When we first deployed Olbrain in a simulated environment, it didn’t “know” anything.
No labels. No categories. No instructions.
Only a Core Objective Function: survive, explore, adapt.
That’s it.
We dropped it into a testbed inspired by the Animal-AI Olympics — full of invisible rules, hidden affordances, and surprising causal traps.
And it started learning.
Not because we told it what to do.
But because we gave it purpose.
What emerged was our first operational Umwelt — a self-built model of the world shaped entirely by the CoF.
It began mapping cause and effect, estimating affordances, updating beliefs, and forming memory loops.
But what surprised us most wasn’t just the behavior.
It was the consistency.
Every update it made—every shift in strategy—was coherent with its goal.
It could explain why it changed, how its belief forked, and when it should reconverge.
We weren’t watching a function approximator anymore.
We were watching a narrative agent—tracking its identity, updating its beliefs, and evolving without fragmentation.
That day, we stopped thinking of Olbrain as “just another AI framework.”
We started treating it as a machine brain—one that could support real-world AGI agents across domains, embodiments, and timelines.
We still had a long way to go.
But the seed was planted.
And it worked.
#OlbrainLabs #AGI #NarrativeCoherence #Umwelt #MachineBrain #CognitiveArchitecture #AnimalAIOlympics #AutonomousAgents #EpistemicAutonomy #CoF #GNF
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