In the UK I had a panel interview with 4 people, in person. But for international students who often brought their own funding sponsored by their government that didn’t happen.

That's an interesting wrinkle. If anything, externally funded international students seem like exactly the case where you'd still want the interview, since the funding answers "who pays for this?" but not "does this person have the background and understanding to do the work?"

I recently tried to get Claude to use Codegraph in a repo rather than using grep/find all the time but I found it didn't follow instructions a lot of the time. I tried putting in a pre-tool call hook and explciitly blocking find/grep, and instead rather than using Codegraph like it was told, it started using Python to find/search instead.

I have a hard time to divert “rm” to “trash” too

Na, was normal in the UK to have A4 too

I think you misread, the fact that the UK is in the Anglosphere strengthens the GP's point

> Growing up in Australia, every computer I’ve ever owned has defaulted to “US Letter” as the paper size.

I'm not sure how; the UK is an Anglosphere country that uses A4 and desktop machines as far back as I can remember (mid 90s) also correctly defaulted to A4 when printing. I suspect it's more likely to be that there's been English (UK) and English (US) options in OS's for a long time and Aus got defaulted to English (US) rather than English (UK) which carried some settings that weren't correct by default.

I thought you were going to say about "bob" being a slang word for a poo.

Turns out that's a particularly East Midlands thing!

This is one of those when you go "Huh, this is amazing!" or "Huh, I thought this trick was really well known!" depending on your background ;)

Here's a similar impl from 1980 written in Fortran that also computes other properties like centroid: https://calgo.acm.org/550.zip Algorithm 550: Solid Polyhedron Measures A. M. Messner and G. Q. Taylor ACM Trans. Math. Softw., 6(1), Mar 1980, pp.121--130 Keywords: polyhedron, graphics, numerical integration Language: Fortran 66/77; Shar Index: Z; Gams: P File size: 19.1 KB;

But Messner published it first in: A. M. Messner, "A surface Integral method for computer calculation of mass properties", Paper No. 852, 29TH ANNUAL CONF. OF THE SOCIETY OF AERONAUTICAL WEIGHT ENGINEERS, Washington, D.C., May 1970.

I think

This is one variant of the 3-d analog of the "shoelace formula" for area:

https://en.wikipedia.org/wiki/Shoelace_formula#Generalizatio...

The 2-d version is from the 18th century. I'd expect the 3-d version was probably known in the 19th century, but I haven't searched for a reference.

This technique should be straightforwardly adaptable to compute arbitrary moments, not just the centroid.

If you have a scalar-valued function that you can conveniently express as the divergence of any closed-form function, you can integrate it like this. And you can generalize beyond scalar-valued functions and beyond Euclidean space using the generalized Stokes’ theorem.

You can even do this in real life: if you want to integrate the electric current density through a surface (that is, measure the total current crossing the surface), you can integrate its anti-curl (is that a word?) around the boundary of that surface, which is what a current transformer or a clamp-on current meter does.

I bet there’s a hydraulic or pneumatic analog as well, but a nontrivial example isn’t immediately coming to mind.

The hydraulic analog is that you can weigh a volume of water (which is the same as computing its volume) by adding up the forces on the surface surrounding the water.

This looks like it requires a dot product with the normal vector for each triangle, but you can expand it into the same form as the article.

As an aside, this seems related to the Holographic Principle? It states that the content of a volume is encoded in its surface

https://en.wikipedia.org/wiki/Holographic_principle

CUDA used to target POWER9 architecture but it was so niche that you ended up compiling from source literally everything since no wheels were available for it. EasyBuild and Spack were full of half broken recipes for the magic incantations necessary to build fundamental packages for HPC like TensorFlow, etc.

The big question is whether companies hold enough proprietary data to do useful things that for e.g. Anthropic, etc. can't easily replicate.

For some very niche cases I think this is probably the case but for the vast majority, the company's data isn't as useful as they think it is or anywhere near the size needed.

One of the big things to think about is whether local LLMs will be things companies want to deploy.

If you think of for e.g. some proprietary piece of software that wants to embed an LLM they've fine tuned or trained, they will want to make back some of their research cost right. So they are not going to want to put this on-device even if the hardware is there, unless there's some way of locking it down. I suspect we'll need on-hardware validation/verification and a way of preventing extraction of weights for this move to happen for many use cases.

> One of the big things to think about is whether local LLMs will be things companies want to deploy.

Non-tech enterprise was already doing this years ago.

Regulatory reasons, privacy reasons, security, etc. They want on-prem and total ownership of the data. Sometimes air-gapped.

Honestly, I've seen a lot less of this than I'd have expected. Lots of discussion, plenty of experiments or toy implementations at the level of individual teams (often driven by one or two enthusiastic individuals), a few proofs of concept for internal services at a more strategic level, but pretty much nothing beyond that.

This is in the UK where there's currently a big focus on data sovereignty in general, and I'm genuinely surprised by how little that's spilled over into demand for inference sovereignty (so far).

I still expect demand to grow substantially, but I've been saying that for the past couple of years and am beginning to wonder if there'll need to be some sort of trigger event before it happens (eg. the datacentre bubble bursting, or some sort of major scandal).

What is the performance like for symbolic maths?

I always preferred Mathematica for that to SymPy since it's baked in to the language much more. It was also way way faster than SymPy was for anything interesting.

Performance is pretty good for most stuff by now. However, there are functions which not optimized yet or implemented in a very basic way and if you use them in non-trivial ways the performance might be quite slow. We're happy about any reports of such issues at https://github.com/ad-si/Woxi/issues!