Claude Code sucks but is still useful: experiences maintaining Julia’s SciML scientific computing infrastructure


Claude Code sucks but is still useful: experiences maintaining Julia’s SciML scientific computing infrastructure

So it’s pretty public that for about a month now I’ve had 32 processes setup on one of the 64 core 128gb RAM servers to just ssh in, tmux to a window, and tell it to slam on some things non-stop. And it has been really successful!… with the right definition of success. Let me explain.

This is a repost of the long post in the Julia Discourse.

* How is Claude being used, and how useful has it been?

j-bowhay, post:1, topic:131009

I think the first will answer the others. Basically, Claude is really not smart at all. There is no extensive algorithm implementation that has come from AI. I know some GSoCers and SciML Small Grants applicants have used AI (many without disclosure) but no wholesale usage has … READ MORE

Implicit ODE Solvers Are Not Universally More Robust than Explicit ODE Solvers, Or Why No ODE Solver is Best


A very common adage in ODE solvers is that if you run into trouble with an explicit method, usually some explicit Runge-Kutta method like RK4, then you should try an implicit method. Implicit methods, because they are doing more work, solving an implicit system via a Newton method having “better” stability, should be the thing you go to on the “hard” problems.

This is at least what I heard at first, and then I learned about edge cases. Specifically, you hear people say “but for hyperbolic PDEs you need to use explicit methods”. You might even intuit from this “PDEs can have special properties, so sometimes special things can happen with PDEs… but ODEs, that should use implicit methods if you need more robustness”. This turns out to not be true, and really understanding the ODEs will help us understand better … READ MORE