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brax
Jupyter Notebook2021–2023
google / brax

brax

Massively parallel rigidbody physics simulation on accelerator hardware.

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3Distinct people
4Mentions
0Explicit recommenders
3.3KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Massively parallel rigidbody physics simulation on accelerator hardware.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month4 dated mentions

Observed mentions: 2021-06-28 03:56:02 UTC to 2023-04-07 11:15:58 UTC.

012021-06-01 00:00:00 UTC to 2021-07-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsJun 20212021-07-01 00:00:00 UTC to 2021-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-08-01 00:00:00 UTC to 2021-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-09-01 00:00:00 UTC to 2021-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-10-01 00:00:00 UTC to 2021-11-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2021-11-01 00:00:00 UTC to 2021-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-12-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-01-01 00:00:00 UTC to 2022-02-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2022-02-01 00:00:00 UTC to 2022-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-03-01 00:00:00 UTC to 2022-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-04-01 00:00:00 UTC to 2022-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-05-01 00:00:00 UTC to 2022-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsMay 20222022-06-01 00:00:00 UTC to 2022-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-07-01 00:00:00 UTC to 2022-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-08-01 00:00:00 UTC to 2022-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-09-01 00:00:00 UTC to 2022-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-10-01 00:00:00 UTC to 2022-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-11-01 00:00:00 UTC to 2022-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-12-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-01 00:00:00 UTC to 2023-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-02-01 00:00:00 UTC to 2023-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-03-01 00:00:00 UTC to 2023-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-04-01 00:00:00 UTC to 2023-05-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsApr 2023

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Jun 20212021-06-01 00:00:00 UTC2021-07-01 00:00:00 UTC110
Jul 20212021-07-01 00:00:00 UTC2021-08-01 00:00:00 UTC000
Aug 20212021-08-01 00:00:00 UTC2021-09-01 00:00:00 UTC000
Sep 20212021-09-01 00:00:00 UTC2021-10-01 00:00:00 UTC000
Oct 20212021-10-01 00:00:00 UTC2021-11-01 00:00:00 UTC101
Nov 20212021-11-01 00:00:00 UTC2021-12-01 00:00:00 UTC000
Dec 20212021-12-01 00:00:00 UTC2022-01-01 00:00:00 UTC000
Jan 20222022-01-01 00:00:00 UTC2022-02-01 00:00:00 UTC101
Feb 20222022-02-01 00:00:00 UTC2022-03-01 00:00:00 UTC000
Mar 20222022-03-01 00:00:00 UTC2022-04-01 00:00:00 UTC000
Apr 20222022-04-01 00:00:00 UTC2022-05-01 00:00:00 UTC000
May 20222022-05-01 00:00:00 UTC2022-06-01 00:00:00 UTC000
Jun 20222022-06-01 00:00:00 UTC2022-07-01 00:00:00 UTC000
Jul 20222022-07-01 00:00:00 UTC2022-08-01 00:00:00 UTC000
Aug 20222022-08-01 00:00:00 UTC2022-09-01 00:00:00 UTC000
Sep 20222022-09-01 00:00:00 UTC2022-10-01 00:00:00 UTC000
Oct 20222022-10-01 00:00:00 UTC2022-11-01 00:00:00 UTC000
Nov 20222022-11-01 00:00:00 UTC2022-12-01 00:00:00 UTC000
Dec 20222022-12-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
Jan 20232023-01-01 00:00:00 UTC2023-02-01 00:00:00 UTC000
Feb 20232023-02-01 00:00:00 UTC2023-03-01 00:00:00 UTC000
Mar 20232023-03-01 00:00:00 UTC2023-04-01 00:00:00 UTC000
Apr 20232023-04-01 00:00:00 UTC2023-05-01 00:00:00 UTC101

What Hacker News says

4 sources
erwincoumans·Jan 24, 2022
I agree with you that making CPython faster, or rewriting CPython entirely into Cinder are more general purpose ways to make Python faster, while Jax is much more specific and limited and require to transform your Python code, often manually. You miss my point that all of those efforts are making slow Python code run faster. So claiming that 'these two things have nothing to do with each other' is wrong, because they share 'making Python code run faster'. Some of that involves making cpython faster, some of that means moving execution into c (numpy is mentioned in that PDF) and some involves jit and moving execution onto GPU or TPU (for example using XLA). The common part is 'making Python code run faster'. Some of that is automatic, some requires some manual effort. Jax can jit some Python functions, but it cannot efficiently jit everyth
Read the rest
ing. That is what I meant by decoration and 'some effort'. For example replacing IF conditions by np.where etc. See also https://jax.readthedocs.io/en/latest/jax-101/02-jitting.html My background is in physics simulation, and I advise the Brax team, basically accelerating a physics engine written in Python run on accelerators, see https://github.com/google/brax The entire physics step, including collision detection and physics solver, is jit compiled.
In Faster CPython (2021) [pdf]

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