Why Nim, Zig, and Crystal Never Took Off (And What Programmers Miss) - YouTube
So why aren't these languages mainstream — even when they outperform the tools everyone uses? In this video, I break down why technically superior programming languages fail to get adopted:
00:00 Why good design doesn't guarantee adoption 00:38 Nim 01:35 Crystal 02:37 ReScript / ReasonML 03:28 Kotlin Multiplatform 04:14 Zig 04:56 V-lang 05:31 What actually makes a language succeed
Why the Fastest AI Systems Are Written in a 70-Year-Old Language - YouTube
Python gets all the credit. Fortran does all the heavy lifting. NumPy, SciPy, and some of the fastest linear algebra libraries in the world are built on Fortran underneath. The language that everyone calls a "dinosaur" is quietly running the infrastructure that modern AI and scientific computing depend on. This video breaks down why Modern Fortran is still the go-to choice for engineers working at the edge of what computers can do — and why that's becoming more relevant in 2026, not less. What we cover: → Fortran vs Python: real performance benchmarks, not theory → Why NASA, CERN, and leading AI labs never stopped using it → How NumPy and SciPy secretly depend on Fortran under the hood → Parallel programming and HPC — why Fortran still leads here → Modern Fortran in 2026: what it actually looks like to write it today → How to get started if you come from Python, C++, or data science
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