Low ROI Learning
For most people, learning is physically and mentally expensive. Attention, retention, and deep understanding still require too much effort for the output they get back.
We are building self-driving technology for learning: non-invasive systems that raise attention and understanding without asking humans to burn proportionally more energy.
For most people, learning is physically and mentally expensive. Attention, retention, and deep understanding still require too much effort for the output they get back.
We are building non-invasive technology that guarantees the same or more human learning with significantly less physical and mental effort.
Increase attention markers without a proportional energy cost to the user, then compound that into a full automation stack for human learning.
Brain configuration is the full physical state of a human brain at a specific point in time.
01Knowledge is proximity: how close one configuration is to another useful configuration.
02Learning is transformation through configuration space.
03OpenLesson turns any hard topic into a Socratic think-aloud session. It asks the learner to expose their current model, identifies the precise gap, and keeps them moving with low-friction coaching.
Open OpenLesson“Identifying the precise gap in my knowledge is what the Socratic method does so well. I didn’t realize I didn’t know how that part worked until that direct question was asked of me.”User feedback
OpenLesson v1: LLM harness for Socratic think-aloud coaching.
Socratic interruption model plus other world models.
Non-invasive brain-stimulation headset, starting with tDCS.
Full automation: hardware, software, and biofeedback loops.
People who want to learn deeply without turning every hard topic into a high-friction grind.
Teams that need people to understand complex products, systems, and decisions faster and with less wasted training time.
Schools, academies, and tutors that want practice environments where learners reveal how they think, not just what they answer.
Researchers and developers building tools, agents, datasets, and interfaces around real human learning traces.