392 karma · joined June 28, 2025
Hey, I played with that when I was approximately 8 years old! Also: wireless printing via infrared, instead of those huge parallel printer cables.
I thought they're still selling gaming GPUs? What did I miss?
Can an expert in sat solving comment if there's any immediate applications of the method from the paper? Or is SAT solving in general pretty far away from worst-case times, and this is a theoretical algorithm which would be hard to apply in practice.
As long as tokens are as energy intense to compute as they are right now, I don't see a flatrate being a viable model.
Not sure why OP would call it amateurish/botched.
If you start thinking more about this, more and more problems pop up.
All of which is picked up from my apartment buildings backyard.
one would hope that they disable websearch and internet access (maybe all tools?) when doing generalization testing?
Just as aside, lithium-battery powered smoke detectors are a godsend. The battery lifespan lasts the recommended life span of the detector, i.e. around 10 years. No battery replacements needed, although you still ought to test them once in a while.
> On Apple's unified memory that transfer compiles to almost nothing. It is still written down, because data locality should be provable by reading the source rather than by profiling the binary.
How does profiling the binary play into this? Elsewhere the hypothesis is "it crashes", whereas profiling is about performance.
> Most languages treat the accelerator as infrastructure: you write math, and a large opaque runtime decides how to ship it. Vx treats it as semantics.
What does that mean? Vx doesn't treat the accelerator as infrastructure? Vx doesn't have a large opaque runtime? "You write math, Vx treats it as semantics". I don't understand that.
so the contrast here is the budget available, not the quality of the talent, if we accept the premise that DeepMind and the universities have approximately similar level of talent.
I don't quite understand your point here. OpenAI has a consistent history of releasing cheap/small models - first nano/mini, then luna/terra. Of course, those are now more capable than half a year ago, but I don't see a behavior change from OpenAI here.
I'd expect that their business strategy is to compete in more markets, and if successful, they can capture more value. This is the "easiest" for them as they already have GPUs, a training environment etc. For that platform it's not the worst if there's an internal customer team that can help shape the future and provide immediate feedback, and if it results in a good model, even better.
Other things I'd not be surprised they offer in the future in the same vein: A multi-model harness, coding agent (cloud and local), and maybe at a later point in time even a CPU-only cloud compute product.
To be fair, though, I mainly ran this experiment to distinguish models, not to see if they were better than humans or not, so for that purpose it didn't really matter if the basic conditions were the same or not.