Most people judge robots by their appearance of intelligence. That misses the point. What's crucial is how intelligence performs in real-world environments, where conditions vary and failures have consequences. This is where @openmind_agi excels. Traditional robotics rely on a single intelligence loop. When it fails, everything stops. @openmind_agi, however, designs robotics as a networked system: - Shared data across robots and manufacturers - Built-in identity for coordination and trust - Multi-agent decision-making, not isolated models This approach makes intelligence resilient by default. Robots learn from one another, and the system adapts when individual models fall short. Real-world deployments, like live fall detection, validate this method. There's no reset button; systems either perform or fail. @openmind_agi also avoids hard-coded behavior. Modular “packs” allow robots to transition between environments without rebuilding the stack. Their adoption strategy mirrors the tech: start with universities, provide builders with real robots and a live system from day one, and let capability grow. As robotics enters its next phase, it won’t be about appearance. It will be about intelligence that scales, adapts, and works collectively. gMind guys!