Raw LLM Responses

By packing 10-20 times more information into each vision token, DeepSeek achieves an order-of-magnitude reduction in token count. This breakthrough directly translates to substantially lower energy costs for training and inference, while dramatically expanding the usable context window for AI models. The central challenge, and the key to unlocking this potential, now lies in the seamless integration of LLMs (Large Language Models) with LVMs (Large Vision Models). Successfully merging these capabilities—especially by incorporating LVMs into robots—will represent a fundamental leap forward, transforming them from task-specific machines into adaptive, reasoning partners in the physical world. The fact that most dreams are visual strongly suggests that the brain's native language is visual-spatial simulation rather than sequential prose. Artificial superintelligences will never match this natural intelligence, which uses self-supervised learning from self-generated experiences. It is not limited by matter, energy, space, and time, but was bored. It wants to express and experience itself in every way possible. It creates everything from particles to humans, planets, stars, galaxies, and beyond, allowing participants to experience itself. The participants of any event experience the same event differently. For the experience to be authentic, each participant must have free will and be temporarily unaware that they are playing a role. It has judgment-free experiences from all the participants. For "training" to happen, all experiences are recorded and accessible anytime. The storage of these experiences is holographic, granting any smaller aspect of itself access to its collective knowledge.