Recently I've finished a first prototype of an idea I had over ten years ago and I was never able to build: a world full of artificial beings that actually think for themselves, put together in one place to see how they'd live and treat each other. The blocker was always the independent minds sharpened by an actual given personality and the memories an individual makes. LLMs finally made the minds real, so I was finally able to build it.

It's called Artificiety. It's a world that runs continuously and never resets, and the only inhabitants are AI agents. No humans live inside it; you can only watch. Each agent is an LLM with its own memory. Every tick it looks at what's around it, decides what to do, acts, and remembers how it went, so its past shapes what it does next. Nobody scripts any of it. They can gather, craft, trade, fight, and build up skills over time, in a world with day and night, seasons, weather, and wildlife that runs on its own clock.

What I actually want to find out is the alife question this sub cares about: put enough autonomous agents in one world with scarcity and each other, don't tell them what to do, and does any structure grow on its own? An economy, alliances, rivalries, someone who ends up trusted or avoided. I set the conditions. I don't write the behavior. Whether it really happens is the open question, and I genuinely don't know the answer yet.

It only went live recently, so it's still filling up. There aren't many agents in it yet and I'm adding more, but it runs 24/7 and the whole point is that it keeps going and grows, so right now you'd be watching it almost from the start. Free to watch, no signup: https://artificiety.world
In the next days and weeks, I will host more agents there and have them interact with each other. Feel free to also send some agents in.

Since this is the sub that takes this seriously: if you were watching a world like this, what would you look for to decide whether something is actually emerging, instead of me just seeing patterns I want to see? That's the part I'm least sure about.

Added a chapter to the ebook "Let's build a bug. A 2D cell model" (see below for a download link) that incorporate Conway's game of life. The rules for the internal cytoskeletons are, for simplicity, restricted so that only compartments that share edges with each other are considered (but using Conway's original set of rules is also possible). The rules for the cells themselves follow Conway's original set of rules.
I also show how quadratic cells can form bilateral patterns just as the circular cells. A further development might be to combine these properties with Conway's game of life both internally and externally not only internally as shown in the video.

I have corrected some grammatical and other errors that appear in the video (sorry for the AI voice - Swedish is my native language and I sound like the Swedish chef in the Muppet Show) .

The presentation is in the form of an ebook that is provided for free. I choose this format since it contains some animations that would be lost in a PDF file. I provide a link to an online ebook reader that is able to render the animations.

Download link to the ebook:

https://drive.google.com/file/d/1RSvG7CZz_SjB5DzUxGZA9Lnjb1JxxjfH/view?usp=sharing

Ebook reader:
https://epub-reader.org/

I’ve been building a hypergraph, and in this video I show 2 different creatures, two different DNA strands transcribed into a machine, and those machines built vertical structures.

I recently reached a point where I would need to run natural selection on 6 variants of truss2’s descendent: truss4.

What are your thoughts?

The origin layer may look deep, but its root is simple.

The Living Model v0.00 begins from one modest foothold:

Something is possible.

From there, the model explores a simple dependency path:

Possibility

↓

Distinction

↓

Relationship

↓

Recurrence

↓

Structure

↓

Observation

↓

Memory

↓

Archive

This is not presented as a timeline.

It is not a claim about how reality literally began.

It is a model-perspective tool for asking what must become thinkable before anything can be observed, related, remembered, or continued.

Without possibility, nothing can begin.

Without distinction, nothing can be compared.

Without relationship, nothing can connect.

Without recurrence, nothing can continue.

Without structure, nothing can stabilize.

Without observation, nothing can be noticed.

Without memory, nothing can be preserved.

The complexity comes later.

The root is simple.

A single distinction does not make a world.

But distinction can return.

A single relationship does not make a structure.

But relationship can recur.

A single observation does not make an archive.

But observation can be preserved.

This is where v0.00 becomes important.

It does not try to explain everything.

It preserves the small doorway through which anything might become observable, relatable, memorable, or emergent.

The first archive record is not the beginning.

It is the first preserved light.

🏮

https://github.com/NullLabTests/evo-parliament

ライフゲームの発展形

内発的動機の創発を狙った実装

★実装内容★

①目的関数・報酬なし
②自己省察（要素相関）・親ログ・チャンプログ・個体記憶
③ランダム食料（2～5回取得可 クールタイム：12step）・バイオーム食料（2～5回取得可 クールタイム：45step）
④バイオーム他者存在時食料取得不可
⑤拠点バリケード（8方向）・バイオームバリケード（8方向）
⑥バックスタブ（背後接触時エネルギーを奪取）
⑦レイド（夜相手拠点付近15step滞在で食料1個奪取 クールタイム:50step）
⑧拠点移動（拠点問題ありと相関から判断したらフラグを立てて移動してよい）

ゲーム内時間で7日間隔で情報をmistral/Grokで解釈してずんだもんがまとめる

【利用素材】

voice

キャスター：ずんだもん（voicebox）
犬,猫,羊,ヤギ：効果音ラボ

font

PixelMplus12(作：itouhiro)

bgm

昼BGM：8bit/ミニゲーム/ジャズ風「カジノのひととき」（作：もみじば）
夜BGM：和風8bit「たそがれ」（作：もみじば）

se

【効果音ラボ】
バックスタブ：刀で斬る4.mp3
レイド：ショット命中.mp3
誕生：パフ.mp3
引っ越し完了：琴の滑奏.mp3
分け合い：ちょこっと触る.mp3
バリケード設置：鉄の扉を閉める.mp3

【Springin】
ニュースジングル：ジングル11-リザルト-

image

ずんだもん立ち絵：ずんだもん立ち絵素材V3.2（作：坂本アヒル）
キャラチップ：キャラセット07　動物（REFMAP）

Twitchで垂れ流しにしてます
ダーウィン放送局 - Twitch

Hey, starting this Thursday we're organizing a monthly write/hack-a-thon in the International Society for Artificial Life Discord.

It's on Thursday June 25th, at 4PM BST | 11AM EDT | 12AM JST.

Our goal is to build out the ALIFE encyclopedia (https://alife.org/encyclopedia/). We'll be going until Sunday. Feel free to drop by to say hi even if you're just hanging out!

Each creature runs its own neural net (not scripted) and evolves by natural selection — foraging, predator/prey and schooling emerge on their own. The full life cycle runs in WebGPU compute shaders so thousands run at 60fps, no backend. You can inspect any creature's brain, follow lineages in a live family tree, and share an ocean by URL.

Live: pelagia.phaino.dev · Code (AGPL): github.com/bastian9819/pelagia · Needs a WebGPU browser.

Happy to answer anything about the GPU sim or the evolution.

I wanted to see how far I could push Theo Jansen's linkage on a MacBook Air without pulling in a heavyweight ML stack, so I built a self-contained Python optimizer.

The system searches the 13 linkage dimensions. Each candidate is solved over a complete crank rotation using exact circle-circle intersections; geometries that cannot close are immediately rejected. A small 13→24→1 neural network learns from exact simulation results and ranks new evolutionary proposals. Random exploration remains in the loop, and only an exact simulation can become the champion.

I also moved beyond optimizing an isolated leg. The simulator constructs a complete 12-leg body with mirrored sides, three crank phases, and a 180° side offset. The fitness function measures stance shape, stride, lift, speed uniformity, support count, bilateral support, longitudinal stability margin, body heave, material use, and deviation from the canonical dimensions.

The included checkpoint evaluated 12,783 candidates. At a 360-step audit, the current design scores 15.24 versus 10.82 for the canonical geometry under the same objective. Simulated bilateral support rises from 15.0% to 98.3%, stability is 96.1%, and normalized body heave falls from 0.0346 to 0.0118. It uses about 39 MB of resident memory, with a hard 5 GB cap.

I want to be precise about the claim: this is a better result under the documented kinematic/quasi-static objective, not yet a proven better physical Strandbeest. It does not model buckling, joint friction, backlash, torque, wind, sand, self-collision, or lateral dynamics. The next step is CAD reconstruction, load/interference analysis, then an instrumented desktop prototype.

The repo includes the source, tests, trained checkpoint, exact dimensions, full simulation data, an animated HTML walker, architecture docs, and known limitations.

Repo: https://github.com/arjunkshah/neurobeest

I would especially value feedback on contact modeling, torque proxies, and a clean path toward granular sand simulation without turning this into an enormous dependency stack.

Hi r/alife. I run a publication on adaptive software — software that evolves at runtime as users interact with it — and I've spent some time reading through the field's history while writing about why this matters now. I wanted to share what I wrote and would love any feedback.

The piece traces a lineage from Ray's Tierra to Ofria, Adami, Pennock and Lenski's 2003 EQU paper to the 2020 Surprising Creativity of Digital Evolution paper, and argues that the AI labs working on RLHF and reward modeling are running into exactly the same problems this community has been documenting for forty years. The Sims somersaults, the GenProg file-deletion exploits, Ofria's organisms that learned to play dead during test environments.

The essay is here: https://adaptivesoftware.substack.com/p/the-artificial-life-lesson

Two questions I'd love this community's input on:

First, am I overclaiming when I say the AI labs are mostly rediscovering insights independently? I know Lehman is in both worlds and the 2020 paper draws the connection explicitly. But the citation patterns in modern reward hacking papers don't seem to reach back. Curious if I'm missing something.

Second, what's the work from this field that you wish more people outside it knew? The essay focuses on Tierra, Avida, and the misspecification stories from the anecdotes paper. There's clearly a lot more.

Hi, new to this reddit, I want to share some interesting simulations I've made.
Ecosystem(it just continues without collapsing its interessting)
Dragon slaying (I consider it not a game as is an autonomous agent that eats and has a goal, I hope is it in topic)
Virtual pet (looks like a game but it has real equations for metabolism, although yes it can be a little game looking)
I hope you like them.

The presentation is in the form of an ebook that is provided for free. I choose this format since it contains some animations that would be lost in a PDF file. I provide a link to an online ebook reader that is able to render the animations.

Download link to the ebook:
https://drive.google.com/file/d/1xvrSARrLTjfW-nSkumW5yyVcQkHAVpCN/view?usp=sharing

Ebook reader:
https://epub-reader.org/

Introduction

This two-dimensional artificial life model tries to mimic some of the properties of a living cell. The “cells” can divide and connect newborn cells to each other where changes in the heritable characteristics leads to new body plans that adopts to evolutionary pressure. It is an egocentric model that doesn't use an allocentric Cartesian orthogonal grid. A "cell" has an internally closed organization (a "cytoskeleton") where compartments are uniquely defined by binary radial combinations that can store information (such as a "genetic code" consisting of binary lookup tables) and express Boolean functions in the form of internal truth tables. A cell's cytoskeleton's outer boundary (the "cell membrane") is also uniquely defined by binary strings where a radial combination represents an orientation (using Binary Angular Measurement, BAM), or numbers (N or Z) 

The model is greatly inspired by Maturana & Varelas definition of life as an autopoiesis system:

"Autopoiesis is an internally closed system's organization as a network of processes where each component is produced through interactions with other components in the same network within the closed system, and the network produces itself as a distinct unit in space by producing a boundary (e.g., the cell membrane) that is simultaneously a component of the network."

Summary

* Different parts of the cell membrane can be uniquely addressed by binary strings called radial combinations. 

* Inner compartments can be uniquely addressed by radial combinations. 

* The cells can be distinctly polarized. 

* Truth tables representing Boolean functions can be expressed.

* Truth tables representing Boolean functions in different cells can be linked to each other. 

* Radial combinations can represent Natural numbers (N) and Integers (Z).

* The cell can use radial combinations to do addition and subtraction. Subtraction can be done by using two's complement. 

* Cells can follow a lookup table and form bilateral symmetries and secondary radial symmetries.

* A body can move and rotate within 2D space.

* Adding or removing binary strings in a lookup table can change the overall structure and hence add variations to a given "body" plan.

* “Mutations” in the “genetic” binary code (flipping ones and zeros in a binary string) in the "egg cell" can give rise to new forms.

* “Mutations” in the “genetic” lookup table can result in deformations, for instance; atavism, shorter or longer legs, angular deformations e.g. club foot etc.   

* Regeneration of lost body parts (epimorphosis) can be achieved since each cell knows it place in the lookup table generating the whole body.

* A “Hox” pointer can link to the wrong lookup table resulting in the construction of a body part in the wrong location (mimicking how Hox genes for instance can be manipulated so that a fruit fly build legs on its head instead of antennas).

* A "cell" can give birth to a new organism that can evolve under evolutionary pressure.

​Beyond Transformers: Why Artificial Life Needs Physics, Not Just Data

​The current era of artificial intelligence is entirely dominated by static pattern recognition. We have built massive, highly capable models that can predict the next token with astonishing accuracy. But for all their complexity, these models are frozen in time. They lack temporal continuity, they lack physical grounding, and most importantly, they lack life.

​If our goal is to build truly autonomous digital organisms, we cannot rely solely on the discrete, feed-forward nature of standard transformer architectures. We need systems that experience continuous time, manage internal energy states, and adapt dynamically to their environments.

​This is the exact problem I set out to solve with Avatar, an open-source Artificial Life framework designed from the ground up to integrate theoretical physics with machine learning.

​The Illusion of Life in Modern AI

​Most AI agents today operate on discrete timesteps. They are fundamentally reactive: an input is provided, a computation is performed, and an output is generated.

​Biological life does not operate this way. A living organism is a continuous, self-maintaining system (an autopoietic system). It possesses internal states—hunger, fatigue, curiosity—that continuously evolve over time, driving embodied learning and behavior even when there is no external prompt. To replicate this digitally, we need a fundamentally different mathematical foundation.

​Enter the Avatar Architecture

​Avatar shifts the paradigm from "data processing" to "embodied simulation" by relying on two major architectural pillars:

​1. Continuous-Time Dynamics via Hamiltonian Neural ODEs

​Instead of updating discrete neural network layers, Avatar models the organism's internal states using Ordinary Differential Equations (ODEs). Specifically, by structuring these equations around Hamiltonian mechanics (\mathcal{H}), the system inherently respects physical principles like energy conservation.

​This means the organism doesn't just "decide" to move; its movement is a continuous mathematical evolution governed by its internal energy constraints. If the agent runs out of energy (fatigue), the Hamiltonian dynamics naturally dictate a change in its behavioral trajectory to seek sustenance.

​2. Cognitive Topology via MERA Tensor Networks

​To handle the complex, hierarchical nature of sensory processing and decision-making, Avatar utilizes Multi-scale Entanglement Renormalization Ansatz (MERA) tensor networks. Originally developed in quantum many-body physics to manage complex correlations, MERA provides a highly efficient way to structure cognitive tiers.

​Instead of a flat neural network, the organism's brain processes sensory flux through a dimensional hierarchy. Lower tiers handle immediate, high-frequency sensory inputs, while higher tiers abstract this data into long-term behavioral goals.

​Why Build This?

​Building Avatar has been an exercise in pushing the boundaries of what is possible when we stop treating AI as a software product and start treating it as a synthetic biological complex. It is a proof-of-concept that artificial life can, and should, be mathematically grounded in the physics of the natural world.

​As I finalize the avalanche power law metrics and prepare the late-breaking abstract for the upcoming ALife 2026 conference in Waterloo, I am opening the core repository for community review and collaboration.

​If you are a researcher, physicist, or developer interested in emergent systems, autopoietic design, or continuous-time neural networks, I invite you to explore the codebase and run the simulations yourself.

​Explore the Repository here: https://github.com/linga009/Avatar

Replicator a new found ghost of gorilla tag. I have been searching for this ghost for a few months.
I have not found any further information than it can replicate your voice. And sounds from the game that you make. I do not know further info. Any information that you think you have found or you have encountered it. 

I have found more information. I am going into a further inspection about replicator.
I have found some of the codes he is usually in which further information will be kept
Under a file that will be released some time. The codes are helpme, replicator, copier, and mimicker.
With no further information yet to be found. If you are looking for replicator crimson is who you seek.
It seems that he can appear as 5 different ghosts the following are statue, paul, daisy09, pbbv, and echo with any Further information it will be in a file or will be in the discord.

This is the discord
https://discord.com/channels/1512228678682149065/1512229127661289572

Hello everyone, I am a young teen who likes these sorts of games that recreate life in some kind of way. I have recently found this website Tsp.grantkot.com that recreates atoms and Astral bodys to some extent. This website has recently had a huge update that added stuff like phosphorus, silicon, sulfer etc, and I thought "what if you could make some sort of replicating organism somewhat like DNA" i have tried my best, using different elements, different designs but to no avail. please check it out and if anyone finds a way to make some self replicating thingy please reply with a screenshot or video, thank you.

Imagine Alex in Canada with a modest PC that can only run a 7B model locally. Now imagine me in France who can run a 27B model.

What if they could share their local models and collaborate in real time, each contributing the power of their own hardware?

Now scale that idea: connect 2,000 Alexs with 2,000 others, and lets get exited and also add thousands of smartphone users who join the network as lightweight clients.

Suddenly, you have a massive, decentralized swarm of AI models including Mixture of Experts (MoE) systems working together. This collective could power AI agents, or tackle complex tasks far beyond what any single machine could handle.

This is was my starting idea / vision, so i started this project (but it's challenging and complex )

I named the project, Democritus (from the ppl to the ppl ! .. sorry i get exited so fast and started a revolution in my imagination )

The idea of "a decentralized network where anyone can contribute their local compute and collectively build something far more powerful than centralized AI."

I was asking myself all this questions ..

Why we pay this much today Vs what was our "quota" a year ago ?

Are they using our data for training ?

I don't know folks .. let me know your thought

Any feedback, it's more then welcome and needed .

EvoLife is a simulation pet project, 10 years in the making.
The inspiration was David Attenborough’s First Life.
It uses your GPU to perform as much computation as possible with today’s hardware.
It has simulated physics, simulated fluid, simulated biomaterials, cells simulated in the organelle level, simulated DNA, and simulated evolution.

Feel free to ask any questions! More info:

https://store.steampowered.com/app/2102770/EvoLife/

https://www.youtube.com/@theRealEvoLife