The gears etc on bikes are customarily on the right, so almost all photos of bikes (eg for sale, by hobbyists) face to the right to show off that, and so it makes sense for LLMs to have such a preference.
You probably can’t get under 500 km orbit, so 2000 km round trip is the shortest you can do between you and a ground based server. That’s 150 ms minimum round trip latency.
I don’t know, I feel like there’s plenty of money to fund a lot of risky bets in AI right now, across the research-commercialization spectrum. Not sure there is a need for a new form of funding the field?
Everyone appears surprised by this news. It’s clear that they don’t have a product with some incredible moat. But they clearly have good engineering and product people that came up with a product people wanted. On top of that they have very strong marketing muscle that took the AI world by storm. And as far as I’ve seen, they still lead in some part of the latency-quality (-cost) curve?
They may well be a good team to throw money behind if you are hoping to bet on a new AI lab.
i mean marketing in some sense “feels” like fraud when presented in some very specific way, no? at some level it’s: generate interest about a thing when there didn’t exist any interest before. sometimes they generate interest about something that has no substance and that feels kind of fraudulent (this isn’t that case)
but in this case, this is product extremely boosted for super viral marketing, and this is the first time i’ve read about this boost since hearing of the product whereas everybody only really talks of its substance. feels like a magician kept a trick going for a very long time, people forgot they were watching a magic show, then the curtains come down and they reveal the deceit after (i’m being slightly melodramatic)
> people that came up with a product people wanted
We've yet to see whether this is true, or is it just manufactured demand. There are dozens of Jev demos, but pretty much all of them are either cool but useless, or simply fake (i.e. harness doing 99% of the work).
But economics of Jev enable classifying high volume things. And I've tried all the open-weight Qwen based decision models on my Spark and none of them came closer to quality and suck at batch inference so they are doing something custom.
Silly question but is it right? I am in grad school and this semester we run multimodal data through ML and LLM-as-judge classifiers. Even plugging in ChatGPT with a relevant judge prompt produces relatively believable results. At the same time, I struggle to trust it to generalize. I’ve read that Jev does classification across many domains and is also powered by an LLM back-end? What separates it from my above approach outside of harness efforts and finetuning of the LLM? Thank you!
Measuring ARR based on 7 days of data is beyond stupid.
You can create a company with 2B shares and sell one share to your friend for $1000. Lo and behold, you own $2 trillion dollar company, leaving Elon behind.
Especially for a company still in their 15 minutes of fame that could very plausibly turn out to be a one hit wonder and a trivia question in two years.
Multiplying out revenue from the peak of their mini hype cycle while a dozen well financed competitors target them directly seems extremely optimistic.
1/3 of the Fortune 500 is likely measured by something like a single @company.com put a credit card in, against their company’s procurement and security policies.
It’s not to be unexpected for a new product and startup, but they lack SOC 2 certification, and the other things enterprises look for on information security.
I’d be surprised if many procurement divisions are approving typesafe AI, and I fear by the time they achieve it, existing vendors would’ve catched up.
What? The moment this was released we integrated decision models EVERYWHERE. If you’re not using it you’re basically like these people who think LLMs are useless lol
This is all due to 40% marketing, 50% execution and 10% credentials (with the founders being associated with creating ChatGPT).
If anyone else came up with the same concept on a Reddit thread (they have) it no-one would care without those characteristics even if you are "first".
Rebranding, execution, marketing, ex- and mostly importantly, hype is what gets the investors scrambling into throwing money at you.
And the 'better' ones are slower and cost more for a tiny bit more accuracy. Its hard to sell that as being better when speed and price have been JEVs main selling points.
It’s fine to dislike AI slop and not engage with it. But I would claim there is a difference between a human submitting a sloppy paper to a journal vs producing one with AI. The former is lazy and unprofessional, the latter is an interesting experiment. I appreciate not wanting to engage in an experiment you didn’t sign up for, but therein lies the difference between this with an attitude of embracing new technology and those wanting to stick to the old. I think Terrence Tao has had a very interesting attitude to AI recently and had also had some fruitful outcomes from it.
I don’t think it’s that simple. I don’t think the AI labs are too concerned about bad press lately. It’s more likely that there actually are some tradeoffs where training on synthetic data gives the model tics but is the only way to improve intelligence.
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