I've said this before, but it's interesting to see momentum go back and forth between the flexibility and ease of everyday language, and the formal rigor of programming languages.
It feels like we are still discovering the optimal operating range on a spectrum between these two domains. Perhaps the optimal range will depend on the specific field in question.
There was an interesting article in the Atlantic recently where a journalist spent a year participating in sports gambling. Part of the article discusses the effect it had on his psychology towards participants he had put his money on.
Octave has not implemented all of Matlab's functionality. You can see a list of Matlab functions that have not yet been implemented in Octave at the link below. It's a long list.
It was a very unpleasant feeling when I graduated from my PhD and realized that most, if not all, of the Matlab scripts I had used for my research would now be useless to me unless I joined a company or national laboratory that paid for licenses with the specific toolboxes I had used.
I'm glad that a significant portion of tools in my current field are in open source languages such as Python and Julia. It widens access to other researchers who can then build upon it.
(And yes, I'm aware of Octave. It does not have the capabilities of Matlab in the areas that I worked in, and was not able to run all of my PhD scripts. I have not tried RunMat yet, but am looking forward to experimenting with it.)
One of my students recently came to me with an interesting dilemma. His sister had written (without AI tools) an essay for another class, and her teacher told her that an "AI detection tool" had classified it as having been written by AI with "100% confidence". He was going to give her a zero on the assignment.
Putting aside the ludicrous confidence score, the student's question was: how could his sister convince the teacher she had actually written the essay herself? My only suggestion was for her to ask the teacher to sit down with her and have a 30-60 minute oral discussion on the essay so she could demonstrate she in fact knew the material. It's a dilemma that an increasing number of honest students will face, unfortunately.
Buzz Aldrin (who was the second person to walk on the moon) wrote his MIT thesis on methods for astronauts to handle the complexities of orbital dynamics when performing rendezvous maneuvers in orbit:
Very curious to see what usage limits are like for paid plans. Anthropic was already experiencing issues with high-volume model usage for Pro and Max users. I hope their infrastructure is able to adequately support running these additional coding environments on top of model inference.
Just to be clear, I'm excited for the capability to use Claude Code entirely within the browser. However, I've heard reports of Max users experiencing throttled usage limits in recent months, and am concerned as to whether this will exacerbate that issue or not.
Platinum Medal: Complete a gold medal task in the presence of multiple children between the ages of 3 and 8. The children must remain safe throughout (and after) the entire process.
Science involves both deductive and inductive reasoning. I would in turn argue that mathematics is a science that focuses heavily (but not entirely) on deductive reasoning.
I use DevPod [1] to run Claude Code within Docker-based dev container environments, and it's been fairly seamless. It lets me run environments locally and focus Claude's context on just the repo I want it to work on.
This looks nice. I wasn't aware of Acorn--how much adoption does it have in the mathematics community (or formal methods / robotics / other communities)? I feel like most are rallying around Lean.
To me, the most exciting aspect of teaching mathematics using Lean is the immediate feedback. If a student's proof is wrong, it simply won't compile.
Previously, the only feedback students could receive would be from another human such as a TA, instructor, or other knowledgeable expert. Now they can receive rapid feedback from the Lean compiler.
In the future I hope there is an option for more instructive feedback from Lean's compiler in the spirit of how the Rust compiler offers suggestions to correct code. (This would probably require the use of dedicated LLMs though.)
It feels like we are still discovering the optimal operating range on a spectrum between these two domains. Perhaps the optimal range will depend on the specific field in question.