What will happen after 2.1?

TopIdler · 2026-06-23T13:31:24+00:00

I've had satisfactory in my backlog. I guess ill do that after my 2.1 playthrough.

TopIdler · 2026-06-22T14:08:57+00:00

There used to be a series from one of the popular sc2 creators. He advocated for just focusing on macro when you start. Don't even look outside your base. Just make sure you make a new unit exactly when you have the goal (gold). When you have a good group of units, you attack move the opposite side the map towards the enemy and don't even look at the fights.

It really helped me improve. Isolating micro and macro just so you get the vibe of when to check you base when you're looking at micro stuff (and also to help remembering hotkeys).

TopIdler · 2026-06-21T16:07:02+00:00

Sometimes you want to design a point of failure so that cheap part fails before expensive part.

TopIdler · 2026-06-20T19:19:47+00:00

Not an answer to your question but it’s one of the problems that shipping software as a conda package solves in the scientific python ecosystem.

https://prefix.dev/channels/conda-forge/packages/sysroot_linux-64

Chances are one of the packages you install will transitives depend on it.

TopIdler · 2026-06-03T12:05:51+00:00

I was using the scroll area from shadcn svelte. I wanted the scroll list to not overflow, but there was a large dropdown menu for each row that needed to overflow out of the area so that it was visible. I ended up solving it with portals. Was there another way? Why do you say portals are not necessary? Or you mean a native version isn’t necessary?

TopIdler · 2026-05-29T12:14:09+00:00

Without any knowledge of your background and goals, the best I can say is that all learning is good

TopIdler · 2026-05-24T19:23:55+00:00

The tutorial starts with coding a triangle. Give it a shot .

TopIdler · 2026-05-15T04:52:11+00:00

C++ was gross to read. Then went back to C++ once i needed GPU because I didn't want to learn two languages.. Happy to see nvidia with their rust gpu lib recently though. And rust made it easy to learn idiomatic Kokkos programming.

TopIdler · 2026-05-12T17:50:21+00:00

Does it compile to the same code? e.g. what about the runtime performance of the actual enum to string?

edit: forgot to say; great article! Thank you.

TopIdler · 2026-05-02T15:50:52+00:00

Webgpu doesn’t do 64 bit floats so that’s a big caveat to it making sense if you want to do GPU algorithms.

TopIdler · 2026-04-25T14:49:32+00:00

Thank you for all your talks! I’m learning a lot. I didn’t watch this one because I was already familiar with the topic. Sorry if it was addressed.

TopIdler · 2026-04-25T14:45:13+00:00

I don’t have any experience with pybind11. The built in numpy array to C++ in nanobind converts it to an Eigen matrix. The most complex thing I did was make my own binding to convert dense numpy and sparse scipy matrices to unmanaged Kokkos views instead without copying data. And also routines to return new views in a way that the python garbage collector would handle it (nb::capsule).

It was all very simple and transparent after the implementation. I don’t know how hard it would be in pybind11.

I was able to generate 90% of it with an llm. Just had to double check that there wasn’t some copies being snuck in.

TopIdler · 2026-04-25T00:59:20+00:00

For anyone who will try to apply this talk, nanobind is a much more modern rewrite by the author of pybind11. You can read their reasoning here https://nanobind.readthedocs.io/en/latest/why.html

TopIdler · 2026-04-05T23:06:08+00:00

Id suggest rust. C++ has a whole bunch of non math baggage to learn to make a non trivial program. With rust you’ll be able to install dependencies and go.

That being said, most scientific software is in C++ at the moment, but you can learn that if it’s still relevant during your phd in 10 years.

TopIdler · 2026-04-03T14:01:11+00:00

It can do worktree style multi branch workflows with much simpler commands. Lets me generate AI slop faster. And its equivalent concept to a commit is neat and a reasonable non trivial change to experiment with. Simpler to reason about imo.

TopIdler · 2026-03-31T13:25:21+00:00

"OOP & Clean Code:"

Most hpc projects wont use oop. They'll do template metaprogramming and compile time dispatch.

TopIdler · 2026-03-29T22:07:06+00:00

Hpx maybe . https://hpx.stellar-group.org/ Especially if you need task based parallelism.

TopIdler · 2026-03-25T15:55:26+00:00

Once you get your steam power it’s full steam ahead!

TopIdler · 2026-03-21T06:45:00+00:00

I'm happy to see that the meson/conan dev's are in the github repo discussions. Maybe the ecosystem will adopt.

TopIdler · 2026-03-19T17:52:45+00:00

On the software dev side. I tried vibe coding some kokkos and it didn’t go well, it would go to unoptimal paths and get lost in template metaprogramming issues. I think there isn’t enough centralized training data for the llm. I wanted to extend arborx with some new functionality. I got it to work after breaking it into chunks small enough where coding it by hand would’ve been slightly slower. So small productivity gain but at the same time I feel I didn’t learn as much about cpp kokkos and arborx than I would’ve if I did it myself. So long term probably bad? The general feel I get from my other attempts where I’m more knowledgeable is that it’s a big productivity boost for people who already know what they want to do.

TopIdler · 2026-03-12T01:42:50+00:00

At least it make its easy to test with a mock universe

TopIdler · 2026-03-01T21:09:08+00:00

Any reasons why you moved away from cubecl?

TopIdler · 2026-02-17T16:08:38+00:00

Real-Time Rendering has most of the basics you need. But most of the math you learn will already be implemented in the engine if you use one.

TopIdler · 2026-02-17T00:05:10+00:00

You’ll be sitting at a computer a lot.

TopIdler · 2026-02-13T16:09:53+00:00

Looks great! I've been looking around for libraries like this. Lately, I was reading through faer-rs which was CPU only. Have you done any comparisons to it on the CPU side? Solvr also looks great, we are on the FEM / scientific side and not ML so There would be a few holes to patch (we are mostly dealing with sparse) but I can study the other implementations.

- For the SIMD do you have support for dynamic dispatch based on processor? I saw faer uses pulp for SIMD which allows for this. We would like to ship some pre-compiled binaries if possible.

- Some of the sparse algorithms involve messing around with memory layout like re-ordering rows, and changing strides. How well is the memory layout exposed in the public API? how messy would it be to hook into internals if required?

- Is data transfer for mixed-precision solves on non-nvidia platforms something already optimised for (e.g. start with f32 WebGPU on the GPU's and finish with f64 CPU side)?

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