bssrdf

The water effect is stunning. The code is open source now at https://github.com/dgreenheck/tidewater

I found [ggml] is really easy, educational and fun to use. It is written in C and the backbone for llama.cpp.

[ggml]https://github.com/ggml-org/ggml

I would like to recommend "Understanding UNIX/LINUX Programming: A Guide to Theory and Practice" for beginners.

You can download source code I assembled https://github.com/bssrdf/UnderstandingUnixLinuxProgramming

Indeed. Even a simple 'wc -l' type operation can benefit from SIMD [0]

[0]https://x.com/cloud11665/status/1799534538873290993

Yes, actually it is a doubly linked list.

I don't think a PR is ready. My solution does not fully solve the problem. It requires more thoughts. Thanks.

Good point, I have the same doubt. Even if it is sorted, you still cannot do binary search on it unless using some skip list ideas. In fact, the code right now is doing linear search to find the ID given a name.

TBH, I don't fully understand how the ID linked list is utilized in Blender and why it needs to be sorted by names. It seems some other data structure could also work, unless I missed something.

I didn't submit the PR yet, but opened an issue which has not been picked up. Maybe most of the USD files used in Blender do not trigger the O(N^2) behavior.

Just finished writing [0] about Blender's USD import issue.

[0] - https://news.ycombinator.com/item?id=36947946

The key word: TUNING. Every climate model has tons of tunable parameters, many of which are not from first principles. They are empirical, observed some years ago over some remote locations, but now applied globally as the truth.

I implemented radix sort in cpython [0] and did some simple benchmarking comparing to python' built-in sort (Timsort I believe). It is indeed faster.

[0] https://github.com/bssrdf/RadixSortPy

This book <<The Nature of Computation>>[0] has the best explanation I have ever seen.

[0]: https://www.amazon.com/Nature-Computation-Cristopher-Moore-d...

You can even play with the algorithm they used "The Affine Particle-In-Cell Method" using the code discussed here

https://news.ycombinator.com/item?id=13325190

Kajiya's rendering equation

Still debating whether I should start with An Introduction to Statistical Learning (ISL) or Bishop's Pattern Recognition and Machine Learning (PRML). I really don't like using R (always a python person). Both have rave reviews on Amazon. Any thoughts?

Because it is Elon Musk?

Loss of Land Ice (Not Sea Ice) = More Sea Level Rise

SCHWEIGER: “Melting sea ice has no impact on sea level rise because it’s already floating in the ocean.”

Like a glass of ice water. As it warms, the ice in the glass melts, but the total volume of water does not change. However, melting sea ice does contribute to climate change. That’s because white sea ice reflects the sun. So when it melts, the dark open ocean now absorbs sunlight and heats up, raising global temperatures, which in turn cause glaciers and ice sheets on land to melt further. Globally, sea levels have risen four to eight inches since the last century and will continue to rise as the ice melts, putting coastal communities worldwide at risk.

http://www.yaleclimateconnections.org/2014/11/loss-of-land-i...

The author clearly confused the land ice/ice sheet with sea ice. Do you have evidence supporting your claim that "Melting sea ice can change sea levels by changing the density of the water in the ocean"? I'd be curious to know.

"In terms of sea level changes, however, the study found the effects of melting are more drastically felt in regions farther from its source. According to InsideClimate News, melting sea ice in the Northern Hemisphere might raise water levels more in “the southern Pacific Ocean and equatorial regions.”

With all due respect, but melting sea ice doesn't raise sea level. It's a scientific fact the author should check carefully before publishing.

At least from one report I read sometime ago, scientists in Los Alamos National Lab were looking at alternatives to Fortran for their future generations of climate models. C/C++ is under their radar. One reason for the switch is to better deal with unstructured grids. They are moving away from the traditional rectilinear grids. This is more in line with many FEM applications where C++ dominates.

He is now the co-author on the 3rd Edition. Expecting coverage of the Volumetric Layered Materials and Manifold Exploration. PBRT is only getting better.