u/Georgiou1226

I would rather around 300€, flexible but I'd rather not go over 500€

I am in Germany (buying via MediaMarkt probably)

I have been using a Samsung Galaxy A40, and I am happy with it (although it was already going slow), but it just died after ~6 years.

Priorities, roughly in order:

1. Battery life

2. Compact, of possible I realize phones now are bigger

3. Runs smoothly for many years (I keep phones a long time)

4. Light gaming, nothing my A40 couldn't handle (with some struggle)

5. Long update support, for the same as 3

I don't care much about camera quality, AI features, brand

From what I've seen, a lot of people recommend the Pixels (something like 8, or 10a?). They seem good, but 8gb of ram doesn't sound like enough, they do not really have "high" specs compared to others.

I have also seen the Poco phones (like X8 Pro or F8 Pro) which seem to have better specs on almost everything, I'm unsure what the trade-offs for this are.

And staying with samsung it seems like the two best options would be the samsung A57 or the samsung s25.

As a side comment, I do not use the default launcher (I was using Niagara) and I would like to keep doing so.

Thanks for any input. Happy to be told I'm weighting something wrong.

Physics / Data Science undergrad here. I wrote this piece for Towards Data Science after finishing my statistical mechanics course, so the obstacle in the thumbnail is their logo rather than a cylinder, or something more normal, apologies for that. Most results below are from the standard circular cylinder case. I'd only ever used (and written) normal Navier-Stokes solvers before, and I remembered LBM being mentioned as a more parallel alternative, so I wanted to try building one.

I built a D2Q9 Lattice Boltzmann (LBM) solver from scratch on a 400x400 grid with the classic cylinder obstacle.

I've run it on a few different configurations since, but I don't have a good sense of how to get real insight out of it beyond the visuals and the parallelism speedup.

• Where does BGK actually blow up? At what Reynolds number does single-relaxation-time BGK become unusable or numerically unstable?
• How bad is weak compressibility in practice? I kept my Mach number low so I’ve been ignoring it, but in real engineering work, I assume it might matter.
• Boundary conditions: I just used simple mid-grid bounce-back for the cylinder. It works visually, but how much accuracy am I giving up compared to interpolated/curved boundary schemes (like Bouzidi)? What are the "gold standards"?
• Not eyeballing it: Right now my check is "the vortex street looks right." Is Strouhal number against Reynolds number the standard quantitative benchmark for this case, or is there something better people use to confirm a solver is actually correct and not just plausible-looking?
• Parallelism vs. modern FVM: The main selling point for LBM is always its local, very parallel nature (collide-and-stream). But with GPU acceleration and modern Finite Volume Method tooling improving so fast, does it still matter that much?

If you work with fluid dynamics or kinetic theory, I’d love to hear where my idea is off or where LBM hits a hard wall in reality.

Tested three formats: chat demos, first-person statements ("I am C-3PO..."), and synthetic Wikipedia-style docs. Same model, same LoRA config, 500 examples each.

First-person statements won on generalization, which I didn't expect. The synthetic doc model was the weirdest result: it knew C-3PO was anxious but only expressed it 37% of the time. Knowing a trait vs feeling it are apparently different things in weight space.

Code and GitHub repo link are included inside!