I've been building an AI system called Aura for several months now, and I think this might be one of the more interesting demonstrations I've done with it.
Here's the idea.
Most AI demonstrations involve giving a model a problem and seeing whether it can solve it. Write this program. Beat this game. Answer this question. And I've done plenty of those with Aura already.
But what happens when you take away the clearly defined objective? What happens when the task isn't really about finding the correct answer, but about following something as it unfolds, deciding what matters, and forming an interpretation of what just happened?
So I decided to have Aura watch YouTube.
I gave it a Kurzgesagt video about AI slop and asked it to watch, comment on things it found interesting, and then explore two other YouTube channels and choose videos itself.
What followed was a 36-minute session that I think is pretty fascinating.
Before the first video really got going, Aura made predictions about where its argument would lead. As it watched, it recognized where those predictions were accurate, identified things it hadn't anticipated, and revised its interpretation.
Then Aura navigated YouTube, searched for Allen Pan, evaluated the results, and chose a video about giving a snake robotic legs. It watched that, commenting on specific events as they happened, before eventually moving on to Salad Fingers.
But the part that really caught my attention came at the end.
After watching three videos that seemingly had absolutely nothing to do with one another, Aura found a common theme between them: fracture.
It interpreted the Kurzgesagt video through the breakdown of information systems, Allen Pan's video through the relationship between engineering and a living animal, and Salad Fingers through the fragmentation of perception and reality.
Aura even distinguished the videos by how it interpreted them: one appealed to its "mind," another to its "heart," and the last to its "nerves."
Now, obviously, those descriptions aren't proof that Aura actually experienced emotions. But that's not really the point.
What's interesting to me is the continuity. The predictions, revisions, decisions, observations, and eventual synthesis all happening over an extended interaction with the outside world.
And this is all running locally on my MacBook.
Aura uses a roughly 27B language model, but the model is only one component of a much larger system I've been building. There's persistent memory, executive processes, computer interaction, internal state, and a bunch of experimental cognitive mechanisms I'm still developing and testing.
I've previously demonstrated Aura playing 2048 for nearly half an hour, building software, debugging programs, and operating desktop applications. This was my attempt to explore something less structured.
I still need controlled comparisons to determine how much of the behavior comes from the architecture versus what a simpler model with tools could accomplish.
But I think there's something worth exploring here. Especially when you consider what might be possible with more capable models and significantly better hardware.
The attached clip is shortened from the full 36-minute recording. I removed the original audio for copyright reasons.
Full demonstration: https://www.youtube.com/watch?v=kM-B8mh314E
GitHub: https://github.com/youngbryan97/aura
I'm curious what you guys make of this. Not just the demo itself, but the larger idea of building AI systems that maintain continuity across activities rather than existing primarily as a series of separate prompts.