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google
liquidfun
C++2014–2021
google / liquidfun · Archived

liquidfun

2D physics engine for games

2Distinct people
4Mentions
0Explicit recommenders
4.9KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

2D physics engine for games

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, 12:54:37 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 year4 dated mentions

Observed mentions: 2014-03-20 06:08:55 UTC to 2021-08-02 11:34:44 UTC.

01232014-01-01 00:00:00 UTC to 2015-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments20142015-01-01 00:00:00 UTC to 2016-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20172018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2021

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
20142014-01-01 00:00:00 UTC2015-01-01 00:00:00 UTC110
20152015-01-01 00:00:00 UTC2016-01-01 00:00:00 UTC000
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC000
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC000
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC000
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC000
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC000
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC303

What Hacker News says

4 sources
yeah, I've played around with a few approaches for running the timestep and for some reason I don't feel like I get the same results as liquidfun.js. their loop [0] is pretty simple; it's scheduled by `requestAnimationFrame`, advances time by 1/60th of a second, and runs their default of 3 particle iterations. it completes the physics simulation within 3.9–5.5ms, which is easily in time for the 16ms deadline. the rendering is WebGL, which I assume fits easily into that 16ms budget too. my loop [1] is more complicated; I don't hardcode the timestep to 1/60 seconds, because requestAnimationFrame may be scheduled less frequently than that. so instead I advance time by the time elasped since I was last scheduled. hm, I think there's a mistake there — `lastMs = nowMs` is probably on the wrong side of the physics calculation. there's an additio
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nal technique I use: I put a `Math.min()` over the simulation interval, so that I don't attempt to simulate more than 20ms (this can happen if you get scheduled infrequently due to hot CPU or backgrounding the app) — simulating too much time will make us fail our frame deadline anyway. furthermore, if we are calculating more than 1/60th of a second, I employ more particle iterations (i.e. 3 particle iterations for every 1/60th of a second that passes). this gave me good results, but turns out it is based on incorrect assumptions (iterations are unrelated to timestep)[3]. moreover, I may be making mistakes in my decision of whether to round this fraction up/down. if too few particle iterations for a timestep: the particles will bounce. if too many: the particles will look too incompressible[4]. I think that's the "solid-like" structure you're describing. the main reason I complicated this is because the last one I did[5] made me feel motion-sick. I think if "every 1/60th, or 1/30th, or 1/20th of a second: you simulate a 1/60th of a second of time": the result (if you're not scheduled consistently) is that the world speed keeps changing. I think liquidfun.js's approach should be vulnerable to this, but for some reason it looks fine to me. maybe they get scheduled more consistently than me (even though by my measurements, my physics runs slightly faster, so should be able to achieve similar results). I think I need to remind myself of what happens if I program the timestep in the simple way that liquidfun.js did. will try that out at some point. [0] https://github.com/google/liquidfun/blob/master/liquidfun/Bo... [1] https://github.com/Birch-san/liquidfun-play-2/blob/master/sr... [2] https://github.com/Birch-san/liquidfun-play-2/blob/master/sr... [3] http://google.github.io/liquidfun/Programmers-Guide/html/md_... [4] http://google.github.io/liquidfun/Programmers-Guide/html/md_... [5] https://birchlabs.co.uk/box2d-wasm-liquidfun/
In Show HN: WASM and WebGL Fluid Simulation
Author here. This demo showcases liquidfun-wasm[0], my effort to revive liquidfun[1] (a fork which adds fluid simulation and soft-body physics to Box2D[2]). to make liquidfun-wasm, I repurposed my existing box2d-wasm[3] and pointed it at a different release of Box2D — a commit obtained by rebasing liquidfun over 7 years of upstream Box2D changes[4]. the end result is that liquidfun is now distributed in WebAssembly and with TypeScript typings for the first time. The TypeScript typings are generated from WebIDL bindings via my webidl-to-ts[5] compiler. this demo in particular aims to bring to the Web the shaders from the liquidfun EyeCandy demo[6], and show how fast JS can run if you avoid incurring the garbage collector (the main loop tries not to allocate objects). the demo repurposes gravity and drag calculations that I'd used previousl
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In Show HN: WASM and WebGL Fluid Simulation
yes, I compiled with -msimd128 to enable LLVM's auto-vectorization. I distribute both SIMD and non-SIMD, and the entrypoint picks whichever distribution your browser supports. for box2d-wasm, SIMD acceleration resulted in a 0.6–0.9% performance boost [0] when simulating a pyramid of boxes. liquidfun-wasm is a fork with additional algorithms for performantly simulating particles. I have not yet built a benchmark to measure the particle code, but do intend to. I am optimistic that liquidfun's particle code could auto-vectorize better than the general Box2D code. the Google engineers considered how to take advantage of SIMD, to the extent that they even ship a NEON SIMD algorithm[1]. I don't believe my compiler config will use that NEON algorithm (and will instead fallback to the general algorithm [2]). that's probably not a missed opportuni
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ty; many NEON features are not supported[3]. but since the engineers were thinking about SIMD, hopefully the non-NEON algorithm will try to make good use of the CPU and memory layout too, and auto-vectorize well. [0] https://github.com/Birch-san/box2d.ts/pull/1 [1] https://github.com/google/liquidfun/blob/master/liquidfun/Bo... [2] https://github.com/google/liquidfun/blob/master/liquidfun/Bo... [3] https://emscripten.org/docs/porting/simd.html#compiling-simd...
In Show HN: WASM and WebGL Fluid Simulation

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