Hey everyone,

After building two watch faces with a real C. elegans connectome, I evolved a 9-neuron digital snake from scratch for Garmin

Some time ago, I made two experimental Garmin watch faces (NeuroWorm Quantum and NeuroWorm Fluid) based on the real biological connectome of Caenorhabditis elegans — the tiny worm that spent around 100 million years evolving its 302 neurons.

After playing with real biology, I got curious about the opposite approach: what if I compress millions of years of evolution into a few days of multi-threaded simulation on my PC, and grow a tiny brain from scratch specifically to live on a smartwatch?

So I built NeuroSnake: Evolved Lifeform as a spin-off to that series.

How it actually works (no fake AI):

• Zero hardcoded behavior: There is no pathfinding, no if-else rules, and no scripted logic telling the snake where to turn. Everything it does comes from a continuous-time recurrent neural network (CTRNN) forged through 10 million simulated lifetimes.

• Tiny brain, real constraints: Smartwatches have very limited CPU and RAM (especially older devices and monochrome Instinct models), so I had to keep it super compact. Right now, its entire brain is just 9 evolved interneurons and 30 synapses connecting its sensory rays to its muscles. My simulation is still running on my PC,so future updates might give it a few more hard-earned neurons.

• It adapts to you: Once it hatches on your watch, real-time Hebbian plasticity kicks in. Your daily steps charge the energy pool that spawns food capsules and sends ripples across the screen. After a few days on your wrist, the synaptic weights slowly tune to your personal movement rhythm - so your snake becomes uniquely yours.

• Don't feel bad when it dies: Sometimes it crashes into the clock digits or a fluttering anomaly. That’s just evolution doing its thing - the same process that built you, me, and this little digital creature.

I got a lot of inspiration from the scientists who spent decades mapping C. elegans, as well as the recent breakthroughs in mapping and simulating the fruit fly (Drosophila) brain. Honestly, I just feel huge gratitude to all the curious people throughout human history who kept digging into how nature works.

WatchFace is on review process for Garmin Connect IQ store as it will be approved I will add a link in the comments below (supports both AMOLED/MIP color watches and monochrome Instincts).

Happy to answer any questions about the neural net, the training process, or squeezing Monkey C performance on Garmin hardware!

(Note: English isn’t my native language, so I used an AI assistant to help me clean up this post and the store description, though I’m good enough with English to answer any questions in the comments!)

Hey everyone! I've been working on my custom Blender add-on, Blob Matter, for over a year now.

This is an extreme stress test with 120,000 fractured pieces-fractured in just 27 seconds using my custom fracture, RBD physics, and smoke backend. Even at this massive scale, the simulation solves at 1–7 FPS (while standard piece counts run significantly faster), and once cached, playback is buttery smooth in real-time right inside the viewport.

The add-on is designed for both fast workflows and complex, non-destructive setups, featuring both a standard UI panel and a fully-integrated node system.

Happy to answer any technical questions about the engine or the pipeline!

New look for the snow: instead of round beads it is now drawn as small blocks, each turned and shaped a little differently, and lit by the shape of the drift they lie in. Loose snow tumbles, packed tracks stay smooth (early prototype)

Source: Southern_Deer4830

@shipstack__ on instagram they have pretty cool physics animation

Fingers crossed I dont get banned into oblivions for this 😄

I'm dropping short clip form my upcoming game Strata - an early gameplay of a persistent open world where pretty much everything is real time simulated.

Thanks for viewing and the feedbacks!

Hey everyone. I'm working on a microorganism simulation app where you can design your own creatures from cell to cell, teach them survival skills, and observe them in their virtual petri dish, which you can also build yourself. I started a video series to document the progress. If you like soft-body physics simulations, reinforcement learning, and artificial life experiments, take a look and follow the progress.

Made with the open-source artificial life simulator ALIEN 5.0-beta: https://github.com/chrxh/alien

YouTube: https://youtu.be/Vxo0vdiDTTo

I'm learning the most basic simulation softwares from an environmental engineering course. This is the first time I ever simulated something.

It is supposed to be the steady state impact of an atmospheric flow on some vary unoriginal structures. The scope of later simulations will be to numerically reproduce so called urban canyons, a phenomenon in which - to my understanding - large vortexes are created in between buildings based on their heights and distances.

This first simulation was made to show us the softwares and setting up the parameters of what we had until now written and learned but never properly seen. In my ignorance, being only a student and having never done anything like this, I am amazed by it so I thought of posting it.

What I particularly like is that while playing with the thing I noticed that I could isolate an axis for the flow field. When isolating Y (vertical direction) I could recognize one of those dislocated vortexes I was always shown on images. Very cool stuff.

With your experience it would be nice to know what you would think of something so primitive.

I'm not sure about the flair, I didn't know which one to add

Simulation #1:
grid: 200x200
pressure function: ideal gas
initial condition: 27°C atmospheric air (γ = 1.4)
lid velocity: 2 km/s
runtime: 1 hours 30 minutes

Simulation #2:
grid: 200x200
pressure function: ideal gas
initial condition: 27°C atmospheric air (γ = 1.4)
lid velocity: 4 km/s
runtime: 1 hours 38 minutes

ㅤ

From what I see, their differences are in the initial stage. After that, they look pretty similar due to heating effect so I stopped the simulation since there's nothing new after that. The previous run was longer - to the point it nearly converges to typical high Re lid driven cavity flow.

In the part 2, it keeps the distinct characteristic for the rest of the simulation due to the lack of temperature effect as I was using isentropic (shallow water) pressure function. (the lid boundary condition was wrong in that simulation, but I can see what it'll look like with the correct BC.)

Next time I might do Mach ∞ run to see how long it converge to low Mach state.

Supersonic Lid-Driven Cavity Flow Series: previous next

I made it using JS and WebGPU. It runs in the browser and can easily manage 240 FPS with a 1920x1080 grid on an RTX 5080, only dropping to 200 fps when you fill the entire grid with water. All this extra performance headroom allows for much more realistic fluid simulation, fire, and post processing effects. The water still needs some tweaking, too viscous, but it's a start. Not sure if I'll continue working on it, but it was a fun experiment nonetheless.

Real-time MuJoCo physics, no keyframes. The walk and the recoveries come from a policy trained with reinforcement learning, and everything runs in your browser (MuJoCo WebAssembly, ONNX Runtime Web, three.js).

Try it yourself: https://www.coolspeed.org/gold-spider/

Setup details in the comments.

Screen capture from Planets in a Box, a planetary physics sandbox on Steam.
Recording sped up 2x.

About 1-2 million particles. SPH with Barnes-Hut gravity and a simplified equation of state for iron and silicate.

https://store.steampowered.com/app/5411970/Planets_in_a_Box/

So i've released it, but I'm already working on v1.1 because I'm not 100% happy with it. Two things I keep going back and forth on:

Heat: After a collision the molten debris stays glowing red for a long time as it orbits. Physically it should cool and go dark much sooner, but the glowing ring is the best part to watch. If it goes too dark too quick, you can't see it against the black starfield. Real and dull, or wrong and dramatic?

Particle size: You can actually adjust particle size with a setting to make them oversized, again so its more visible, at the cost of it looking a little cartoony. default has particles at 2x size. too big?

Two physics models: There's a Fluid Collisions mode that runs proper fluid dynamics for two worlds hitting. But a whole solar system breaking apart on a phone can't afford true fluid dynamics, so in Orbital Chaos the particle interaction is simplified with a handful of corner cuts: the breakup and the debris are approximated rather than truly simulated as fluid. Is it still convincing, or does it break the illusion?

It's on the app store if you want to give it a try.

https://apps.apple.com/app/id6822567730

A test of my Atlántica plugin for Blender: PF-FLIP on a 128 × 128 × 96 grid over an 8 × 8 × 6 m domain, giving 6.25 cm cells.

600 adaptive simulation steps cover approximately 9.85 seconds of physical time. This run uses the experimental CUDA pressure solver, with surface and whitewater cached during the bake.

Rendered in Cycles at 512 samples, played back at 24 fps.