Software Engineering

DJ Dave showcases the power of strudel. The programming language I use varies depending on the software I develop, I write polyglot software. It's a lot easier now with AI. I use Go for platform engineering, Python for data science (I prefer Julia, but it is limited to CUDA), I prefer Elixir for writing APIs, and TypeScript a must for Qwik.

Sometimes I think too hard and believe I need to design application specific integrated circuits (ASICs) and write memory safe applications in Ada. Ada’s a little slow but it’s safe. Rust isn’t as safe, but it’s faster. Bjarne Stroustrup’s introduction of concepts to C++ is cool, but C++ is like bowling with the gutters down.

3D integrated circuits are the future, AI is needed to design them. Proprietary software is too expensive for me. The closest open source equivalents are not as good, but China is closing the gap in both design and fabrication tooling. ASML lithography machines are still the best, and there’s only a couple fabs in the world that can afford them. The licenses for proprietary instruction set architectures (ISA) are also too expensive for me. I love RISC-V’s free and open source nature. It’s backed by the Linux Foundation. It recently got mainline GPU support, but it’s not as featureful as x86-64/ARM. I wish for Sylkan to remove the Rusticl-on-Zink translation and go straight to Vulkan.

NVIDIA’s CUDA programming language is more featureful than AMD’s ROCm and OneAPI (CUDA had accelerated vector search when I needed it whereas AMD and Intel did not). I personally prefer AdaptiveCpp for its heterogenous hardware support, though it still has work to do in order to catch up to CUDA. I switch hardware around and I don’t like to rewrite code. I like what I see out of the Berkeley Architecture Research Lab — specifically Chisel (based on Scala which I prefer for data engineering). VHDL is a little outdated. Chisel compiles down to Verilog.