Ever wondered how to simulate 3D from 2D based primitives? Here is a nice experiment explaining how to approach it.

Of course I recommend reading the actual research paper. This article is a good summary of the consequences though. LLMs definitely can't be trusted with formal reasoning including basic maths. This is a flaw in the way they are built, the bath forward is likely merging symbolic and sub-symbolic approaches.

This is a short article summarizing a research paper at the surface level. It is clearly the last nail in the coffin for the generative AI grand marketing claims. Of course, I recommend reading the actual research paper (link at the end) but if you prefer this very short form, here it is. It's clearly time to go back to the initial goals of the AI field: understanding cognition. The latest industrial trends tend to confuse too much the map with the territory.

Need to know if two shapes overlap? Good explanation of an elegant algorithm to do it.

This gives a good idea of the important parts in a CAD program. It also list a few of the usable libraries to build one such program in the browser.

Funny experiment playing with the frequency domain and the spatial domain of an image. This gives unintuitive results for sure.

Very cool BSDF. Should lead to better diffraction rendering in real-time 3D.

I didn't know this book. It is written in a surprising style, but it's very much down to earth and to the point. For sure a good way to learn calculus.

On the importance of invariants and consistent requirements in our trade. Admittedly it's a long demonstration but it show the point well.

A response to "The Hunt for the Missing Data Type" article. There are indeed potential solutions, but they're not really used/usable in the industry right now. Maybe tomorrow.

Indeed, graphs are peculiar beasts. When dealing with graph related problems there are so many choices to make that it's hard or impossible to come up with a generic solution.

Interesting exploration of what could be done in a 3D engine using plane-based geometric algebra (PGA). This brings in nice properties that matrices don't have. And the performance impact is apparently not as bad as one could have suspected. I definitely look more into it.

Neat article about colorspaces. Definitely worth reading if you're curious about the topic. It also has interactive bits to ease the understanding.

Or how calculus can give a feel of why approximation errors can be great or small with floats.

Some reasons why Python and C behave differently on this matter. It's a source of mistakes.

Interesting explanation of the method of differences to easily compute polynomials.

Wonder how to implement such real-time simulations? This is a good summary of all the math involved. Also comes with code snippets and demos.

Probably the definitive resource on how floating-point arithmetic works.

Another nice introduction to raymarching. I still find this a very interesting rendering approach. It's really cool what you can do with those Signed Distance Fields functions.

A nice reminder that the π value is not as set in stone as we tend to believe. It depends on the metrics we're using.