I really like Julius O. Smith book [0] on physical audio signal processing. It covers some of the basics discussed in this article, and goes much deeper into modeling and applications
I love this blog so much that I used it as a guide for building my own series of explainers for how a general circulation model of the earth's oceans and atmosphere works: https://webgcm.app/explain/air
I put nowhere near as much care into the interactives but the inspiration for the format comes 100% from the Bartos Ciechanowski blog.
Oh how I'd wanted to have this when I was a kid learning an instrument. I had all these questions but no one had a answer for me. Music teachers were like "because it's the 3rd, or because it's the 5th". If I could go back in time I'd show them this. THIS! SEE IT! THIS IS WHAT I WANTED TO KNOW!
The progression here is what impressed me most... You start with individual air molecules bouncing around and somehow end up understanding harmonics, interference and etc. Love this kind of articles!
On one hand, it's great that more people are able to more easily make things like these. They're a great tool for understanding, but obviously an enormous amount of work.
But on the other hand, I also wonder if those that get pumped out now, also uses up all the oxygen in the room for the really great ones.
Like, someone shoddily slaps together one for concept X. It's not great, has some misunderstandings, and what it visualizes is mostly random and not the vision of the maker. But it goes semi-viral, and beat everyone else to the punch. So it ruins someone else's motivation to do it properly.
I'm not sure it ruins the motivation. e.g. Rayleigh scattering is one of those norotiously bad explanations that's for some reason still bad. At some point I ended up on the topic with my 5 year old the other day, so I figured I'd check what's out there, and yeah, it's still bad: everything starts with "why is the sky blue" and talks about scattering which doesn't mean anything when you're 5. So I have codex whip up an animated demo for what the question should be: why isn't the sky black? Once you understand that that's the correct question, then you can properly add in light bouncing in the air as an effect, and that blue bounces more, therefore you see blue at off-axis angles (the whole sky), and red remains in the direct rays (c.f. red sunsets where most blue has scattered away from the longer rays).
Anyway, the motivation here is just that the existing explanations are already bad, and AI helps with motivation here because it makes it super easy to do a better explanation if you know what you want. I doubt that the it really sunk in and that she could properly wrap her head around it yet, but I'm sure that whenever she's ready to really think about that, it's a much better explanation than any of the ones that exist today.
What really impresses me about these AI visualisations is that they're often (but by no means always) fully screen reader accessible, with proper keyboard and ARIA support. Those by Ciechanowski very much aren't, and even vibe-coded slop accessibility is better than no accessibility at all.
Accessibility with AI is hit-and-miss; if you don't tell it to make things accessible, it either will or it won't, and then it'll just continue with whatever trajectory it chose. If you tell it to do accessibility initially, it'll see its own ARIA later and continue in a similar vein.
From very anecdotal experience, I find OpenAI models more likely to consider accessibility for greenfield projects, even when not asked to, but this might just be an accident of statistics.
In 2022, I worked on a project to build a climate model that runs in your browser. I got as far as I could until the math and physics became too difficult. Now 4 years later I figured that if frontier models can solve open questions about Navier-Stokes equations then it can probably help me fix my climate model and it has been great! I also worked with it to create this 10-part series (still in progress) to explain how the climate (and the model) works.
Reading this guy blog posts makes me feel like when I was reading as a kid good pop-science article in the magazine or encyclopedia. Somehow it's easy to follow, nicely visualise and I can trust his work was verified because he was writing like this far before AI.
Your question pointed me to look and here - Bartosz (read Bartosh like Shalom) has a Patreon page. You can even see what he's working on. patreon.com/ciechanowski
Wish he would do some physics lectures. I always wanted to understand how antennas work.
My 10 yo son just spent an hour clicking through the widgets and, frankly, learning quite a lot, with just a few additional explanations from me. Another hit from Bartosz!
[0]: https://ccrma.stanford.edu/~jos/pasp/
I put nowhere near as much care into the interactives but the inspiration for the format comes 100% from the Bartos Ciechanowski blog.
But on the other hand, I also wonder if those that get pumped out now, also uses up all the oxygen in the room for the really great ones.
Like, someone shoddily slaps together one for concept X. It's not great, has some misunderstandings, and what it visualizes is mostly random and not the vision of the maker. But it goes semi-viral, and beat everyone else to the punch. So it ruins someone else's motivation to do it properly.
Anyway, the motivation here is just that the existing explanations are already bad, and AI helps with motivation here because it makes it super easy to do a better explanation if you know what you want. I doubt that the it really sunk in and that she could properly wrap her head around it yet, but I'm sure that whenever she's ready to really think about that, it's a much better explanation than any of the ones that exist today.
Accessibility with AI is hit-and-miss; if you don't tell it to make things accessible, it either will or it won't, and then it'll just continue with whatever trajectory it chose. If you tell it to do accessibility initially, it'll see its own ARIA later and continue in a similar vein.
From very anecdotal experience, I find OpenAI models more likely to consider accessibility for greenfield projects, even when not asked to, but this might just be an accident of statistics.
Because he's writing the WebGL code by hand. It was crafting these entries, not merely producing them for internet points.
(also tufte: https://en.wikipedia.org/wiki/Edward_Tufte)
In 2022, I worked on a project to build a climate model that runs in your browser. I got as far as I could until the math and physics became too difficult. Now 4 years later I figured that if frontier models can solve open questions about Navier-Stokes equations then it can probably help me fix my climate model and it has been great! I also worked with it to create this 10-part series (still in progress) to explain how the climate (and the model) works.
[1]: https://webgcm.app/explain/air
Sound - https://news.ycombinator.com/item?id=33249215 - Oct 2022 (206 comments)
Wish he would do some physics lectures. I always wanted to understand how antennas work.
[1] https://news.ycombinator.com/item?id=42443229