julia

Laguna S 2.1, a 120B-class model, impressed by solving a complex coding problem in Julia with long thinking tokens, outperforming Qwen models on a memory-constrained rearrangement task.

This Wired article examines Python's performance limitations in scientific computing and discusses Julia as a potential solution to the two-language problem, drawing historical parallels from Turing Award lectures.

Pluto.jl 1.0 is a reactive notebook environment for Julia, enabling interactive and reproducible computing with automatic reactivity.

A 2019 blog post from FLOW Lab at BYU explores how to optimize Julia code to match C++ performance using a real-world aerodynamics application (vortex particle method) as a benchmark. The author shares lessons learned about achieving high-performance computing in Julia through type declarations, JIT compilation, and code optimization techniques.