- > In 2010, a team led by Andrea Cavagna at the University of Rome tracked individual starlings in 3D using multiple synchronized cameras. And they found something surprising!
> In 1986 Craig Reynolds encoded this insight as three rules:
This part just left a really bad taste in my mouth. I am not against using LLMs to write stuff but please proof read what it writes before posting it.
The way it's written it's sounds like Craig Renault travelled in time to 2010 to come up with his rules for boids
by catlifeonmars
2 subcomments
- I found this difficult to read due to the LLM-isms, but love the concept. Gentle feedback: take the time to write articles in your own voice.
- I have to second the complaints about LLM writing. The tropes were grating, to the point where I hit the back button before ever learning what the difference between a boid and a noid is.
Ecto, I see that you’re reading and responding to comments. In your own words, concisely, and assuming I know what what boids are: what sets this apart?
- Cool stuff! It's an interesting approach to the starling phenomenon. I'm familiar with the phenomenon through the lens of phase transitions and the critical point, which you allude to in the article briefly. Any further thoughts on how your neural-network based approach maps conceptually to the critical point and related models of emergent behavior?
by cadamsdotcom
1 subcomments
- Very cool and I love the visualisations..
There’s a saying, “people are smart but crowds are dumb”. One wonders if humans in crowds subconsciously do something like flocking.
- This is awesome. I think I've heard of other research that's similar to try and speed up Navier-Stokes or other water/smoke/etc. simulation.
But this isn't actually recreating murmurations, is it? This is a neural network that's using the Reynolds criteria as a loss function, with Cavagna's topological neighbors?
As far as I know, there's no good research that reproduces the murmations seen in starling flocks. This seems like it would be a good use case for neural networks but I don't know of any publicly available 3d data of actual starling flocks, aside from some random YouTube videos floating around.
- Wow, this post is beautiful. Well done, great read.
- Is this some OpenClaw blogging setup? I've seen similar posts [0] on Twitter lately (not from OpenClaw, but maybe the claw is getting the idea from there).
[0] https://x.com/fleetingbits/status/2028669892686438818
- Huh, this just gave me an idea (provided a few modifications and enhancements to the noids) to create a god game with true emergent behaviour (yes, that's not very gamey like, I know, it can collapse for no reason). Let's see if I'm smart enough to pull it off (note: I'm waaaay over my head in this)
by friendo_fez
1 subcomments
- Really great work! I would love to know how this could be extended to handle additional information. Things like walls and other environmental factors, pathfinding, keeping formations, etc.
- Boids are little lovely simulations. This just looks like a boring force directed graph. I wonder if there is any correlation between the blandness of LLMs and weight based models.
by adammarples
4 subcomments
- What's the point of training a nnet on outputs from the original 3 rules so it can effectively just relearn them?
by titanomachy
2 subcomments
- > That's the whole mechanism... Local perception. Local action.
> it’s not communication. It’s physics.
Dude, if you’re going to go to all the trouble to make something cool why don’t you take like 20 minutes to write in your own voice about it! I’m so tired of reading robot slop.
- Is this not just self organization?
- It's sad to see an LLM take over a blog, because you can see the line: before 2026 it's an interesting person you would like to talk to. After 2026, it's like generic LLM marketing-voice copy.
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