I used it for double checking some stuff while helping my friend build his PC! I had it in tight spaces and it helped me verify some details around which nvme slot to use first.
Gemini live has been able to do this for over a year now. I can just activate it on my phone and it really works surprisingly well, especially the interruption. I've tested it with my 95 year old Dutch grandmother and it switched seamlessly between English and Dutch with her and handled her poor hearing very well, including her asking for repetition.
I'm a little surprised by how much OAI is playing catch up here.
Having used it quite a bit when it was out, it's not. It's certainly better, but in some ways it's worse. It's trained to be more "agentic" and even in cases where I wanted to talk things through first and I would explicitly tell it not to do something, it would take action on my behalf without checking first.
It's also still just prone to the kind of "stupid" mistakes we see from all LLM's. Like it can write great code, but it doesn't really have common sense without enormous guidance.
A crucial factor tech industry folks tend to ignore is how much executives value predictable costs. Cloud migrations got away with this, but still had to argue fiercely, because 'the cloud' and its serverless tech had the potential to significantly decrease overall spend for unpredictable, bursty workloads.
The usual counter-argument is the operational burden, but human capital is also a relatively fixed cost. A dedicated team of 3-5 FTEs could probably handle inference ops for a F500 company.
Meanwhile, the capability delta is shrinking fast. We have more evidence that local open-source is viable with the release of DeepSeek v4, and the industry is only trending further in this direction. Especially as we rely more on test-time compute and task-specific harnesses rather than model size.
So, if you're an executive looking at a marginal but fixed operations cost, added flexibility, and a rapidly closing gap in capability, why wouldn't you just run open-source models on your own infrastructure to get those highly predictable costs? Plus, you decrease the risk of one of the frontier
It is frustrating, because I really enjoyed my Valve Index and want a replacement and Meta has some of the best VR tech in the world, but I've waited 6 years for Valve to release their new headset to buy a replacement, simply because Meta can't be trusted.
The quality of apps in the Google Play Store has dropped massively. There are still some gems, but for better or worse, the ecosystem is simply not as strong as Apples and it's certainly not comparable to just having a device where you can install anything you'd like in a full desktop grade OS.
There was a time where Google could've been competitive in this space, specifically against Apples MacBook product line, but that has long since passed. The 3rd party manufacturer path means Google isn't committed to this and won't have competitive hardware. It'll just be another Chromebook and limited to the Google Play Store too, which just isn't good at this point.
Interesting side effect of this is that Google Cloud may now be the only hype scaler that can resell all 3 of the labs models? Maybe I'm misinterpreting this, but that would be a notable development, and I don't see why Google would allow Gemini to be resold through any of the other cloud providers.
Might really increase the utility of those GCP credits.
Benchmarks are favorable enough they're comparing to non-OpenAI models again. Interesting that tokens/second is similar to 5.4. Maybe there's some genuine innovation beyond bigger model better this time?
Whenever we get the locally runnable 4k models things are going to get really awkward for the big 3 labs. Well at least Google will still have their ad revenue I guess.
In my experience Azure is full of consistency issues and race conditions. It's enough of an issue that I was talking about new OpenAI models becoming available via Bedrock on AWS and how convenient that was since I wouldn't have to deal with Azure and my colleague in enterprise architecture went on an unprompted rant about these exact issues. It's not the first time something like this has happened and I've experienced these issues first hand, so yes. I'd say reliability is a critical issue for Azure and it hasn't gotten better each time I've gone back to check.
I'm finishing my annual paid Pro Gemini plan, so I'm on the free plan for Claude and I asked one (1) single question, which admittedly was about a research plan, using the Sonnet 4.6 Extended thinking model and instantly hit my limit until 2 PM (it was around 8 or 9 AM).
Just a shockingly constrained service tier right now.
See I really want this in a simpler format. Like a single file embedded database on my filesystem that I can point a single/or few tools at for my model to use when it needs.
If you don't "have the full context on the Persona/Discord story" you should work on getting it.
It's literally the first thing that came to mind when I saw your post and not having a convincing/satisfying answer in direct relation to that catastrophe doesn't bode well for getting people to trust your brand. The rest of your answer is essentially the absolute minimum I'd expect from a business like this, but not sufficiently convincing.
Regardless of your opinion of Yann or his views on auto regressive models being "sufficient" for what most would describe as AGI or ASI, this is probably a good thing for Europe. We need more well capitalized labs that aren't US or China centric and while I do like Mistral, they just haven't been keeping up on the frontier of model performance and seem like they've sort of pivoted into being integration specialists and consultants for EU corporations. That's fine and they've got to make money, but fully ceding the research front is not a good way to keep the EU competitive.