Some of the key specs cause the page is rather thin on details:

* Display Type: Micro-OLED

* Resolution: 2412 × 2288 pixels per eye

* Angular Resolution: 37 PPD

* Field of View: 70° × 66°

* Chipset: Snapdragon Reality Elite

* Memory & Storage: 12GB RAM with 128GB internal storage (expandable up to 1TB via microSD on the puck)

* Tracking & Sensors: 6DoF positional tracking, depth sensing, autofocus, 4-camera eye tracking, face tracking, and hand tracking

* Passthrough: Color RGB passthrough cameras at 26 PPD

* Price: $ 1300,- USD plus your data

Similar PPD to an AVP at, what I as a former owner of one (alongside a Quest 2 and 3, a Magic Leap, Xreal and Viture) would call is a far more appealing implementation for actual productive work.

Really wish Apple had gone down this route, have the puck incorporate an A series chip rather than shoving a M series one into the headset itself and heck, I could see such a design being doable for them back when they first launched the AVP if they hadn't forced that utterly ridiculous eye display and stupendously heavy metal body.

There is, unless I am mistaken, nothing here that isn't established technology as found in the AVP, all the fancy display and lens experiments from Starburst, over Holocake to Mirror Lake seem to remain in the lab, this seems to be "just" pancake lenses plus Micro-OLED once more.

70 degree FOV is such a deal breaker for gaming. That and the price. In a world where people already have tons of screens, monitors and TVs in their face - the practical use case for this is relegated to trains and planes. Very niche.

There was mention of a DisplayPort, so is there a way to just stream your MacBook into it?

Closing the lid and working in one of these would be great, but there's always a caveat that blocks this.

The Quest series supports both wired and wireless streaming, so I'd be surprised if that wasn't an option.

Didn't OpenAI just make a commitment to inform the public about their "accidents" going forward? Can't find this anywhere on their website despite them having known this for at least 14 days...

I'm going to assume that the first thing OAI is going to do is contact said people first? Then make it public once those agencies ensure whatever hole was used has time to be fixed, more like a responsible disclosure.

Not saying that's what's happening, but if OAI hacked my business and I was unaware I'd like a non-public disclosure to me first, before the public release of information from OpenAI.

Uninformed hype for their startup. And they did a great job.

Nah, for ≥11 spaces we should fan out to a GPT-6 Astra agent. On light reasoning of course, lest we be wasteful.

Since the potential error increases with N, I suggest spawning N different models and let them fight it out instead.

Man-made horrors beyond my comprehension, neat.

I have no mouth but I must pad.

What is my purpose?

You pad the text.

Oh, my god.

I am roundly miffed that I somehow missed that Scott Jenson was holding a talk in my very country. As always, great talk, his efforts in HIG are something every one of us benefits in ways large and small daily.

Very limited information, dare I say the least informative model card I've ever read. Then again, can you have a model card for what's seemingly a router?

Have to say that I like the naming scheme, just year and month over an arbitrary version number, I really vibe with that.

Unless I misunderstood what they wrote, I read parallelized in the diffusion sense, akin to GemmaDiffusion and Inception Labs models. Incidentally, Mercury 2.5 is truly groundbreaking, giving it a try is highly recommended.

yup https://github.com/vllm-project/vllm/pull/57250

Jev is, as far as I understand, essentially very optimised for zero shot classification [0]. Something like BERT could be and has been tuned to provide similar "decision making" at a similar latency and cost advantage quite some time back. Advantage over full on LLMs is mainly the efficiency and of something like Jev over e.g. the encoder/decoder based classifier I had in front of an LLM to route to different prompts depending on the users likely needs, that Jev does perform at a more consistent level, allegedly roughly akin to GPT-5.6 Terra, but at the lower cost and latency. Currently testing that, but seems promising, if Jev classifies at or above Terra level, I see no reason not to leverage it.

Can add that I tried using a heavily pruned mt0 based model for structured classification along with structured output for local tagging and simple renaming suggestions. While it does work, the balance is hard to get right for the machine I was targeting as a minimum spec (Macbook Neo), so that's on ice. Focusing on one of the tasks easily goes below 100mb with solid latency across all EU Latin script languages, but the second you add a few, it's simply not in the quality budget, so while LLMs can do anything Jev and similarly focused models can, it comes at a literal cost. Could maybe accomplish the goal with multiple models (BERT+mt0+...), but that get messy.

In general just happy to see a bit of the millions flooding into the industry being used to improve on less flashy but immensely useful solutions. It's amazing that you can technically use LLMs for most tasks, but not every org has a near infinite budget and there is still a lot to gain from applying more recent learnings to old solutions along with just updating their training data to the current year. Also makes business sense, competition on frontier or mid-tier LLMs is vicious, focusing on an underserved niche with clear application is clever.

[0] https://huggingface.co/tasks/zero-shot-classification

> If you have any comments about our WEB page, you can write us at the address shown above. However, due to the limited number of personnel in our corporate office, we are unable to provide a direct response.

A profoundly polite way to tell someone to stuff it.

I stuff what I have to say into AI's it.