Many visualizations that I have always wanted but just didn't have the time to build, I now have.
To give an example, I wanted a simplified 8-bit computer to complement the 16-bit teaching computer I use and designed this in a few days with the help of claude:
Terry Tao using coding agents to build apps means we're one step away from a Fields Medalist asking an LLM why his Docker container won't start, just like the rest of us.
larme 2 hours ago [-]
Before LLM there has already been Fields medalist[0] who creates professional software[1].
I'm waiting for the reverse, coding agents asking Terry Tao if the proof they plan working on is worthy of a Fields Medal
semiquaver 16 minutes ago [-]
There is infinite latent demand for software, most especially outside the traditionally software-focused spaces. If LLMs stopped improving today it would take us 10 years to catch up to the new software-writing abilities that have become available. This is a great illustration of that fact.
wffurr 3 hours ago [-]
Nice balanced perspective there at the end:
"as such [LLM-coded interactive] supplements are not mission-critical to the core of the paper, I again feel that the downside risk of using guided interaction with LLM agents to generate such visualizations is acceptable."
It's a tool. Good for some things but not others and generally not to be trusted.
dahart 9 minutes ago [-]
> It’s a tool. Good for some things but not for others and generally not to be trusted.
I agree completely you always need to check the work of LLM agents, but it does strike me as a tiny bit funny to anthropomorphize AI by using ‘trust’ while warning against anthropomorphizing the AI by using unchecked output. ;) Generally speaking, “trust” in AI has been going up very quickly as the models & harnesses improve, and as people figure out effective workflows.
I trust my hammer with nails but not screws… does that mean the hammer should generally not be trusted? The problem with AI is we don’t know the difference between nails and screws. (This may be where my analogy breaks down. :P) But I feel like saying don’t trust it isn’t as helpful as saying something like you should expect to spend more time planning and iterating than before, and you should expect tot spend more time reviewing and checking output than before, and learn how to use skills and context and subagents, and learn to use AI on some non-production low-consequence projects first. Saying ‘generally not to be trusted’ implicitly suggests not using AI, and doesn’t leave the reader with how to use AI. The goal is to build trust by building good workflows and by understanding what works well and what doesn’t, right?
an0malous 1 hours ago [-]
> and generally not to be trusted
There are many AI bulls who adamantly disagree and cite Tao’s statements about LLMs for mathematical proofs as an example of how advanced and autonomous these systems already are
irishcoffee 34 minutes ago [-]
Statistical gradient descent token vomiter. We can all say it together. Nothing about this is advanced or autonomous.
CuriouslyC 10 minutes ago [-]
This is like saying humans are a self contained electron transport system, nothing special or advanced about that, just a scaled up nematode.
mxkopy 23 minutes ago [-]
I mean just from the above quote it’s clear he doesn’t trust them for “mission-critical” tasks. And I doubt LLms have evolved significantly from their stochastic parrot nature over the last few years
apignotti 13 minutes ago [-]
Running legacy educational Java applets, especially around math and physics, has been a longstanding popular use case of our CheerpJ Applet Runner extension, running Java bytecode in the browser via WebAssembly.
I am not sure how to feel about agents solving the problem via proper modernization. It's certainly positive that students will be able to interact with this content in a modern and more accessible way, but the educational use case for our product, although not commercially important, has always been a source of pride.
Terry Tao using coding agents feels like watching a Michelin-starred chef discover microwave dinners and get genuinely excited about them.
r_lee 3 minutes ago [-]
I wouldn't be surprised if that was actually more common than one might think
thejazzman 5 minutes ago [-]
i'd imagine when microwaves first came out chefs were genuinely excited? it's pretty insanely magical to observe ... at first.
alansaber 2 hours ago [-]
I always enjoy these "domain expert has fun using AI to do something in their domain" articles. But it's always a hobby project, never something serious.
rsfern 2 hours ago [-]
Terry Tao has actually been one of the more prominent voices in the math community exploring AI for cutting edge mathematical discovery. This particular post is a bit softer but he has also written a lot about using AI assistance for serious core research
It sounds more like a project that suits the tool.
The measured argument for these things all along is that this novel technology is uniquely capable, but not universally so. The phase we're in, collectively, is all about finding uses that fit it well and charting the boundaries of those that don't.
One of the most natural fits is for modest or supplementary efforts where the of imperfections and noise it introduces are irrelevant. But something being modest or supplementary doesn't relegate it to "hobby" status -- like a workshop jig, it can make all kinds of difference in how quickly and how well you reach your "serious" end.
jebarker 2 hours ago [-]
What makes this a hobby project? He’s a university professor so developing teaching material is part of his job.
indymike 16 minutes ago [-]
That is how it starts, trust is built on hobby projects.
cma 2 hours ago [-]
But he's also using AI for formally verified math and for ideas in solving math problems. The part about it being ok because it is a supplement just means ok that these aren't formally verified and may have bugs, and may also mean ok to not credit the AI for the paper as it is just a visual supplement and not the main work.
dboreham 2 hours ago [-]
Serious things tend to be long and tedious and potentially full of proprietary information.
vatsachak 2 hours ago [-]
Using LLMs to generate dashboards is probably their most productive use case
koe123 2 hours ago [-]
I am far from a mathematician but I am excited by the possibilities of using AI for generating more math. Math in my mind exists purely in the world of forms, and cannot be appropriated for profit, but is downstream to everything else. I am keen to see what this enables.
lioeters 41 minutes ago [-]
It may be a question of perspective, but in my mind mathematics is upstream to everything else, including physics, biology, etc. And it doesn't just exist in the human mind or the "world of forms", as in Platon's realm of ideas. It's more fundamental than that, closer to the foundation from which all existence emerges. Our reality is like a shadow of a shadow, a fleeting illusion, compared to the eternal reality that gives birth to all lesser realities.
As for profit, there's a reason why governments and AI companies are hiring philosophers and mathematicians. It's not to make the world a better place for everyone, or to encourage the progress of human knowledge; but to gain cutting-edge advantages over their competitors. Same reason why theoretical physicists were prized before/during the Second World War.
muragekibicho 3 hours ago [-]
The article's awkward opening statement proves it wasn't written by AI.
I have been interested in machine-assisted ways to do and teach mathematics from as far back as 1999, when I started coding several applets in Java 1.0, both for my complex analysis and linear algebra courses, to visualize various mathematical objects I was interested in (such as honeycombs or Besicovitch sets).
r_lee 42 seconds ago [-]
i would take this every single time over some Claude rewrite slop
kccqzy 2 hours ago [-]
It’s very much Terrence Tao style. His style is having long sentences that could have been broken down into shorter sentences but he chose not to. It doesn’t really affect reading comprehension.
jdw64 2 hours ago [-]
His website using mathematical knowledge is refreshing. There's a small UI bug, but personally, I wish more educational materials were this rich in audiovisual content.
onionsential 33 minutes ago [-]
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jgalt212 3 hours ago [-]
The more Terry talks about AI, the more I'm starting to feel like Terry may have some undisclosed conflicts of interest.
When it comes to coding, non-programmers do not have to be in a defensive position worried that their job is under risk, instead they just see a great tool that saves them time, especially doing boring coding like dashboards, visualizations, interactive web-pages, or doing experiments that they otherwise would not have time for.
Mathematicians are a kind of programmers, the original ones.
CuriouslyC 7 minutes ago [-]
Disagree. Programming is about sequences (behavior, state, data, etc), math is about relations.
lagrange77 2 hours ago [-]
Why are mathematicians a kind of programmers? Besides applied maths, aren't they more researchers that explore and discover, in contrast to the majority of programmers who are more like handymen?
lowsong 2 hours ago [-]
"When it comes to a field I'm not an expert in, AI is a great tool."
Every time.
azan_ 2 hours ago [-]
Tao is not an expert in math research? That's a really high bar then.
alansaber 2 hours ago [-]
Yes, because AI gets the "shape" of something right. If you don't know the field you don't notice the pockmarked surface.
lagrange77 2 hours ago [-]
I think the opposite is true.
wizzwizz4 2 hours ago [-]
So does anyone familiar with the Gell-Mann amnesia effect.
perching_aix 2 hours ago [-]
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hansmayer 3 hours ago [-]
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nmfisher 3 hours ago [-]
Or he just finds it an incredible time-saving tool to help him do more maths.
perching_aix 3 hours ago [-]
The well-known shadowy bias and conflict of interest of "I just enjoy experimenting with this new thing".
2 hours ago [-]
perching_aix 2 hours ago [-]
I do not think he's shilling; I think you misread the tone of my comment. Added an extra word now to maybe make the intent clearer.
That said, I do think "honeymoon phases" are a real source of bias. But then I don't think he's going through one of those either. He's been trying to leverage these models for a while now after all.
He might still be under a more general "tech adoption trend" bias, but at that point I'd say the lines become a bit blurry.
hansmayer 3 hours ago [-]
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suwapat 2 hours ago [-]
LLM will do very good job in pure mathematics since it don't need the senses to logically understand/conclude a given topic.
Rendered at 15:13:37 GMT+0000 (Coordinated Universal Time) with Vercel.
https://htmx.org/essays/universities-and-ai/#demos-visualiza...
Many visualizations that I have always wanted but just didn't have the time to build, I now have.
To give an example, I wanted a simplified 8-bit computer to complement the 16-bit teaching computer I use and designed this in a few days with the help of claude:
https://bdp.cs.montana.edu/
[0]: https://en.wikipedia.org/wiki/Martin_Hairer
[1]: https://www.hairersoft.com/
"as such [LLM-coded interactive] supplements are not mission-critical to the core of the paper, I again feel that the downside risk of using guided interaction with LLM agents to generate such visualizations is acceptable."
It's a tool. Good for some things but not others and generally not to be trusted.
I agree completely you always need to check the work of LLM agents, but it does strike me as a tiny bit funny to anthropomorphize AI by using ‘trust’ while warning against anthropomorphizing the AI by using unchecked output. ;) Generally speaking, “trust” in AI has been going up very quickly as the models & harnesses improve, and as people figure out effective workflows.
I trust my hammer with nails but not screws… does that mean the hammer should generally not be trusted? The problem with AI is we don’t know the difference between nails and screws. (This may be where my analogy breaks down. :P) But I feel like saying don’t trust it isn’t as helpful as saying something like you should expect to spend more time planning and iterating than before, and you should expect tot spend more time reviewing and checking output than before, and learn how to use skills and context and subagents, and learn to use AI on some non-production low-consequence projects first. Saying ‘generally not to be trusted’ implicitly suggests not using AI, and doesn’t leave the reader with how to use AI. The goal is to build trust by building good workflows and by understanding what works well and what doesn’t, right?
There are many AI bulls who adamantly disagree and cite Tao’s statements about LLMs for mathematical proofs as an example of how advanced and autonomous these systems already are
I am not sure how to feel about agents solving the problem via proper modernization. It's certainly positive that students will be able to interact with this content in a modern and more accessible way, but the educational use case for our product, although not commercially important, has always been a source of pride.
https://chromewebstore.google.com/detail/cheerpj-applet-runn...
Nov 2025: https://terrytao.wordpress.com/tag/artificial-intelligence/
https://academy.openai.com/public/blogs/terence-tao-ai-is-re...
It sounds more like a project that suits the tool.
The measured argument for these things all along is that this novel technology is uniquely capable, but not universally so. The phase we're in, collectively, is all about finding uses that fit it well and charting the boundaries of those that don't.
One of the most natural fits is for modest or supplementary efforts where the of imperfections and noise it introduces are irrelevant. But something being modest or supplementary doesn't relegate it to "hobby" status -- like a workshop jig, it can make all kinds of difference in how quickly and how well you reach your "serious" end.
As for profit, there's a reason why governments and AI companies are hiring philosophers and mathematicians. It's not to make the world a better place for everyone, or to encourage the progress of human knowledge; but to gain cutting-edge advantages over their competitors. Same reason why theoretical physicists were prized before/during the Second World War.
I have been interested in machine-assisted ways to do and teach mathematics from as far back as 1999, when I started coding several applets in Java 1.0, both for my complex analysis and linear algebra courses, to visualize various mathematical objects I was interested in (such as honeycombs or Besicovitch sets).
https://www.reddit.com/r/mathematics/comments/1tryyw7/terenc...
Every time.
That said, I do think "honeymoon phases" are a real source of bias. But then I don't think he's going through one of those either. He's been trying to leverage these models for a while now after all.
He might still be under a more general "tech adoption trend" bias, but at that point I'd say the lines become a bit blurry.