Author: The Principle Lab Who Actually Makes the NEO Robot — And Why People Mix It Up with Tesla Short version The NEO robot is made and sold by 1X Technologies. That is what 1X itself says in the official announcement on October 28, 2025. It is a home-focused humanoid — not an automotive add-on and not a Tesla experiment. It is not a Tesla product. Tesla’s own AI & robotics page in 2025 still calls its humanoid “Optimus.” If the robot in your thumbnail is called “NEO,” you should attribute it to 1X, not to Tesla. Why people get confused In late 2025, two things happened at once: 1X showed a good-looking, household-scale humanoid called NEO, and Tesla kept posting progress videos of Optimus. Both are tall, both are bipedal, both talk about “doing chores or repetitive tasks.” When someone then drops a Tesla logo and a real NEO photo into the same frame — exactly what you ...
Author: The Principle Lab How Multimodal Models Power Real Apps — Search, Docs, and Meetings If you’ve wondered howmultimodal” turns into something you can actually use at work, this is the practical map. We’ll walk a scenario from the first user action to the last model call, and point out where tool calls, enterprise search, and meeting transcripts plug in. The focus is on how things are wired under the hood , not hype. Scenario: one workday, three touchpoints Picture a knowledge worker’s morning flow. They open the company portal and search for a policy (search app). Later, they ask an assistant to summarize a PDF and extract a few fields (docs app). In the afternoon, they need decisions and action items from a project call (meetings app). A modern assistant can span all three because it accepts text plus other inputs and can call tools for retrieval. That’s the entire idea behind a multimodal + tool-using pipeline . Timeline — what actually fires (0 → 1 → 2)...
Author: The Principle Lab The Multimodal AI Overview — From Concepts to Real-World Apps If you follow AI news, you keep hearing that multimodal AI is where everything is heading. Models that read text, look at images, listen to audio, and still answer you in natural language can feel almost magical. But when you are actually building products, you need something more concrete than hype: a mental map of what multimodal really means, where it helps, and why it still fails. This overview ties together four deep-dive articles in The Principle Lab series. Think of it as a guided tour: we start with the basic idea of modalities, move through the comparison with text-only LLMs, step into real applications like search and meetings, and finish with the uncomfortable but necessary topic of noise, alignment, and bias in the real world. By the end, you should have a clear sense of how the pieces f...