RRepoGEO

REPOGEO REPORT · LITE

ekzhang/jax-js

Default branch main · commit a0abef37 · scanned 6/11/2026, 3:56:40 AM

GitHub: 817 stars · 47 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
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 ekzhang/jax-js, 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's opening statement to emphasize core value

    Why:

    CURRENT
    jax-js is a machine learning framework for the browser. It aims to bring JAX-style, high-performance CPU and GPU kernels to JavaScript, so you can run numerical applications on the web.
    COPY-PASTE FIX
    **jax-js** is a high-performance machine learning framework for running JAX-style numerical computations and model inference directly in the browser, offering a NumPy-compatible API and leveraging WebGPU & WebAssembly.
  • mediumreadme#2
    Add a 'Comparison with Alternatives' section to README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., '## Comparison with Alternatives' or '## Why jax-js?'. Content should explain how `jax-js` differentiates itself by bringing JAX computations (JIT, auto-diff) directly to the browser via WebGPU shaders, unlike other browser ML frameworks.
  • lowreadme#3
    Add a simple JAX-specific example to the README

    Why:

    CURRENT
    The Quickstart only shows `np.array` and `mul`.
    COPY-PASTE FIX
    In the 'Quickstart' or a new 'JAX Features' section, add a small code snippet demonstrating `jax-js`'s `jit` or `grad` functionality, similar to how JAX examples are typically presented.

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 ekzhang/jax-js
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorFlow.js
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow.js · recommended 1×
  2. ONNX Runtime Web · recommended 1×
  3. WebNN API · recommended 1×
  4. MediaPipe · recommended 1×
  5. Transformers.js · recommended 1×
  • CATEGORY QUERY
    What are the best libraries for high-performance machine learning inference directly in the browser?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow.js
    2. ONNX Runtime Web
    3. WebNN API
    4. MediaPipe
    5. Transformers.js

    AI recommended 5 alternatives but never named ekzhang/jax-js. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I perform efficient numerical computations with a Python-style API in web applications?
    you: not recommended
    AI recommended (in order):
    1. Pyodide (pyodide/pyodide)
    2. JupyterLite (jupyterlite/jupyterlite)
    3. Rust (rust-lang/rust)
    4. NumPy (numpy/numpy)
    5. FastAPI (tiangolo/fastapi)
    6. Flask (pallets/flask)
    7. Django (django/django)
    8. Transcrypt (qquick/Transcrypt)
    9. Brython (brython-dev/brython)
    10. MicroPython (micropython/micropython)

    AI recommended 10 alternatives but never named ekzhang/jax-js. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 ekzhang/jax-js?
    pass
    AI named ekzhang/jax-js explicitly

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

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

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

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ekzhang/jax-js — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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