DE version is available. Content is displayed in original English for accuracy.
Advertisement
Advertisement
⥠Community Insights
Discussion Sentiment
79% Positive
Analyzed from 1842 words in the discussion.
Trending Topics
#more#using#trust#https#always#don#may#math#serious#llms
Discussion (74 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/
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/
"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 âtrustâ while warning against anthropomorphizing the AI by using unchecked output. ;) Generally speaking, âtrustâ 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 donât know the difference between nails and screws. (This may be where my analogy breaks down. :P) But I feel like saying donât trust it isnât 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 âgenerally not to be trustedâ implicitly suggests not using AI, and doesnât 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 doesnât, 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 Taoâs statements about LLMs for mathematical proofs as an example of how advanced and autonomous these systems already are
Are there any documented essays or reactions from the great chefs of back in the day reacting to the first microwave dinners?
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 donât know what youâre reading, but always and never are strong words. Iâll predict by this time next year youâll 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. Itâs way too early to be using words like always and never, but FWIW Iâve already seen some serious uses. There are good reasons personal blog posts rarely talk about âseriousâ production code; it may be against organizational policy, it may involve code that isnâtâ public, it may reveal proprietary information, and moreâŚ
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).
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".
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.
https://www.reddit.com/r/mathematics/comments/1tryyw7/terenc...
Every time.