RRepoGEO

REPOGEO REPORT · LITE

n2cholas/awesome-jax

Default branch main · commit ccc12842 · scanned 5/26/2026, 11:02:38 PM

GitHub: 2,111 stars · 169 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 n2cholas/awesome-jax, 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 README opening to clearly state it's an awesome list

    Why:

    CURRENT
    JAX brings automatic differentiation and the XLA compiler together through a NumPy-like API for high performance machine learning research on accelerators like GPUs and TPUs.
    COPY-PASTE FIX
    This is a curated list of awesome JAX libraries, projects, and other resources. JAX brings automatic differentiation and the XLA compiler together through a NumPy-like API for high performance machine learning research on accelerators like GPUs and TPUs.
  • mediumhomepage#2
    Add repository URL as homepage

    Why:

    COPY-PASTE FIX
    https://github.com/n2cholas/awesome-jax
  • lowabout#3
    Clarify repository's role in the description

    Why:

    CURRENT
    JAX - A curated list of resources https://github.com/google/jax
    COPY-PASTE FIX
    A curated list of awesome JAX libraries, projects, and resources. For high-performance machine learning research on accelerators. (https://github.com/google/jax)

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 n2cholas/awesome-jax
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 2×
  2. TensorFlow · recommended 2×
  3. JAX · recommended 2×
  4. MXNet · recommended 2×
  5. Keras 3 · recommended 1×
  • CATEGORY QUERY
    What libraries offer automatic differentiation for high-performance machine learning research on accelerators?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. Keras 3
    4. JAX
    5. MXNet
    6. Julia
    7. Zygote.jl

    AI recommended 7 alternatives but never named n2cholas/awesome-jax. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Which deep learning frameworks provide a flexible NumPy-like API for neural network development?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. JAX
    3. TensorFlow
    4. MXNet
    5. NumPy
    6. Autograd

    AI recommended 6 alternatives but never named n2cholas/awesome-jax. 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 n2cholas/awesome-jax?
    pass
    AI did not name n2cholas/awesome-jax — 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 n2cholas/awesome-jax in production, what risks or prerequisites should they evaluate first?
    pass
    AI named n2cholas/awesome-jax 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 n2cholas/awesome-jax solve, and who is the primary audience?
    pass
    AI named n2cholas/awesome-jax explicitly

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

Embed your GEO score

Drop this badge into the README of n2cholas/awesome-jax. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/n2cholas/awesome-jax.svg)](https://repogeo.com/en/r/n2cholas/awesome-jax)
HTML
<a href="https://repogeo.com/en/r/n2cholas/awesome-jax"><img src="https://repogeo.com/badge/n2cholas/awesome-jax.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

n2cholas/awesome-jax — 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