tirutiru

Aah thanks for explaining that. I was wondering how a fake key could possibly help.

I heard about St. John's from a twitter thread and find it deeply baffling.

It's as if a group of monks wanted to keep the quadrivium and trivium but their clock stopped at the 16th century. One of their faculty proudly said they study analysis by reading Descartes! Which I thought was a highbrow joke but nope, dead serious.

There's a reason that 'standing on the shoulders of giants' is a thing. Dive into the classics after you have gained the maturity from modern texts.

Take one of the easier problems from Rudin. Prove that a continuous function from the unit interval [0,1] to itself has a fixed point. I wonder how a student immersed in the 'classics' would even begin to tackle this.

The boundary lines of the convex hull can easily have 3 points each. So not as obvious as all that.

The Appolonius circle is a theorem in pure geometry. It is used in the 'method of images' in physics. And not just for ancient textbook problems either. I saw a paper in experimental fluid mechanics that builds up the intuition with images (2D incompressible flow). Appolonius live and kicking in the 21st century.

The Kochen-Specker theorem is interesting (if not useful). The proofs have a similar flavour to Sylvester. One tries to make a set of projections 'compatible' and it turns out to be impossible. Maybe there's a deeper connection.

I work on vehicle routing for a living. Our routing engine is mostly original heuristics, no MIP.

The thrust of your remarks is correct IMO. We are forced to support a large number of granular constraints, too large for my comfort.

But no solver will deal with large 'global events'. That's just not its job. Different levels of decision making altogether.

My email is in my profile. Would be happy to hear more.

How did you straighten it out?

I am drowning in gating propagating semantic mismatches...

I wrote a logistics optimization program (think travelling-salesman++) in Python and it gradually took over from the v1 version written in Java.

People generally compare "speed of execution" to "speed of coding".

I'll add "speed of troubleshooting". When something goes wrong and the clock is ticking, you need tools to identify quickly if the data is inconsistent, a config set wrongly or the algorithm stuck at a local optimum. And then decide if things can be mitigated with a patch or we should we grovel and buy time from the client... All of this is much easier in Python (bar the grovelling).

LLMs are changing things though. I recently ported a complex part of the kernel to C++. 90% of it was creating the scaffolding before the actual algorithms were ported. Very tedious work but Claude can just chug through. Wouldn't like to maintain it just yet.

I did not. Post-conditioning by your comment and the other one,I can see some signs such attempting to be unusually comprehensive. The 'atoms in your liver' could be an awkward human trying to be poetic about scales.

I still don't see idiomatic markers of AI so that's scary if your claim is correct.

I can roughly guess the train of thought and I am a bit surprised that Claude is failing you.

That said, I am puzzled at the algorithms that Claude & GPT "get" and ones that they do not.

(former physicist here. would love to know the kind of things you're working on. email on my profile)

I wonder how many completely u related applications have that interval check logic coded up somewhere. I'm pretty sure I wrote one for my work codebase. Would I bet my life that the < and <=s are correct? Nope.

Hey I am trying to get back into German. What are some of the good channels that you've found? I remember Fokus Deutsch.

Spent 4 years in Germany but never took a course out of sheer laziness. I can read children's books but every native speaker sounds like they are zipping along at 2x speed.