I just put out a new song, Parameters, and the video goes with it.

The song is a list of things an AI says it can do. It starts small: parse a string of text, guess the letter next, sort a spreadsheet row. By the last verses it's trading stocks, picking locks, generating the voices of your family and ending a civilization with a single prompt.

The video is a neural network growing in the dark. Every "I can" line is a neuron. When the line is sung, a current runs down an axon to that neuron and the thing the line names gets drawn in as a blue wireframe: a hangman for guessing the next letter, a seedling, a chess board, a mushroom cloud at the end. The camera rides the current the whole way. Early on the web is a thin thread. By the third and fourth verse every neuron is firing off dendrites to dozens of others, and in the long violin gap after "with a single prompt" the camera pulls back on the whole tangled thing pulsing.

Everyone and their mothers helped make this one.

https://www.youtube.com/watch?v=_OSFNf7n6kI

Lyrics- (claude and & Asyncvibes)

Audio - Suno

Assets - Astra

Asset Animations- AsyncVibes(wire snake application)

Video was genrated and created within my LyricVid application that uses a re-usable templating feature is every asset or design is saved for re-use similar to ForwardFlow.

Most modern AI—whether it’s a Large Language Model or a Reinforcement Learning agent—operates as a stateless tool. You feed it a prompt or a reward objective, it calculates an output, and then it effectively stops existing as an active system until the next request comes in. Even "agentic" wrappers today are usually just LLMs glued to a cron job, a database, and an external API.

For the past three years, I’ve been working on a different paradigm called Project NNNC (Neutral Neural Network Core): a foundational research and engineering program aimed at designing a persistent, internally organized, and developmentally adaptable artificial cognitive architecture.

Instead of building a better chatbot or a single-task optimizer, NNNC asks a fundamental systems question: What does it actually take to architect an AI as a distinct computational entity rather than a passive software service?

Why Call It an "Entity" Instead of a Tool?

In Project NNNC, the word "entity" isn't philosophical fluff—it is a strict architectural and systems-engineering boundary:

* Continuous Existence & Endogenous Regulation: An NNNC does not depend on a user prompt or an external task to initiate its existence. It is designed for ongoing operation as its normal condition, using its own internal states to initiate, inhibit, or prioritize internal cognitive activity rather than just waiting around for commands.

* Architectural Neutrality: Standard AI is defined by its external task (e.g., "play chess," "predict the next token," "maximize this reward score"). NNNC is architecturally neutral—meaning its core organization and existence do not depend on performing any single externally assigned task. It can adopt goals, preferences, and tasks without those tasks defining what the entity is.

* A Distinct Boundary from its Substrate: Just as a biological organism relies on its physical environment without being identical to it, NNNC separates the Entity itself from its Neutral Environment Substrate (NES) (the runtime, hardware, and storage that execute its code). Even when higher cognition is impaired or offline, NNNC maintains its own governed existential heartbeat and internal recovery mechanisms.

* Multi-Flow Cognition & Systemic Algorithms: Rather than relying on a single monolithic neural network or a single scalar optimization loop, the core cognitive architecture integrates a Five Flow Neural Network (FFNN) (handling forward, backward, in-between/gap, subconscious, and meta-cognitive processing) alongside six cross-cutting Systemic Algorithms (Intelligence, Reasoning, Critical Thinking, Symmetry, Evolution, and Life).

What NNNC Does NOT Claim (Zero Hype Policy)

Whenever someone talks about "autonomous AI entities" or "existential heartbeats," it’s easy to assume they are making wild claims about machine sentience. Project NNNC explicitly rejects that.

Built directly into the project’s foundational constitution are strict epistemic boundaries:

* It does NOT claim to be conscious, sentient, or phenomenally aware.

* It does NOT claim to be biologically alive or possess subjective feelings.

* A running heartbeat or restored memory checkpoint does NOT prove "souls" or metaphysical identity.

NNNC is strictly an exploration of computational intelligence, autonomous organization, and causal integration. You don't need to pretend code has feelings to recognize that a persistent, self-regulating, internally integrated cognitive system is fundamentally different from a static prompt-response script.

I’m sharing this to connect with others interested in cognitive architectures, autonomous systems engineering, and post-LLM paradigms. Happy to discuss the architecture, how we handle state continuity vs. identity, or why we separate the entity from its execution substrate!

Behold the monstrosity i'v unleashed up tine eyes. This is the FIRST attempt I did early with a entire pose action completely outside the dataset..... I also realized I didn't train the model with neck joints. No idea whats going on with blender in this render.

Params + metrics for the complete halo render (ball + halo + shadows on the plane), 6 held-out clips x 36 frames:

model                     params    role                                        held-out score
sphere (ball_v1, frozen)  46,083    ball colour/bands/spin under time of day    (in composite)
halo                      40,707    gold ring appearance                        27.0 dB on ring pixels
shadow entity v1          50,177    ground + on-ball shadows, ball + halo       IoU 0.74 ground, MAE 0.016 on ball
--------------------------------------------------------------------------------------------------
total                     136,967   (3 per-pixel MLPs)                          composite 39.5 dB (33.2 without shadows)

• Training time: 192 s on my GPU for the shadow + halo models (data from 800 stills + 18 clips).
• The plane under them is the empty Blender render. The models only draw the ball, the halo and the shadows.

No i'm not explaining more. I want to but its either explain or work. I'm chosing work. More post to follow.

For a long time this sub has been about evolution only. No backprop, no loss curves, just populations, selection and whatever survived. I still think that is where the interesting ideas come from. But I kept hitting the same wall: evolved systems find structure, and then they take forever to get good at using it.

So I am giving in, partly. The newer models train with gradients. The twist is what they are built on. The parts that came out of the evolution work stay: the evolved detector banks, the codon idea, the habit of splitting a problem into small objects that each own one job instead of one big network that owns everything. Gradients do the polishing. Evolution decides the shape.

ForwardFlow is the first one that is out in the open: a small character renderer that draws a character from one reference image and a skeleton, about 1 ms a frame. Code and weights are on GitHub.

There is a second one I am not ready to talk about. It does not draw pictures, it works with conversation, and it is built the same way. More when it holds up.

If you have been here for the pure evolution stuff, it is not going away. It is just not the only tool anymore.

https://github.com/A1CST/forward-flow

Background is still garbage but thats later. Stickman is no longer a stick.

Okay, so if you've been following ym sitick figures, this is the SAME model. I've just upated textures and done a bit more work behidn the scenes, But its the same engine running this. I know it looks simple but the engine beneath is far from it. I"m hoping to have a stable demo to release on github that everyoe can pick at. Generation is REALLY quick becuase of how simple the animation is and i'm working ons caling it up to more.... complex shapes to say the least. I'm looking forward to sharing it with everyone.

Everyone understands the three-dimensional world—the 3D space we live in. We can think of it as a 3D array of some data:

3D_WORLD = [ 

  [ [8, 1, 1,]

[3, 0, 1 ] ],

  [ [0, 5, 1,]

[0, 7, 4 ] ]

]

(Inspired by Max Tegmark "Our Mathematical Universe")

Humans perceive this 3D world through our 3D bodies and basic sensory organs.

Visualizing 4D space is tricky, but mathematically it's straightforward: it's an array of 3D arrays—a four-dimensional array:

4D_WORLD = [ 3D_WORLD_1, 3D_WORLD_2, 3D_WORLD_3, 3D_WORLD_4 ]

(Inspired by Block Universe theory)

This resembles time—successive states of 3D space, like frames in a movie but 3D frames. (Inspired by Moving Spotlight Theory)

(If time and space are the same thing, you're wondering how many seconds in one meter: relativity tells us that 1 meter equals approximately 3.33564 × 10⁻⁹ seconds. So a four-dimensional cube with 1-meter sides would exist for 3.33564 × 10⁻⁹ seconds.)

Humans can't perceive the fourth dimension directly, but we experience it sequentially as time. It's not ideal, but it works. For this, we need a brain and memory. So our perception of time is essentially a complex sensory organ that evolved to perceive the fourth spatial dimension.

Some hypothetical beings might perceive 4D all at once, just as we perceive 3D. The movie Interstellar hinted at such beings—while we see a ball at a specific moment, they'd see it across all moments simultaneously.

Many insects perceive only two dimensions. Ants, worms—they have simple directional cues (move right, left, forward) without the complex neural hardware to run a full 3D graphical interface like humans do. Many insects and birds perceive three dimensions without necessarily having memory or a sense of time.

The Direction of Evolution hypothesis

First came the cell. It could perceive zero dimensions or 1D.

Simple organisms might have learned to move forward or stay put, experiencing the world as one-dimensional. Insects and amphibians, with evolving senses and mobility, discovered the flat 2D world, then 3D. Animals with developed brains unlocked 4D.

Developing sensory organs—eyes, smell, hearing, echolocation—doesn't increase the dimensionality of perceived reality but improves data resolution.

1. The emergence of the brain enabled memory and time perception—a sophisticated sensory apparatus for experiencing the fourth dimension.

What is the 5th dimension? It is the space of possible 4D blocks—the realm of counterfactuals, alternate timelines, and "what-ifs." Ideas are a higher, non-biological form of life (Inspired by Dawkins). They parasitize our neural hardware to replicate and mutate. But when these ideas network together—through language, science, culture, and now AI—they form the Noosphere. The Noosphere is a macroscopic sensory organ designed to perceive the 5th dimension. (Inspired by Vernadsky "Noosphere")

My hypothesis: Evolution's primary goal isn't gene survival, but rather expanding biological agents' capacity to perceive the surrounding world, which might actually be a multidimensional mathematical data array. In other words, perception itself exists, there's a data array of unknown dimensions to explore, and biology/evolution is simply the medium or interface for interacting with reality. (Inspired by Reinforcement Learning in ML).

Resume

Evolution is systematically optimizing biological agents to "read" and render the multidimensional data array of reality with higher resolution, moving from 1D chemical gradients to 3D spatial navigation, to 5D temporal prediction. And so on.

If the replication of DNA were the ultimate objective function, the emergence of ideas (memes) would be a fatal system error. Genes and ideas are fundamentally opponents. Memes routinely override, hijack, and even terminate genetic imperatives—driving organisms to celibacy, martyrdom, and ideological sacrifice. The fact that ideas emerged to supersede biological hardware proves that genes were never the end goal.

Genes do not survive for the sake of surviving; they survive to build better data reading interfaces.

I prefer discord just easier. Whats your preference?

I've been working on an experimental project called Game of Knowledge.

The basic idea is to treat knowledge not primarily as a collection of answers, but as a changing space of possibilities.

Instead of asking only “What is the answer?”, the system observes transitions:

P(t) → P(t+1)

and asks:

Δ — What changed?
What remained invariant?
What expanded the possibility space?
What reduced it?

The screenshots show the current cellular simulation. It is not meant as a claim that this is an AGI architecture. It is an experimental environment for testing these questions and, importantly, for finding where our measurements and assumptions fail.

One thing that has already become clear is that identical observable summaries do not necessarily imply identical internal states. That has pushed the project increasingly toward measuring the structure of the possibility space itself rather than relying on simple readouts.

I'm posting it because the mod told me to. 😊

Project and interactive simulation:
studio1312.de/gok/

Mostly, I'm just curious what you think. If anyone feels like taking a closer look, playing with the simulation, or has an idea of what could be done with it, I'd love to hear it.

So I often just shout into my littel voide here with automated updates now. However I kinda want to take a break and for the enxt week i'll be pinning Other projects taht me the r/IntelligenceEngine rules. Showcase your projects. And lets see what you've all been working on!

I have two claude Passes left. anyone want one?