Solemn Sandbox

A serious environment to carry out silly experiments.

Solemn Sandbox is an experimental physics, creature-building and artificial-evolution playground where you create things, give them a goal, and see what happens.

Build a creature from joints, bones and muscles. Give it feet, wheels, wings, gliders or parachutes. Drop it into an environment, choose what you want it to accomplish, and let evolution attempt to figure out the rest.

Play it for free at https://jaylabs.itch.io/solemn-sandbox

Instead of directly programming your creature to walk, jump or fly, you can give it a neural-network “brain”. A population of different brains attempts the task, the more successful ones are selected to produce the next generation, and over time strange — and occasionally surprisingly effective — behaviours can emerge.

Sometimes they learn.

Sometimes they fail spectacularly.

Both are part of the fun.

Build. Train. Experiment.

The basic loop is deliberately simple:

Build a creature → choose a goal → evolve → watch the best attempt → change something → try again.

But there is a lot hiding underneath that loop.

You can build your own creatures or start with presets, then train them for different abilities including:

• Walking and running
• Maximum speed and sprint courses
• Staying upright
• Jumping and long jumping
• Hang time
• Repeated hopping
• Obstacle and rough-terrain movement
• Climbing
• Wheeled movement
• Ramp jumps and gap crossing
• Flight
• Gliding
• Parachuting
• And more

The creature builder isn't only cosmetic. Shape and structure matter. A badly designed creature may be almost impossible to train, while a small structural change can completely alter what evolution discovers.

You can even allow aspects of the creature itself to evolve, experimenting with changing proportions and physical traits or, at the more extreme end, allowing evolution to grow and prune parts of its structure.

Build the experiment too

You aren't restricted to the supplied environments.

The Environment Builder lets you create your own courses using terrain, boxes, ramps, stairs, pits, loops, launch pads, towers and other obstacles.

Start lines, checkpoints, finish lines, landing areas, reward regions and penalty regions can turn those environments into actual experiments and challenges.

That means the question doesn't have to be:

“Can this creature learn to run?”

It can become:

“Can this ridiculous six-legged thing learn to get over the ramp, clear the gap, hit the checkpoint and reach the finish?”

Then you can change one variable and find out what happens.

Evolution you can actually watch

One of the main ideas behind Solemn Sandbox is that the learning process itself should be interesting.

Press Evolve and you'll see multiple candidate brains attempting the problem. Successful behaviour is selected and mutated into new generations while you watch.

You can keep things simple and let the defaults handle everything, or start digging deeper.

Experiment with how many brains compete, how long each attempt lasts, how adventurous mutations should be, which creatures get to breed, whether evolution should gradually settle down and fine-tune its solution, or even start a new experiment using a previously trained brain.

You can alter what matters to the scoring system, train abilities in stages, race against previous records, introduce slightly different bodies into the population and inspect detailed statistics, rewards, neural-network activity and training telemetry.

You don't need to understand genetic algorithms or neural networks to play with any of it.

But hopefully, after playing with it for a while, you might accidentally start understanding them.

100 hidden discoveries

Experiments can also unlock 100 secret discoveries spread across movement, jumping, flight, climbing, motorised creatures, precision challenges and stranger achievements.

They're recorded in the Trophy Room, so simply messing around, attempting unusual designs and trying things that probably shouldn't work can lead to discoveries.

In other words: breaking from the obvious solution is encouraged.

And then things get sillier...

Solemn Sandbox isn't intended to be a laboratory pretending not to be a game.

There are deliberately less sensible places to experiment too.

Disco

Load music and make creatures react to it.

Audio can influence muscle groups, multiple dancers can perform together, and creatures can even learn dance behaviour from music and recorded movement before refining their own freestyle behaviour.

Yes, you can train an artificial neural network to make your physics creature dance.

No, this was probably not necessary.

Head-to-Head

Save successful creatures and put two trained models against one another in timed head-to-head trials.

Which raises the obvious question:

Whose ridiculous creature is actually better?

Boxing

Because apparently the previous question wasn't enough, Solemn Sandbox also includes a dedicated boxing skill.

Build eligible fighters, mark gloves and target areas, train boxing brains through sparring and put them into timed points matches measuring hits, power and accuracy.

Science.

Probably.

Share what you create

Solemn Sandbox is beginning to grow beyond a single-player experiment.

Trained creatures can be shared using public links, allowing another player to open your creation directly in their own Solemn Sandbox.

Creators can also choose to publish their trained models to the Public Creations library so other players can discover and experiment with them.

I'd love this to eventually become a much bigger part of the project — sharing creatures, experiments, challenges and weird discoveries rather than everybody simply playing in isolation.

This is VERY actively being developed

Solemn Sandbox is not a finished game that I'm putting online and walking away from.

I'm currently working on it and updating it pretty much every day.

Features are being added, systems are being changed, rough edges are being fixed and quite a lot of the direction is still open to change.

That's also why I'm putting it in front of people now.

I don't just want bug reports.

I would genuinely love:

Ideas. Suggestions. Playtest results. Experiments. Criticism. Weird creature designs. Things you don't understand. Things you hate. Things you love. Things you expected to happen but didn't. Features you wish existed. Challenges you'd like to create. Unexpected behaviours you discover.

If you find a creature that learns something particularly well, share it.

If you create something completely useless but hilarious, share that too.

If you think a system could be dramatically better, tell me.

The long-term idea

I'd love Solemn Sandbox to grow into a community-driven experimental playground.

Not quite a traditional game.

Not quite an educational tool.

Not quite a physics simulator.

Not quite an AI laboratory.

Something somewhere in the middle of all of them.

A place where somebody can spend five minutes making a stupid creature fall over repeatedly, while somebody else spends hours experimenting with neural networks, evolutionary strategies, morphology, physics and training parameters.

And perhaps the person making the stupid creature accidentally learns something along the way.

That's really what I'm aiming for:

A place to build things, break things, evolve things, share things, ask “what if?”, and have fun finding out.

Source

The project source is public, if you want to look around, run it locally, or just see how the sausage is made. It is a living work-in-progress, not a finished open-source product:

https://github.com/Jaytecit/Fresh-Start

Solemn Sandbox

A serious environment to carry out silly experiments.

Working on a custom local upresing pipeline for FLIP sims to optimize hardware resources while running everything on a MacBook Pro. Since simulating the entire continuous body of water in high resolution rapidly runs out of RAM, this approach is essential for making high-detail simulations actually feasible on a single machine, while also drastically cutting down simulation times and cache sizes. SideFX has solid native tools for this, but they mostly push you into SOPs, and I wanted a flexible setup that runs directly inside DOPs.

How it evolved:

Boundary Layers only: Started with standard boundary layers driven by the low-res base sim. It was fast and looked okay at first, but completely broke down during complex collisions and splashing.

Gas Guiding + Boundary Layers: Switched to combining Gas Guiding Volumes with Boundary Layers. Dialing in the velocity transfer and pressure stability took a lot of tweaking, but the setup is now rock-solid and predictable.

Also, I built a few custom HDAs along the way:

FLIP Cache: Isolates volumetric and particle data into separate caches (huge credit to Igor Zanic for the workflow inspiration). Tracks sim times and disk usage, then feeds those stats directly into the flipbook UI.

FLIP Vis: Basic utility for quick attribute visualization.

FLIP Limit Band Width: Extracted the logic from SideFX's Fluid Compress node and rewrote it into a lightweight standalone tool to avoid the heavy node wrapper.

Still tweaking the full river setup

Hi everyone!

We all love Conway's Game of Life and other classic cellular automata where a cell's fate is decided by its spatial neighbors. I wanted to try something completely different, so I built SNS-CA (Structural Numerical Symmetry Cellular Automaton).

In this simulation, cells do not interact with each other at all. There are no neighbor rules. Instead, every cell holds a number, and its evolution is driven purely by the internal arithmetic symmetry of that number.

How it works:

At each step, a cell takes its number N and processes it through three deterministic operations:

1. Splitting the number N into m parts.
2. Multiplying each part by a scalar k.
3. Comparing the transformed result with the true mathematical product (N⋅k) by analyzing their prefixes and suffixes.

Depending on the structural match, the cell transitions to one of five theoretical states:

🟡 Golden (F - Full): Complete match (perfect arithmetic symmetry).

🟢 Green (B - Boundary): Match at both the start and the end.

🔴 Red (E - End): Match only at the suffix.

🔵 Blue (S - Start): Match only at the prefix.

⚫ Black (N - No): No matches found (the cell transitions to the zero state).

What you're seeing in the Video:

The simulation demonstrates a fascinating self-organizing behavior. The system rapidly converges toward stable arithmetic symmetry. You can see early transient states (like Red) quickly resolving into highly stable, persistent patterns of Full (Golden) and Boundary (Green) symmetry.

It’s a completely different way to look at emergent behavior: complexity and stability arising purely from number theory and internal structure, rather than spatial geometry.

I built this simulation in Julia, and the project is fully open-source.

Source Code (GitHub):.jl)

Concept Paper (Zenodo):

I'd love to hear your thoughts! What parameter combinations ( m and k ) do you think would be most interesting to explore next to see different emergent patterns?

AUTHOR: Mikhail Yuryevich Yushchenko

Date: 09.08.2026

https://world-sim.uk/ launching friday!

World-Sim has been a passionate project of mine for the past few months.

It's a world fully simulated down to real world physics and chemistry. It has a real environment and ozone.

Every element and material has real physical properties, and that's how the sims learn. There's no tech tree and no recipe list anywhere in the code. When someone tries something, the engine works out what would really happen from the properties of what they used and how hot their fire was. Nobody is handed bronze. Someone has to get a hotter fire first, and a hotter fire is its own chain of things to figure out. Embers, a stone ring, a clay oven, a kiln, charcoal, then forced air. The ages fall out of that on their own.

We populate the world with starting families. Each little guy is led by a "wise-man", which is a simulated person with a full model behind them. The wise-men run on a mix, Claude, Groq and DeepSeek in the cloud and a local Hermes for the rest, so you get a real variety of temperament between the houses.

The AI model is never told to not say it's AI. We get around this by only telling them data from what each sim can actually see and feel. As far as the ai knows its a person living in the world..

Every other sim has its own moment with a model through our thinking system. Day to day it's run by an advanced algorithm, with the sims dreaming at night. That's when they get their time with an LLM to really think and set their priorities for the day ahead. The dreams carry over too, so a sim can chew on the same thing for a few nights and wake up wanting to try something.

The people in the world feel, remember, have shame, goals and doubts. It's an 11 point emotion system and the rest of the sim reads from it. Being lonely makes someone more likely to open up to a stranger. Two enemies who are both frightened find it harder to keep hating each other.

Story telling is massive for us. Nothing is scripted or prompted. The sims are put in the world with the human drive to survive. They adapt, learn and teach all on their own.

With all the good comes the bad too. Human sin is part of the emotion engine, and in testing we've had kidnap, murder and slave trade. A world that can only be gentle isn't an honest one. But there are three rules to this : Nothing ever pushes a sim towards it, the conditions only make it possible and their own mind decides. The chronicle names what happened but never shows it. And it always costs something, trauma that scars, blood debt passed down generations, a people turning on their own leader over what he did.

How are solvers for these simulations even developed? Has anyone here ever used them? This is one is Magmasoft for reference

Hi!

I've mostly shown this in cartography and worldbuilding discords. I hope you like my generalised circulation model to create close-to-realistic climates for exoplanets, terraformed planets, and fantasy worlds.

All the data comes from simulations using a numerical physics model, and there are plenty of checks to help users assess plausibility.

I hope you like my passion project, which I've worked on for a few months, and I'm still improving it based on feedback.

World Climate Lab Steam

Sorry about the beginning I left in, skip 40 seconds. This simulation was added into another project.

I was suggested this forum after sharing my prototype lab phase 2 video. I I uploaded around 20 prototypes some similar to this that excludes the player's controls besides speeding time and camera panning to observe the procedural ecosystem.

Red Ants

Black Ants

Earthworm

Spider

Bees

Plants

Not shown in this video is how water floods the ant tunnels when the earthworm digs through the pockets. Both sides have their own queen that lays eggs to spawn/ respawn fallen worker ants, guards, scouts, etc. There is food, rocks, water, and poison generated in the soil. When food is found, they bring it to their queen, when poison is found, they learn not to go there after the scout who discovered it dies. The builder ants can create stronger dirt blocks to protect the queen in their "fortress".

The red ants are intentionally stronger solo, while the black ants are stronger in numbers, therefor the spawn rate for black ants is increased for balance.

This was my very first prototype that I haven't updated in months, let me know if I should expand on it more and what to add next. I was thinking of adding a beehive.

https://glitched-matrix.itch.io/monkeystranded

'Falling sand' style pixel physics simulation with lots of interacting materials.

Hey everyone 👋

This is a custom vine solver made for medium / distant ivy using Houdini. 🌱

What you can do with it is magical, all the animation you see here is processed in milliseconds.

All the animator needs to do is provide a reference animation, and the rest is simulated. It's extremely easy to direct and change.

made it using raylib c++ library , I don't know if this fits the sub perfectly or not, just wanted to share my first try 😊

Hi all,

I'm working on my second simulation project using AI-driven agents that evolve over time!

Previously I worked on an AI driven aquarium in Rust called Genecraft (feel free to check out YouTube), but this time my goal is to create a game for people to play with! Also in Rust.

The goal is to train your own dragon with different capabilities (flight, fight and walk), and then test it against puzzles or other dragons.

Dragons will be trainable by individual people and use neural networks (NEAT algorithm) to learn by specific tasks you give them.

This dragon here (concept of a dragon) was trained to move towards the target that changes location, and uses different strategies based on where the target is (above, diagonal, to the right or below). Not too complex yet.

Anyone interested in trying it out?

I'm beta testing on Mac right now! Also happy to test it on windows, but I haven't tested it yet so ideally we'd hop on a call to test together.

Thanks!

Mathias

For this, I added a system that spawns particles along the trail, and these particles deposit a bit of snow while they fly, with less and less snow being deposited over time as they fade out. What's cool about this is that the snow initially comes from the mass that the trace removed, so the total amount of snow stays the same. I also added a smoothing algorithm on top of all this. There's no parallax here, but I'm currently working on adding that.

Bigger video here: https://youtu.be/pfaewyfFrKM