It’s kind of sad IMO. Bartosz has made a ton of these super interesting and meticulously designed explainers. Something thrown together with AI is much more likely to be made by someone who doesn’t know what they’re talking about, and I’m worried that the sheer volume will crowd out actually quality content like this.

Don't think so, and we should stop spread damaging narrative like this. I'd say it's already able to imitate this kind of explainers(badly) thanks to his training data. All the subtle teaching nuances, effort, know-how and visual creativity that people like Bartosz Ciechanowski put on this kind of work is not reproducible if not statistically imitating it

Haven't people been saying this since 2023? Yet to see AI build this kind of stuff "quite quickly".

It's been said before, but this prediction isn't amazing, imo.

I look forward to Bartosz's articles because they're rock-solid sources of information and the visualizations are both easy-to-understand and surprisingly light on performance. It's all shockingly digestible.

Honestly, as popular science writing goes, this is art as far as I'm concerned, and art is best when it comes from a place of passion and conviction, something AI will never be able to reproduce.

I think it's actually already there. It's definitely possible to make these sorts of explainers with something like a Claude Code, you just have to spend a fair amount of time making sure that it's actually doing what you expect it to do. I think the biggest danger with something like a Claude Code is that you get something that looks functionally correct but that the details are suddenly wrong on. I wrote a little bit about this on my blog for some of the places that I've done visualizations actually, and I think it's remarkably easy to iterate on them now.

https://estsauver.com/blog/scaling-visualizations

Good rule of thumb: it should take less time to consume content than it does to create it.

I don’t know how long it takes Ciechanowski to create these explainers, probably a few months? It shows and it’s well worth spending your time reading through his content meticulously.

How long does it take for an LLM to crap out an equivalent explainer? 60 seconds? You should be spending less time than that reading it.

I was excited for a moment, and wondered why the RSS feed didn't work, but later realized that the article is from 2024.

The author of the Airfoil article has made several similar tutorials on various topics:

https://ciechanow.ski/archives/

For machine learning, Distill.pub has some excellent hands-on tutorials. For example, here's one on momentum:

https://distill.pub/2017/momentum/

https://explorabl.es collects various

Umm no, at zero degrees AoA as the first diagram on the page shows, a flat plate does not generate lift. But nobody actually questions that a flat shape can generate lift; we all made paper planes as a kid.

Was gonna say where is the debate of bernouli vs. AoA/pforce