The Gulf is pretty shallow, about 35 meters on average, and only 11.9 meters deep at the northwest end. During the last ice age the sea level was 120 meters lower, so the whole basin was completely dry. The exploration ship Meteor confirmed it: an entirely dry basin around 15,000 BC.The Tigris, the Euphrates and the Karun all met up and flowed the entire length of it as one single river, heading out through the Strait of Hormuz. That old channel is still there. You can see it clearly in the bathymetry.
it wasn’t empty. There was a floodplain down there with vegetation, wildlife, and Neolithic communities living on it.Then the ice melted. Sea level rose 150 meters between 12,000 and 7,000 BC, and at the fastest stretch the shoreline was advancing ten meters a year. They didn’t get washed away. They just got pushed out, one salty well at a time, over generations.Bahrain’s name is the dual form of the Arabic word for sea, meaning two seas. One reading of it is the salt water above and the fresh water below. Pearl divers have said for centuries that in certain spots you can flip a bucket on the seabed and bring it up full of fresh water. That’s the old river system still draining.The clip is that basin emptying, run in a planetary model I’ve been building. I used a different method. I closed the strait and let it evaporate instead of dropping global sea level, but the same floor comes back out and the river shows up right where it’s supposed to be.

An origami crane is flying in a mirror cube. The camera is a rear shot following the origami crane as it flies inside the cube.

A while ago I posted a version of this on a circular track. Two things came up in the comments often enough that I decided to rebuild the whole thing.

The first was the loop itself. Several people said it was distracting, and that it was hard to tell whether a jam was really moving backwards or whether the track was just going round. The second was lane closures - a few of you asked what would happen if one lane were blocked. So this time the road is straight, it has four lanes and one of the cases is a closed lane with the merge that follows it.

In the phantom-jam case there is no accident and no obstacle at all. The density is simply high enough that one driver braking a little harder than necessary gets amplified by the cars behind, and the wave then travels backwards along the road while every individual car keeps moving forwards.

There is one comment from last time that I want to answer. Someone wrote that the narration read like AI slop. That is fair. English is not my first language, so I use AI assistance for the wording. The simulation, model and idea are my own work, the phrasing had help. I am trying to get better at that part.

I should also be clear that there is no income expectation in this. The channel is not monetised and I am not expecting it to be. I am doing it as a hobby, to learn how simulation and visualisation actually work, so that I can use them properly in my own field. Youtube is simply where the files are stored.

The full version, with the other cases and a proper explanation, is here:

https://www.youtube.com/watch?v=msX3kewLRIQ

What you see here is the short version. The full one works through the cases I had to leave out. It covers where the tipping point actually sits, which is the density above which the road stops recovering from a disturbance, why the lane next to you nearly always looks faster than the one you are in, what a small proportion of slow trucks does to the flow behind them, what tailgating costs you compared with keeping your distance, which was not the result I expected, and what changes when roughly one car in five drives itself. It ends with the few things a driver can actually do to avoid making a jam worse.

Along with other essential features for a proper muscle simulation tool. The good thing is that I’m using it as a character artist while developing it, which allows me to find the most artist-friendly workflows and implement them directly into th

This runs live on an Apple silicon GPU.

Every bird reacts only to the ones near it. Nothing choreographs the shape and nothing is keyframed, so what you are watching falls out of 137,500 of them following local rules at once.

It is a GPU flock on an atomic spatial hash, which keeps neighbour lookups linear instead of quadratic at that count. Each bird steers on a bounded turn rate and banks into its turns, and the banking is what makes the flock flicker as they roll.

Based this on starling literature rather than plain boids, mostly Cavagna's work on scale-free correlations in real flocks and Couzin on spatial sorting. Cool thins is that the same system with one flag flipped runs a bait ball instead: change the attractor from polarised streaming to milling and the sheet becomes a rotating sphere.

Built in Swift and Metal as part of TideGlass, a small Mac app I make where worlds like this run on a second monitor while you work. Happy to answer anything about the sim.