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#using#more#https#software#trust#llms#always#code#math#something
Discussion (96 Comments)Read Original on HackerNews
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/
It helps me digest the content faster and allows me to read more articles than I otherwise would.
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-?
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.
[0]: https://en.wikipedia.org/wiki/Martin_Hairer
[1]: https://www.hairersoft.com/
And then realizing they put together something that would have taken you a few days to do.
The supply of software is about to go way up, and that's going to massively impact demand unless every firm on earth is clamoring for more.
We're going to see if Jevons paradox holds true, or if wages get impacted drastically.
People always argue "well, Slack and Notion have distribution and the product isn't everything." Ok and? The person making it for themselves doesn't necessarily need distribution for it to be valuable. In fact, it's even more attractive that way.
Every time someone paid me to write software it was some combination of 1. not that interesting of a problem 2. no real utility i could see or touch, useful in some abstract way of making a number go up 3. involved a constant, painful maintenance burden 4. involved incident management of one kind or another 5. involved a long tail of details with no unifying principle other than a lot of implicit legacy constraints and stakeholders whose involvement waxed and waned with no seeming rhythm..
I'm a big fan of the new capability, it opens up new regimes of performance and correctness and capability for what I can achieve, that in turn grinds me up against math and theory that I had thus far been able to avoid, it's pushing me up the ambition ladder hard and that's a good thing.
But the change is a change in degree not in kind at least in the vibecode regime: it was always relatively fun and relatively easy to do one small program with modest requirements around defect rigor that had a big legible "oh cool!" surface that I didn't have to maintain. Fable doesn't seem any better than Opus at grinding detail work in the bowels of a compiler, but it sure can make an iPhone-scoped platform game with a bunch of bugs in it in a single shot?
If there's a job where you get paid for doing fun, high defect, "oh wow!" factor one-off software that you can immediately disavow any responsibility for? Fuck man, I should have had that job before Fable got that job.
It’s kind of like saying wedding photographers will be impacted because of the people posting on r/iPhoneography. Seems kind of silly doesn’t it.
Really bullish on LLMs expanding code development by a very large group of people who are really smart in some domain but could not get into 'coding'.
"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?
I trust a hammer to be able to hit a nail, without breaking. But if the hammer is old and the wood brittle, I don't trust it anymore.
Using it for anything else (screws) has nothing to do with trust, but using the wrong tool.
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
> 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...
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.
Are there any documented essays or reactions from the great chefs of back in the day reacting to the first microwave dinners?
But, no, it's not "any day now." The required size and structure of the ANN is to be determined.
/s
[1]https://sequoiacap.com/article/2026-this-is-agi/
https://github.com/bradfitz/koffer#der-verloren-koffe
Play online at https://bradfitz.github.io/koffer/js/
So neat seeing ~30 year old code come back alive.
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...
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.
Whether reviewing agentic code rather than writing it is a job he wants to do... Different question.
Its older people who can't or won't retool that are going to find that in the game of musical layoff chairs they won't have a chair left when the music stops (and some I've met haven't really internalized that they're even part of the game...)
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…
Teaching, research and publication are the core activities of his job as a math professor. How does it get more serious than this?
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).
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.
By famous I mean someone whose biography is in the training data. All models know a lot more about Terrance Tao than they know about me, when he's working on his projects do the models know they don't need to explain "Besicovitch sets".
Since the system prompt likely includes something about not insulting the user, does the LLM modify it's responses if it realizes it's talking to famous politician, like "dont mention the time $politician was cancelled".
https://www.reddit.com/r/mathematics/comments/1tryyw7/terenc...
Every time.