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nn3

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There are many examples in anquity. Greek had a flowering in the Bronze age, then a dark age, before classical Greek took over. The Hittites had a major disaster including the destruction of their capital before they recovered in a great way (until their final demise at the end of the Bronze age). Egypt had several dark periods where they recovered from, until they fell prey to Greek and Roman imperialism.

Sizing chaos 5 months ago

What's also annoying is that sizes of the same clothes change. I have a pair of jeans that I ordered on Amazon in 2020. It happens to fit me great. So recently I decided to order two more of the same. I got exactly the same model with the same size on Amazon, just with different colors. But neither fit very well, they were far wider. The first one had such a horrible fit that I immediately sent it back. The other I can wear, but it's quite different from the other perfectly fitting one. Why are they doing that? It's insane.

I don't feel his overflow miscompilation example is a good one. A 64bit multiplication converted back to 32bit has the same overflow behavior as if the computation was in 32bit (assuming nobody depends on the overflow indication, which is rare). And in high level programming languages you typically can't tell the difference.

chimpanzees, gorillas, orangutans evolved intelligence too. They are smarter than most other critters in the jungle. Just all not as much as the lineage that leads to humans.

It's actually quite difficult to define human intelligence. Every time we think we find something unique by humans eventually some animal turns up that can do it too. It may be all just a question of degree and how it's used.

If you would spin the whole structure you couldn't have multiple shells all with 1G on their surface. The required spin speed for 1G depends on the diameter. But their whole concept is built around multiple shells, which is clear from the name.

Regarding the GDP needed once you have a working "mine from the moon and send to orbit" economy it doesn't seem to be too bad. The assumption would be that a lot of technology is already developed for other projects. Launching it all from earth obviously wouldn't be possible even with vastly cheaper launch. That's why they put the build into the moon-earth L1 lagrange point to be easily reachable from the moon.

For propulsion and reactors, but there are multiple projects today working on all of this. Building a life support system for 400 years is still an unsolved problem however.

I suspect for most cloud providers you deleting data is cheaper because the data is not charged by the byte. But then they like having data, maybe just to train their AI models or for bragging rights to their investors.

For the expiration dates most modern file systems have the concept of arbitrary extended attributes per file. It's quite easy to add meta data like this yourself.

It is hard to say what the effect will be:

If it's true that these indirect costs only contain expenses closely related to the actual research then they can just move to the actual grant with somewhat more accounting overhead. I suspect the universities are doing that accounting already for their internal purposes so it won't be that big a change.

But if it's true that a significant part of them are not related there would need to be significant changes in budgets, and whoever benefits will have a problem.

I suspect the truth is somewhere inbetween. In any case it's a good opportunity for these organizations to figure out how to become more cost effective.

Secondly, if you think any compiler is meaningfully doing anything optimal >>("whole program analysis") on a TU scale greater than say ~50kloc (ie ~10 files) >relative to compiling individually you're dreaming.

That's wrong. gcc generates summaries of function properties and propagate those up and down the call tree, which for LTO is then build in a distributed way. It does much more than mere inlining, but even advanced analysis like points to analysis.

https://gcc.gnu.org/onlinedocs/gccint/IPA.html https://gcc.gnu.org/onlinedocs/gccint/IPA-passes.html

It scales to millions of lines of code because it's partioned.

Lagos can't use starlink very much because starlink has limited capacity in any given area. The future sats might improve that a bit with more sats and more capacity per sat, and also there will be more non starlink constellations, but it's an inherent problem. If they targeted any densely populated area they would vastly over-provision the rest of the more sparsely populated world.

So in general it's good news for the rural population (if they can afford it), but it doesn't really help too much for the cities.

Just for comparison the LOCs for some other small C or C like compilers. It's not that far away from Ritchie's

C4x86 | 0.6K (very close)

small C (x86) | 3.1K

Ritchie's earliest struct compiler | 2.3K

v7 Unix C compiler | 10.2K

chibicc | 8.4K

Biederman's romcc | 25.0K

Usually it happens anyways, just delayed. Old people usually don't want to move, but the younger people are more flexible and move away as soon as they can if they don't see any future.

Then these towns get older and older until eventually they won't be viable anymore (or find something else to still attract young people).

So the trends are happening, they are just delayed by a couple of decades. As the average life expectation grows the cycles get longer however.

BCD is a requirement for many financial calculations, which these machines were targeted for (e.g. running payroll).

Since the hardware was extremely limited they could not afford to support both binary and decimal, so everything ended up being decimal. This was actually not an uncommon choice on some of the early computers. For example the ENIAC was decimal too, although that one wasn't designed for business.

There used to be a lot more experimentation in early computers before everything became standardized. The Russians even did some early ternary computers, there was a lot of variety in word length (36bit systems), the 8-bit byte wasn't even invented yet.

Another thought: it would be interesting to know if it's a fundamental problem (full tenure track for every researcher/teacher needed cannot be financed at all in the current system), or if the number would work out if the funding for all the administrative bloat was shifted to teaching/research.

I suspect the fundamental truth of PhD production far exceeding job market in the academia won't go away though.

the real trick is to do the debugging/exploration on a small subset of the data. Then usually you don't need all these extra measures because the real processing is only done a small number of times.