Cooper8

Hmmm… let’s find the best ways to both make money from this program and yet somehow the targeted individuals manage not to get detained.

It is actually an old and well established britishism

https://youtu.be/dU3ru09HTng

Welp, looky there, an expansion port right on the bridge of the headset with PCIE compatibility.

I don’t see it in the hardware design, but from a software perspective the groundwork is there for modularity. Offloading the core compute to the PC frees up onboard processing to run peripherals like full color front cameras (onboard are black and white / IR) and more advance proximity detection, hell hook up lidar and go nuts with full body tracking.

That said, all of that would depend on decent I/O. 2x USB4 ports would go a long way.

It days right in the marketing text that the headset is “a PC” which to me implies full SteamOS distro with no limitations on installing a different OS, if you can get the many hardware drivers to work.

I remain convinced they have held back budget on AI because they are waiting for the bubble to burst so they can buy one of the bigger developers like Anthropic. Why burn a bunch of cash now just to loose the race when at the end of the day Open Source options might come out competitive or one of the leaders in the space can be bought out once valuations hit a reality check?

Houdini M2 over LORA is kind of a replacement.

YouTube video on it

It’s too bad it leaves the door open for age verification requirements, but the language is overall pretty decent.

Makes sense to me. I have always thought that if the goal is to emulate human-level intelligence then developers should consider the human brain, which not only has multiple centers of cognition dedicated to different functional operations, but also is massively parallel with mirroring as a fundamental part of the cognitive process. Essentially LLMs are just like the language centers being forced to do the work of the entire brain.

More functional systems will develop a top level information and query routing system with many specialized sub-models, including ongoing learning and integration functions. The mirroring piece is key there, because it allows the cognitive system to keep a “stable” copy of a sub-model in place while the redundant model is modified and tested by the learning and integration models, then cross checking functions between the new and old version to set which one gains “stable version” status for the next round of integration.

Anyway, thanks for sharing.