In a society where the abuse of human labour was factored into the cost of the product, the 8-inch fab line would have been shut down, since the cost of the 8-inch wafers would now be prohibitive and not be competitive with the wafers from the 12-inch line. This in-turn would mean that customers would have to switch over to the 12-inch wafers.
We are not supposed to compete on who can abuse their workers the most to improve efficiency and to cut costs. Thankfully, knowledge work does not seem to scale the same way as manual labour, meaning that more abuse of the workers does not mean more output over the long-term.
We have a replacement for CUDA, it is called C++17 parallel algorithms. It has vendor support for running on the GPU by Intel, AMD and NVIDIA and will also run on all your cores on the CPU. It uses the GPU vendors compiler to convert your C++ to something that can natively run on the GPU. With unified memory support, it becomes very fast to run computations on heap allocated memory using the GPU, but implementations also support non-unified memory
GPGPU programming seems to be in a really good spot with the widespread adoption of C++17 parallel algorithms by GPU vendors.
Now, I can just program against this API using standard C++ code, that interacts with CPU heap allocated memory, and get really performant computation on it using standard map-filter-reduce semantics.
Which begs the question, if attractiveness is such a big predictor of success, why hasn’t everyone evolved to become very attractive? The evolutionary pressure for increased attractiveness should be very high, since it affects so many areas of your life, from career success, to getting partners etc.
Is it that the speed at which we evolve to become more attractive is superseeded by our ability to become better at discriminating for attractiveness?
That was why OP suggested to have 2 motors on each joint, going in opposite direction. The problem with this is that you now have twice the amount of motors.
If you are already using gRPC in your codebase, then you can define your enums with Protobuf, which does much of the same as the tool shown in this article.
Well, if I found an unpatchable zero-click RCE exploit on an iPhone, then that would have a major effect on the stockprice when the news is announced. It definitely seems like it would be insider trading if i used that knowledge to do options trading.
Could someone enlighten me on how one goes about testing whether a particular crypto implementation is vulnerable to side-channel attacks?
In high school I implemented a basic ECDH key exchange algorithm, which I compiled to WASM, and it can be tested at the bottom of my blog: https://gredal.dev/projects/elliptic-curves
Using only the WASM blob, without looking at the source code for exploits, how would Alice find Bobs private key?
Just because a compiler cannot prove whether or not a given set of C/C++ is safe, doesn’t mean that an expert in the language can’t write safe C++ and prove that it is actually safe.
Why not just tax stock buybacks the same or higher than regular profits? This would force companies to either increase R&D/CAPEX spending or lower prices.
Edit: Nevermind, I think I have misunderstood the purpose of this project. I thought it was a CUDA competitor, but it seems like it is just a shading language compiler for graphics.
I have a medium sized dependency-free TKinter program written in Python, and AFAIK there is no way to run it in the browser.