JuliaCon 2025

To optimize code, we must have good tools for measuring performance. Chairmarks.jl measures performance substantially faster than BenchmarkTools.jl while gathering more data at a similar accuracy. By avoiding eval and repeated gc calls and an efficient tuning algorithm, Chairmarks achieves efficiencies of up to 99%, making setup and tuning time negligible. This allows for a more straightforward API and a much snappier user experience (from 5 seconds to 100ms).

We’ll share updates on standalone executables, binding replacement, Pkg features, threading stabilization, and RISK-V support. We’ll also discuss the impact of AI on Julia development and open source relations and how Julia language development is organized and directed. Finally, we’ll discuss future work already in the works and speculate on future work that may soon commence, followed by a Q&A panel.

By using novel optimizations on top of Walker's alias method for random sampling, AliasTables.jl implements a discrete random sampler that supports arbitrary category weights and provides O(n) construction and O(1) sampling with a constant factor of 20-40 clock cycles for construction and 5-10 clock cycles for sampling.

BasicAutoloads.jl allows you to automatically load packages on use in the REPL. This streamlines workflows without adding the startup cost or precompile and memory bloat of proactively loading packages. The API and semantics are both incredibly simple and, by virtue of supporting arbitrary Julia expressions, surprisingly powerful.

Put WatchJuliaBurn.jl in your startup.jl file and you'll rarely be more than a keystroke away from a game of space invaders! And when I say keystroke, I'm not referring to just any key, certainly not that F13 key tucked away behind your monitor, I'm referring to the space bar.

But don't worry! Like any other REPL mode, a game of space invaders is entered by pressing a key at the start of a line that expressions don't normally begin with so this should not impact your existing workflows.