Any discussion related to this topic always seems to assume everyone uses code the same way and for the same function, and then forces the rest of the world through that lens.
So here we walk around the circle one more time again, voicing our anxieties, talking past each other, waiting for the next opportunity for commentary to come in half an hour.
> At the time of the publication, Meta admitted subcontracted workers might sometimes review content filmed on its smart glasses when people shared it with Meta AI.
They just got fired for "piercing the veil". They committed the sin of bringing attention to the invasion of privacy.
If you don’t disable the glasses they could continue to share content. The article describes the glasses being left on a dresser and then sharing content of people without their consent, which could easily parallel into showing a sexual encounter or other privacy-sensitive scenarios.
Sure, and the same is true with my iPhone or my Olympus. Except the former encrypts the video and the latter isn't internet-connected.
The problem here (other than Meta being Meta) is people assuming Meta isn't permanently operating in bad faith. I'm just surprised anybody into tech to the extent they'd buy first-gen VR glasses would be surprised at Meta doing Meta things. That's all, I guess.
I would love to be able to run frontier locally, but I think the larger importance of open weight models is price accountability.
In the US with our broken system of capitalism, it’s the only way we can tether these companies to reality. Left to their own devices, I’m not convinced they would actually compete with each other on price.
Buy nobody like to talk about how “moat” building is fundamentally anti-competitive, even in name.
Funny that self proclaimed capitalists hate the system in practice. Commodity pricing is what truly terrifies them.
I'm not necessarily interested in having frontier locally. You don't need to be frontier to be a very good and useful coding agent. I agree with your point on price accountability though. Hopefully no tariff comes down on the Chinese and European open-weight models.
This is my own take, directly related to this that I posted a little while back. The one thing that I think the article missed is the geopolitical angle they’re also working:
* We need to completely deregulate these US companies so China doesn't win and take us over
* We need to heavily regulate anybody who is not following the rules that make us the de-facto winner
* This is so powerful it will take all the jobs (and therefore if you lead a company that isn't using AI, you will soon be obsolete)
* If you don't use AI, you will not be able to function in a future job
* We need to lineup an excuse to call our friends in government and turn off the open source spigot when the time is right
They have chosen fear as a motivator, and it is clearly working very well. It's easier to use fear now, while it's new and then flip the narrative once people are more familiar with it than to go the other direction. Companies are not just telling a story to hype their product, but why they alone are the ones that should be entrusted to build it.
"The race to build smarter-than-human AI is a race with no winners."
And specifically about the point on China, several people in power in China have also expressed the need to regulate AI and put international structures of governance in place to make sure it will benefit mankind:
The outcome of this is, in my opinion, the United States Government classifying and regulating LLMs as something akin to how the ATF classifies weapons, ie. requiring a license to operate an LLM (hosting), with different classifications and determinations on the relative "power" of a particular model and framework, and outright banning most open-source models, like how DIY machine guns or suppressors are banned.
Think of a standard for classifying and regulating the self-hosting of open-source models similar to how an FFL works. You can do it, but you must have all your paperwork lined up, with background checks, a valid business license, and if you forget to dot an "i" or cross a "t" the Cyber version of the ATF shows up and shoots your fucking dog.
> We need to heavily regulate anybody who is not following the rules that make us the de-facto winner
How about building a multipolar world where different parts of the world (US/China/India/EU/Africa,..) get to build sovereign tech and have their own winners?
> In a provocative GitHub post, machine-learning engineer Han-Chung Lee argued that even rosy internal numbers that do show AI-assisted productivity gains are suspect, as they’re produced to hit adoption targets no one can effectively audit.
Isn't this fundamentally what MBAs do with their time? Keep going with this analysis, because it goes much deeper... In my experience, BI is often a house of cards. A lot of times it's just narrative crafting, just like we're all encouraged to do when we write our resumes.
Can you embellish a story? Can you invent a convincing political narrative? As far as I can tell, that's the fundamental unit of US corporation.
> Traditional call noise canceling relies on those small onboard neural networks and can have difficulty isolating your voice in very noisy environments, which results in ambient noise leaking through or voices getting highly compressed, making it difficult to hear. Anker says the larger neural network available on the Thus chip, plus eight MEMS (micro-electromechanical systems) microphones and two bone conduction sensors to focus in on your voice, in its yet-to-be-announced earbuds will have significantly cleaner call audio, regardless of the environment.
Anyone who likes good noise cancellation, which is a lot of people.
Back in the day we just called it ML. But now you have to stop for a minute to read and determine what they’re talking about, because “AI” is primarily a marketing term.
I tried it on openrouter and set max tokens to 8192, and every response is truncated, even in non-thinking mode. Maybe there's an issue with the deployment, but in your link also shows it generates tons of output tokens.
My tinfoil hat theory, which may not be that crazy, is that providers are sandbagging their models in the days leading up to a new release, so that the next model "feels" like a bigger improvement than it is.
An important aspect of AI is that it needs to be seen as moving forward all the time. Plateaus are the death of the hype cycle, and would tether people's expectations closer to reality.
My purely unfounded, gut reaction to Opus 4.7 being released today was "Oh, that explains the recent 4.6 performance - they were spinning up inference on 4.7."
Of course, I have no information on how they manage the deployment of their models across their infra.
I was there too, but honestly after today, 4.7 "feels" just as a bad. I was cynical, but also, kind of eager for the improvement. It's just not there. Compared to early Feb, I have to babysit EVERYTHING.
So here we walk around the circle one more time again, voicing our anxieties, talking past each other, waiting for the next opportunity for commentary to come in half an hour.
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