I recall my father (also a mathematician, incidentally) often repeating this to me.

So technically possible but also a tall ask (I didn’t know at the time of asking and my PI went with it).

I then came back and carried on without any immigration issues.

Meanwhile, median net salary in CH is 5'000-5'500 per month, double to triple its neighbors. So food is actually very affordable.

The food that costs more is the one someone cooked for you, which is logical considering the cook is likely paid more than your engineer (assuming that's your case) salary. But then again, minimum wage Italians are not eating out at the restaurant with any frequency. If you were an engineer in Switzerland instead, you could afford eating out there. The restaurants and terraces are never empty, anyways.

Now, if you want to enjoy a beer in the sun, you can get a 2CHF can at the supermarket and go fire up a barbecue at the lake of Zurich, I see people doing that all the time.

If that's the case, it affects earnings quite a bit. Say your investments beat inflation by 3 percentage points, you're effectively down to 2 percentage points after tax, so a 33% reduction in income.

Then there's everything to do with children (from direct subsidies to public schools or kindergarten slots), education (not everyone goes to university, for instance), and other subsidies that are income-dependent (two common ones in France are rent subsidy and a salary top-up for low-but-not-too-low incomes).

Plus, ultimately, 100% of what comes in goes out (modulo administrative costs) at the global scale, so you can't just average this or everywhere looks the same.

I did my PhD in France where we were legally employees like any other and did 100% research with like 100 hours training over the three years which could be 5min MOOCs counting for hours or classes the professors would sign us off on. We were hired by a specific researcher for a specific topic, unlike US students who join a broader program and explore their own directions more. My mentoring was drinking coffee with my advisor and colleagues and the odd e-mail exchange the day before turning in a paper.

I believe Germany and quite a few other European countries are similar. Any country that does 3 years PhDs is bound to cut on the student part of things.

So this article is really not saying anything controversial in the strictly ontological side of things, in fact it's already a relatively common stance to prefer supervising few (or, more rarely, none at all) students.

This researcher is saying "when I consider hiring someone as a workhorse, I might prefer AI instead"; what's the harm in that? Too many PhD students are used as disposable cheap labor, seeing little personal growth in their PhD journey and being generally neglected and abused.

When a PC was expected to boot to an OS and not much else, we had all the freedom - by necessity - to tinker and learn. Hardware was barely enough for most day-to-day usage, so we upgraded relatively frequently and got to know the physical innards as well.

This is all so streamlined today that even computers can be smartphones with "apps", or even just a browser that gets you to google slides and everything else (or the MS equivalents). It was probably a necessity that, as computers became infrastructure, they would become simplified, so 90% of the population can indeed file their tax return online (and the remaining 10% have their younger family members do it).

This also means that people nowadays simply don't know that they can walk into any second hand store and get a $200 PC with a warranty that'll be much more productive than any smartphone if they have the knowledge to use it properly. But was there really a loss? These are, for the most part, people that would not have been able to hop on the internet wagon if it'd relied on maintaining a linux distro at all. That's regarding adults; children now do indeed grow up with walled systems for the most part, and that might be a loss.

That's definitely valuable, but not for a child in my opinion, it's the type of luxury equivalent to a Mercedes over a Renault. Perfectly defensible but, just like a Mercedes is hardly a starter car, I don't think an MBP is that fit for a starter PC. It's also mostly useless if you're not traveling for work regularly.

That said, does any of that even matter any more? People were learning Blender, programming and whatever else 15 years ago on low to mid range machines already. The equivalently priced - or dirt cheap second hand - machines of today are multiple times more capable at everything. Stick Linux and a $5 mouse in it and you're 90% of the way to a macbook pro in terms of user experience.

That's to say, I agree with the core of the article: kids will make the most out of the least. But I disagree that this particular laptop is a necessity or a boon for that. If anything, it's a hindrance for being a mac.

Theoretically, each of those steps is parallelizable to some extent. Amdahl's law equivalent here would be that some delays are outside the reach of an organization to improve. For instance, a building permit will take the time it takes to be examined based on an external public administration.

I'll admit I'm not very familiar with that type of work - I'm in the forward solve business - but if assumptions are made on the sensor noise distribution, couldn't those be inferred by more generic models? I realize I'm talking about adding a loop on top of an inverse problem loop, which is two steps away (just stuffing a forward solve in a loop is already not very common due to cost and engineering difficulty).

Or better yet, one could probably "primal-adjoint" this and just solve at once for physical parameters and noise model, too. They're but two differentiable things in the way of a loss function.

What do you mean by that? Simulating physics is a rich field, which incidentally was one of the main drivers of parallel/super computing before AI came along.

However you highlight what I think is the way forward: using scriptable CAD that can leverage LLMs or, maybe in the future, specialized generative algorithms that output in a sane geometry specification.

> AI models generate meshes using "isosurface extraction" or similar volume-to-mesh techniques

This creates the "lumpiness", the inability to capture sharp or flat features, and the over-refinement. Noisy surface is also harder to clean up. How do you define what's a feature and what's noise when there's no ground truth beyond the mesh itself?

Implicit surface methods are expensive (versus if-everything-goes-right of the parametric alternative), but they have the advantage of being robust and simple to implement with much fewer moving parts. So it's a pragmatic choice, why not.

3D generative algorithms might become much better once they can rely on parametric surfaces. Then you can do things like symmetry, flatness, curvature that makes sense, much more naturally. And the mesh generation on top will produce very clean meshes, if it succeeds. That is a crucial missing piece: CAD to mesh is hardly robust with human-generated CAD, so I can't imagine what it'd be with AI-generated CAD. An interesting challenge to be sure.

I'm particularly intrigued by your mention of keeping old code around. This is something I haven't found a solution for using git yet; I don't want to pollute the monorepo with "routine_old()"s but, at the same time, I'd like to keep track of why things changed (could be a benchmark).