Hi everyone! I’ve been seriously considering investing time into Julia for scientific computing and operations research, and I genuinely want it to succeed: the promise of high-level syntax with near-C performance is incredibly appealing.
That said, I recently came across discussions like “What’s is bad about Julia” (updated as December 2025) or “Why I no longer recommend Julia” which mention issues such as bugs in Base and general instability in parts of the ecosystem (The fact that sampling from a probability density could produce an incorrect result is scary to me). I’m trying to understand how much of that still applies today.
For those of you actively using Julia in scientific or production contexts:
- How stable is the language and its core libraries right now?
- Are bugs in Base still a practical concern, or have things matured significantly in recent versions?
- How confident are you relying on Julia for long-term, correctness-critical work (e.g., optimization, simulations, OR models)?
To give more context, my use cases within operations research include building my own solvers (especially metaheuristics) and calling them from Python, as well as running discrete-event simulations in a language that’s significantly faster than Python.
One thing that gives me pause is the comparison with Rust. Even though Rust is newer than Julia, it seems like nobody really questions the stability of the language itself, especially in terms of correctness guarantees. That contrast makes me hesitate.
I’m also weighing alternatives, and part of me wonders whether it’s unreasonable to consider Rust for operations research workflows, given the higher development overhead.
Would really appreciate candid perspectives—especially from people who’ve used Julia in non-trivial, real-world projects.
