There really aren't though. The reason there's only three is because memory is a commodity and margins are historically very low. It's not a very good business to be in, generally.
In the past when memory supply was short and then rebounded, many companies went out of business because making memory was no longer profitable.
And margins will continue to be low, otherwise they'll discover they don't have a moat. Commodity markets being competitive is a self fulfilling prophecy.
The companies have two choices. They either produce RAM cheaply and in large quantities, or they get replaced by someone who will produce RAM cheaply and in large quantities. Current incumbents are free to pick which of those two scenarios they prefer.
There used to be over 50 memory manufactures in the US alone. Everytime there was a bust (following a boom) there'd be bankruptcies. The lucky ones got bought out and consolidated. Empirically, attempting to capitalize on memory booms is a losing strategy.
Antigravity is just a vs code (more correctly: codeium) skin with Google telemetry and agent Integration. You can switch back to Microsoft's or cursor's flavor in minutes.
PayPal isn't without flaws but I think buyer protections and the obfuscation of the banking info are genuine features for people. I would like a real competitor to PayPal in Europe. Maybe even something that is not from a private company.
What I fear is that people will try Wero, see that it's not what they wanted and then never go back. But maybe I'm wrong.
They seem to be doing let's say case studies on how AI based simulations can help industry.
They did injection molding for example, and I'm sure they're testing similar approach to everything which can be modeled by PDEs, which is well pretty much everything ever of engineering interest (I'm assuming this is somehow connected to a research project funded by Engel, one of the leading injection molding machine companies, located in the same Region): https://www.emmi.ai/models/neuralmould
I usually use PostShot.. but the quality was not very good. I want to try LichtFeld but my Graphics Card has too little memory.. so I reached out on twitter, some people ran tests and Mykhailo got some better quality out of it so I took his training. You can d/l the COLMAP dataset for free and try yourself.
If you don't focus stack and try to train on partially unfocused images, the optimizer will try to match the rendered view to be also partially unfocused.
You would have to mask out the blurry areas for each image. I guess one could just implement a feature where the optimizer only optimizes gaussians within the sharp distances relative to the camera.
Other way of looking at the question is if you could make focus stacking better by using the full multi-view dataset? Afaik focus stacking essentially does depth estimation so it seems like multiple views would help with that.
Another way would be some kind of 4d GS where one dimension is the focus distance. But I'd guess the renders would inherently have shallow depth then, which is less useful usually.
I'm not sure i agree. The blobs are exactly where the surface appear to be because they are constrained by multiple viewing angles.
Otherwise the splat would fall apart as soon as the viewing angle is changed slightly (Which it absolutely does in many examples on supersplat, you cannot really create an out of distribution view with 3GS, it's not magic)
Yes, my statement was loose. The blob doesn’t really have a position since it is theoretically an infinite distribution in 3 space.
It has a mean, and that mean doesn’t have to lie on the surface, consider the case where the mean is deep inside the strawberry but its spike contributes to the surface appearance (e.g a seed could be represented this way, or it could be represented by a small well-oriented blob on the surface, the optimiser doesn’t care)
The very fact that it is a tens-gigabit-per-second connection in an environment full of RF noise (we all love our high powered switching power supplies) makes it hard to get a reliable signal instantly. Still shouldn't take longer than a few hundred ms though (worst case).
The pretty generic TV I use a a monitor apparently keeps the links up with its three inputs, at least for a while after recent use, so switching between two powered computers is quick, about half a second. I started using that rather than a KVM since the KVM caused a retrain, adding several seconds to switching.
So I would say the most important thing is that the APIs these are using as in mlx5 DevX (essentially direct fw access) or ibverbs are exactly the same regardless if it's CPU or GPU talking to it. So with that in mind the source of rdma-core, DPDK, ucx etc may be the most elucidating when it comes to low level details.
For higher level patterns again the APIs are the same so anything building on libibverbs or aforementioned ucx etc are pretty compatible from a high level ideas perspective. If you are new to RDMA in general definitely start with raw verbs instead of using abstractions like MPI if you really want to build a good intuition and then move to MPI once you understand what it is doing for you.
reply