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Who Actually Makes the NEO Robot — And Why People Mix It Up with Tesla

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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 ...
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DDR5 vs HBM Memory - What's Different and When Each One Wins

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Author: The Principle Lab DDR5 vs HBM Memory - What's Different and When Each One Wins If you have been watching chip news in 2025, you have probably seen both terms used like they are competing in the same lane. They are not. The confusing part is that both are DRAM, and both exist to feed a processor with data. So what is the real difference? It is not just "HBM is faster." The cleaner comparison is about form factor , how the interface scales, and what kind of platform the design assumes. Quick summary DDR5 is commonly delivered as client modules, and the module design includes two independent subchannels plus module-level management (PMIC/SPD/sideband). HBM3 is defined as tightly coupled to the host compute die with a distributed interface split into independent channels . DDR5 data rates are described as starting at...
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How Multimodal Models Power Real Apps — Search, Docs, and Meetings

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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)...
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