core concepts without bloat?
    you: not recommended
    AI recommended (in order):
    1. NumPy (numpy/numpy)
    2. Micrograd (karpathy/micrograd)
    3. JAX (google/jax)
    4. PyTorch (pytorch/pytorch)
    5. TensorFlow (tensorflow/tensorflow)

    AI recommended 5 alternatives but never named tinygrad/teenygrad. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an ultra-lightweight machine learning library in Python, solely dependent on NumPy.
    you: not recommended
    AI recommended (in order):
    1. scikit-learn
    2. mlxtend
    3. PyTorch
    4. JAX

    AI recommended 4 alternatives but never named tinygrad/teenygrad. 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 tinygrad/teenygrad?
    pass
    AI named tinygrad/teenygrad explicitly

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

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

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

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tinygrad/teenygrad — 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