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Data sourced from GitHub API

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  3. jax-ml
jax-mlOrganization

jax-ml

@jax-ml • Pushing back the limits on numerical computing.. Use this route to separate flagship concentration from portfolio breadth before you treat a publisher as broadly strong.

Portfolio concentration

95%

Top three share

Shows whether the organization is driven by one breakout repo or several visible projects.

Breadth

13 repos

Visible snapshot

9 repositories updated in the last 90 days.

Leading language

Python

Portfolio mix

Python (8), C++ (2), HTML (1)

Average size

3K

Stars per repository

Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.

Back to organizationsCompare repositories
Updated: 2026-01-10(155d ago)GitHub API fallback13 repositories

Portfolio Shape

95%

of the visible star count comes from this organization's top three repositories.

Average Repository Size

3K

stars per repository in this same snapshot.

Current Mix

Python

is the most common language here, with 9 repositories updated in the last 90 days.

Why this rank

This organization stands out because one flagship repo drives 91% of its visible star count.

Flagship share 91%Breakout repo: jax

Organization pages work best when you separate portfolio breadth from flagship concentration. In jax-ml's case, the visible top three repositories account for about 95% of total stars in this snapshot, which helps explain whether the organization is known for one breakout project or for a broader repeatable portfolio.

The dominant language mix here is Python (8), C++ (2), HTML (1). That makes this page useful not just for popularity checks, but also for seeing what technical shape an organization's public ecosystem actually has.

Source: GitHub API fallback. This is the same cache-first snapshot used by the organization ranking list, so the summary view and the detail view should stay aligned.

Top Repositories

#RepositoryLanguageStars🍴 ForksUpdated
1jax-ml/jax

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

Python35.8K3.6KToday
2jax-ml/scaling-book

Home for "How To Scale Your Model", a short blog-style textbook about scaling LLMs on TPUs

HTML1.1K1613 days ago
3jax-ml/jax-triton

jax-triton contains integrations between JAX and OpenAI Triton

Python462571 weeks ago
4jax-ml/ml_dtypes

A stand-alone implementation of several NumPy dtype extensions used in machine learning.

C++347582 days ago
5jax-ml/oryx

Oryx is a library for probabilistic programming and deep learning built on top of Jax.

Python317131 weeks ago
6jax-ml/jax-ai-stackPython303475 days ago
7jax-ml/jax-llm-examples

Minimal yet performant LLM examples in pure JAX

Python256352 months ago
8jax-ml/bayeux

State of the art inference for your bayesian models.

Python242135 months ago
9jax-ml/bonsai

Minimal, lightweight JAX implementations of popular models.

Python236462 weeks ago
10jax-ml/coix

Inference Combinators in JAX

Jupyter Notebook5331 years ago
11jax-ml/jax-tpu-embeddingPython345Yesterday
12jax-ml/australisC++2853 years ago
13jax-ml/jax-blog625 months ago

Next step after the organization read

Open a flagship repository, compare a couple of portfolio leaders, or return to the organization map when you want a broader concentration read.
Open flagship repoCompare repositoriesBack to organizations

Learn and methodology

Keep trust-building context reachable, but behind the first data read instead of ahead of it.
GuideMethodologyArticlesWeekly Digest

How to read this organization snapshot

Total stars are useful as a discovery signal, but they do not tell you whether a team maintains every repository equally. Pair this page with release cadence, maintainer activity, and the flagship concentration shown above before making adoption decisions.

For broader background on GitStar's ranking logic and editorial guidance, see Methodology & Editorial Standards.