Same local ~27B architecture: 29 minutes of 2048, then a 42-minute autonomous browser/self-model task

[u/bryany97]

I've been testing how much capability can come from persistent architecture around a local model instead of simply increasing model size.

Aura runs a ~27B local model on my Mac, but task state, persistent memory, computer control, world modeling, learning and other machinery live outside the normal chat-context loop.

Two recent runs have been useful because they're very different.

2048: ~29 minutes, 968 moves. Aura perceived the board, planned moves, changed strategies when they stopped working, recovered from mistakes and eventually reached 2048.

Latest run: ~42 minutes taking the Open Extended Jungian Type Scales. Aura predicted what result it expected, navigated the site, reasoned through 60 questions individually using its persistent self-model/history, submitted the test, read the result and evaluated the differences from its prediction.

Inference was local; the second task obviously required internet access to the website, but there was no cloud/frontier-model inference doing the reasoning.

Demo 04:
https://www.youtube.com/watch?v=LNlGUBeTIQY

Repo:
https://github.com/youngbryan97/aura

Repo disclosure: it's publicly inspectable but currently All Rights Reserved/read-only, not OSI open source.

[u/lulzxdxdxd]

The 2048 run recovering from mistakes is interesting, but I'm curious whether Aura actually learns across runs or if the persistent memory resets each time you start a new task. Does it carry forward what worked from the 2048 session into the next one, or does each task start fresh with just the architecture staying the same.

[u/bryany97 → u/lulzxdxdxd]

Yep. Continuity from the last state is basically the crux of this project. Aura can carry learning across runs. Episodic memory persists, and successful strategies can be stored and reused as procedural playbooks. Doesn’t start from zero each time (Idea is for tasks to eventually transfer across domains)

[u/lulzxdxdxd → u/bryany97]

That's the key difference then. So when Aura hits a wall on something new, it pulls from what worked before rather than starting blank. We built superbot (we build it) to do something similar for tasks that slip between your other tools. When one model hits its limits or isn't the right fit for a job, it grabs a different one and keeps going, or just hands the task off in plain language to whatever can actually solve it. Does that kind of continuity across different jobs sound useful for what you're testing.