The Philosophy: Learning by Doing

Humans learn most effectively through demonstration. Whether it’s tying a shoe, using a new app, or assembling a product, we mimic behavior by watching and doing. Omniminds applies this same paradigm to machine learning, allowing agents to learn by observing real users complete tasks on a screen. This creates a much richer training signal than traditional supervised learning or static datasets.

Instead of relying on labeled data or hard-coded logic, agents begin with context-rich demonstrations that reflect real user flows, decision points, and interface complexity.