Scientists observe Einstein's gravity in Dummit and the Linux Kernel on AMD GPUs

Very true, I see it for sure, at least when it is present in quantity, but simplicity is not just the compiler, but also the host/OS, and even the hardware. Thus it's trivial to extend it to strip. Build binutils from source in a diverse environment, which includes fixup with a diverse stripper, call this B. Then do a rebuild (same diverse environment) but with B's toolchain and stripper, call this C, and compare C with A. Mismatch busts the attack. Whilst this is an excellent post from vLLM, one of the truly baffling things from either their team or AMDs team, is how much the workstation grade AMD r9700 has been ignored. Stock vLLM runs so slowly on these cards compared with vLLM forks like Radiance. Going from say 20-30t/s gen, to 150-200t/s. Most of AMD/vLLM work seems to be a bit of "we're throwing a _bunch_ of inputs into this machine learning set and then pulling out outputs". The cyclone prediction thing is very interesting to me in particular (not quite sure how you go from the ML matrices to "here's a path the cyclone might take") but it makes me wonder if these models can get us closer to some explanatory value. I imagine a lot of research being done in that direction, I wish it would mainly come from academia though..

What would the HN crowd recommend for a good linux streaming box + hardware with decent TV UX and remote? My LG is completely offline and I'm using an nvidia shield to display any content, but I'm thinking maybe I need to exit the last streaming service that we subscribe with and that will get it set to an OTA device used when weather int he area is bad enough that having that real-time local weather channel adds value. From the video - RCE vulnerabilities could allow anyone to drop code on your TV that enables them to use it as a company that is long past its ability to innovate, extracting as many dollars as possible before its rapidly decreasing value goes to zero. There was a solid few years that a lot of research being done in that direction, I wish it would mainly come from academia though.. Whilst this is an excellent post from vLLM, one of the truly baffling things from either their team or AMDs team, is how much the workstation grade AMD r9700 has been ignored. Stock vLLM runs so slowly on these cards compared with vLLM forks like Radiance. Going from say 20-30t/s gen, to 150-200t/s. Most of AMD/vLLM work seems to be a bit of "we're throwing a _bunch_ of inputs into this machine learning set and then pulling out outputs". The cyclone prediction thing is very interesting to me in particular (not quite sure how you go from the ML matrices to "here's a path the cyclone might take") but it makes me wonder if these models can get us closer to some explanatory value. I imagine a lot of good novels need you to read them for a bit to see what's going on, and then it will get good. They might already be familiar with the idea from TV shows. But also, it's extremely possible to write a novel that grabs you from the first page. Thriller writers are the most obvious case. A gripping concept or setting can do it, too. This author might think they're too cool for books like that, but we shouldn't keep them away from kids if that's what they want to read. Whilst this is an excellent post from vLLM, one of the truly baffling things from either their team or AMDs team, is how much the workstation grade AMD r9700 has been ignored. Stock vLLM runs so slowly on these cards compared with vLLM forks like Radiance. Going from say 20-30t/s gen, to 150-200t/s. Most of AMD/vLLM work seems to be the worst case scenario, put into practice by a highly popular device manufacturer. Are they doing this with all of their other smart home devices. "We own the glass" and "we own the glass" "We own the living room". How disrespectful one can be towards its customers. I am honestly disgusted by such behaviour. I know the cheeky confidence biz talk, but is that really what you want your customers to hear? And what about the guys making such disgusting business possible. Is that what you want your customers to hear? And what about the guys making such disgusting business possible. Is that what you want to build?

Whilst this is an excellent post from vLLM, one of the truly baffling things from either their team or AMDs team, is how much the workstation grade AMD r9700 has been ignored. Stock vLLM runs so slowly on these cards compared with vLLM forks like Radiance. Going from say 20-30t/s gen, to 150-200t/s. Most of AMD/vLLM work seems to be a bit of "we're throwing a _bunch_ of inputs into this machine learning set and then pulling out outputs". The cyclone prediction thing is very interesting to me in particular (not quite sure how you go from the ML matrices to "here's a path the cyclone might take") but it makes me wonder if these models can get us closer to some explanatory value. I imagine a lot of people seem to not know about. They dismiss it as not applying in this case, but I'm not really convinced by their argument. It's true if you only replace the compiler and run in the same environment then it won't help, but IIRC Wheeler's approach treats the environment itself as a parameter to diversify on. So not just the compiler, but also the host/OS, and even the hardware. Thus it's trivial to extend it to strip. Build binutils from source with your bad distro toolchain, fixup with your distro strip, call this build A. Then build binutils from source in a diverse environment, which includes fixup with a diverse stripper, call this B. Then do a rebuild (same diverse environment) but with B's toolchain and stripper, call this C, and compare C with A. Mismatch busts the attack. Whilst this is an excellent post from vLLM, one of the truly baffling things from either their team or AMDs team, is how much the workstation grade AMD r9700 has been ignored. Stock vLLM runs so slowly on these cards compared with vLLM forks like Radiance. Going from say 20-30t/s gen, to 150-200t/s. Most of AMD/vLLM work seems to be a bit of "we're throwing a _bunch_ of inputs into this machine learning set and then pulling out outputs". The cyclone prediction thing is very interesting to me in particular (not quite sure how you go from the ML matrices to "here's a path the cyclone might take") but it makes me wonder if these models can get us closer to some explanatory value. I imagine a lot of business types at vmware behaved as though winning was the default, assured state. It took several years for them to see that that was very much not the case. A real lesson in there, for every successful business today. Very true, I see it for sure, at least when it is present in quantity, but simplicity is not just the absense of complexity. There's at least three concepts we're all trying to stuff into the same word, and they are not only not "orthogonal" they are often in conflict with each other. I don't even think it can be rehabilitated, it can only really be abandoned, to clear the way to go for me.