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Using LLMs to build out the nice-to-haves that I’ve always wanted but never had time for is one of their great use cases. Visualizations are a perfect use case for this because they don’t have to be perfectly architected, maintainable code. Getting to the correct visual output is good enough, and LLMs excel at iterating something until it looks right.

I've been using LLMs to create visualizations for math papers I come across. Prompting "Create a visualization for each segment of this article in the style of a 3 brown 1 blue video using manim." has yielded impressive results.

It helps me digest the content faster and allows me to read more articles than I otherwise would.

LLMs to create and revise PIL (python image library) commands/params have saved me HOURs.

Regarding the changes to your grading weights: https://acbart.github.io/2026/04/19/proctored-grades/

Sounds like 50/50 for the distribution? That means you are okay with a student getting a 40% across all your quizzes and then passing the class with a C-?

This is v cool.

When I did my microcontroller class with lecturer hand drawing an 8-bit computer, the registers, memory, instructions on the white board, it was v cool to understand how things worked under the hood.

Wondered if someone could make more simulations for what was being taught. Teaching is about deciphering a thing into it's components and seeing how they interact. Vibe coded simulations are a great tool for that.

Before LLM there has already been Fields medalist[0] who creates professional software[1].

[0]: https://en.wikipedia.org/wiki/Martin_Hairer

[1]: https://www.hairersoft.com/

This is a very humbling thought, thank you.

I'm waiting for the reverse, coding agents asking Terry Tao if the proof they plan working on is worthy of a Fields Medal

> 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?

I don't understand what trust means in this context. Even if I were able to hire Donald Knuth to write all my code, I wouldn't "trust" it to be bug-free, let alone to be the right fit for my needs.

> 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

Indeed. LLMs produce truly atrocious code, unmaintainable and unreliable. If you're vibecoding a toy to amuse yourself or something similar low-stakes, that's perfectly fine! For higher-stakes code, it's definitely not.

I liked this article about an old recipe book and what cooking could have looked like if we took microwave cooking seriously: https://malmesbury.substack.com/p/my-journey-to-the-microwav...

Ferran Adrià published a recipe for an omelette using potato chips.

here you go, Michelin-starred chef:

> Marco Pierre White passionately defends chefs using microwaves. White dubbed microwaves “sensational things” and revealed he thinks they’re far better at preparing kippers than any other technique, like boiling or grilling

https://www.independent.co.uk/life-style/marco-pierre-white-...

And another one:

> José Andrés, a renowned Michelin-starred chef, New York Times bestselling author and internationally recognized humanitarian. He listed the microwave omelet as his number one foolproof dish and called it the “best fluffy omelet in the history of mankind!”

https://www.tasteofhome.com/article/jose-andres-microwave-om...

A more accurate analogy is Charles and Henry Greene using tech to construct an intricate rig to fasten the joints in a delicate jewelry box to fit inside the Gamble House. Yes, they could make the rig by hand, but time is a precious resource to people with so much to build.

What Tao and other artists of his caliber are demonstrating is that the tech is capable of building the rig. And the machine makers are incrementally demonstrating that the machine can make not only the jewelry box rig, but rigs to build rig-making machines.

This makes me curious.

Are there any documented essays or reactions from the great chefs of back in the day reacting to the first microwave dinners?

i'd imagine when microwaves first came out chefs were genuinely excited? it's pretty insanely magical to observe ... at first.

I wouldn't be surprised if that was actually more common than one might think

People are so confident that this just-a-tool will hit its limits any day now...

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

Nov 2025: https://terrytao.wordpress.com/tag/artificial-intelligence/

https://academy.openai.com/public/blogs/terence-tao-ai-is-re...

What makes this a hobby project? He’s a university professor so developing teaching material is part of his job.

Having started using Claude Code at work, I think coding as we now know it will probably no longer be a career path in 5 years at the outside.

I’m old. If I had to, I could retire tomorrow, albeit on a restricted budget. But I worry about the younger folks (like my 25-year-old nephew) who haven’t built up the resources to survive without working who are in the field right now. There’s going to be a mega disruption and writing code is going to go the way of calculating square roots by hand or hot metal typesetting. There will still people doing it, but it will very much be a niche endeavor.

> always a hobby project, never something serious.

I don’t know what you’re reading, but always and never are strong words. I’ll predict by this time next year you’ll have seen some pretty serious AI uses, and can no longer say always/never. Widespread use of AI coding is brand new, and the models only just barely got good enough to do serious things. It’s way too early to be using words like always and never, but FWIW I’ve already seen some serious uses. There are good reasons personal blog posts rarely talk about ‘serious’ production code; it may be against organizational policy, it may involve code that isn’t’ public, it may reveal proprietary information, and more…

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.

That is how it starts, trust is built on hobby projects.

What do you mean not serious? He’s developing visual aids to teach students and to accompany his mathematical research papers. Also, not in this post, he’s been actively using LLMs to do real math research, that is, to prove theorems and solve problems.

Teaching, research and publication are the core activities of his job as a math professor. How does it get more serious than this?

Serious things tend to be long and tedious and potentially full of proprietary information.

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.

i would take this every single time over some Claude rewrite slop

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.

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.

Or he just finds it an incredible time-saving tool to help him do more maths.