julia

MIT Press's 634-page book "Algorithms for Optimization" is available as a free CC-BY-NC-ND PDF, with the second edition (featuring Julia implementations and new chapters on DCP, surrogate models, and multidisciplinary optimization) releasing January 5, 2027.

A set of open-source notes for learning calculus using the Julia programming language, emphasizing graphical, numerical, and algebraic approaches with self-graded exercises.

This article explores FunSQL.jl, a Julia library for SQL query building, and compares its data-oriented interface with other query builders, highlighting its unique combination of compositional features and full SQL support.

Julia 1.13 has been released with significant improvements in latency (TTFX), REPL functionality, hashing, garbage collection, and other areas, thanks to community contributions.

The article traces the origins of the Julia programming language from an MIT research project to a global tool used by over a million people, and highlights the launch of Dyad 3.0, an AI platform by JuliaHub for automating engineering simulations.

GaussianSplatting.jl 2.0 release brings multi-GPU backend support via KernelAbstractions.jl, a multithreaded UI, MCMC densification strategy, and depth/geometry supervision for better 3D reconstruction.

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.