The autobahn is mostly kept around due to lobbying. There have been repeated attempts to introduce a maximum speed (130 km/h), the last one failed because “paying for that many signs would be too expensive” iirc.

But its also quite a bit safer than you might think. They’re very good/well built roads, most people actually stick to the recommended 130, and (and I think this might be the largest factor) German driver’s ed is very good (and hideously expensive).

I recently started going to a course related to law at university. Its aimed at people not studying law, and the first thing it started with was clarifying that law is not morality.

This was quite interesting to me, since the course’s topic (laws about digitalization) has nothing to do with morality. So law != morality is obviously something the courses organisers thought to be a very important thing about law.

So I think the professionals are well aware of it, and anyone using law as a morality is either deliberately choosing it to ignore problems they’d have in a proper moral system, or not well educated on law.

I use Matrix with a group of friends that I persuaded to move from Discord. We still use Discord’s voice chat, but now that Matrix is starting to have something similar, I might try to get them to use Matrix for that too. So, the usage here is just general groupchat-like stuff.

I’m also in the process of moving the student council I’m in from Discord to Matrix. Once that’s up and running, it’ll cover groupchats, coordinating and announcing events, channels/rooms for various courses, and discussing/organising the various things the student council does.

Corridor Crew (a small VFX team with a long history on YouTube) have made a couple of videos on identifying AI generated video. Start with this one for example.

The points that I can remember are:

1. AI tools can’t generate more than 10-20 seconds, if you see an unbroken shot longer than that, its not AI.
2. Check for consistency between shots, this is pretty hard to do with AI. E.g. if the same person keeps appearing with similar, but still different clothes, its probably AI.
3. Visual artifacts, especially in the background can be a good tell.

This is only partially true.

RSA public keys consist of two parts:

1. The exponent e, which is usually 65537, for the reasons you described.
2. The modulus n, the product of the two primes used for generating an RSA keypair.

If the public key were always completely the same, then there would be no point in making different private keys.

Technically not one book, but uhh… one/two book series: Mistborn by Brandon Sanderson.

You’re question made me think of rainbow codes, the naming scheme for British secret military projects during part of the cold war.

I couldn’t find the wordlist they used (if there ever was an official one), but if you used a system like that (with two words instead of one), then you might even get away with making your own wordlist by hand.

Wait, that’s “faking it”?

Well shit, that’s how like … 80% of my hobbies started. The rest are from other people who pulled me in.

How do normal people get hobbies?

I’ve found this one: https://codeberg.org/polyphony/slopscore

By its own metrics, it was made without AI (and the source is on Codeberg, which iirc has strict rules about AI code).

You should be able to just fork it normally, then reset (or is it revert?) to the commit you want.

Fluid sims sound like an excellent thing for you, since (in Blender) they’re entirely CPU bound anyways.

I can recommend trying to simulate explosions. Its pretty easy to get into, once you figure out how to get particles to be smoke/fire sources, but it has the opportunity for endless tinkering trying to make it look better (or just different). The only issue is having to wait up to an hour or longer for a sim to finish (I just watch movies or TV shows while it runs in the background).

Also, if you haven’t done it yet: Do the donut tutorial! I haven’t seen the latest version, but given that the trend looked like it kept improving with each iteration, it’s probably still amazing. It teaches the basics of pretty much every system Blender has to offer.

And also, remember, you don’t need to know how something works to try things out! You can just mess with settings and see what comes out. That’s pretty much how I started (after the donut though, so I guess I knew a little). Try things out, don’t worry about making “good” things, just make something.

Probably?

Its more likely to depend on your GPU.

I’ve got a laptop with a 10th gen i7, 16 GB RAM and an RTX 3070 (the laptop version, so less power than a proper one).

For modelling, sculpting, …, even geometry nodes, your setup should be just fine. For rendering, if you don’t have a dedicated GPU, just stick to EVEE. CPU rendering with Cycles is an option, if you need raytracing, but it will be slow.

I mostly use Cycles, because with denoising its pretty much realtime for me. But from what I’ve heard, EVEE is pretty close to Cycles nowadays anyways. I guess the main thing you lose is free reflections everywhere.

Also, I’m honored you found my stuff good quality, I don’t really consider myself particularly good at this.

I occasionally play around with Blender a bit, so I went to see if I’ve got anything interesting to show, and found I rarely actually end up rendering stuff out. So then I went through my Blender files, and rendered some stuff out. So, in no particular order:

• This was a sort of “bat signal” like thing. It’s projected onto volumetric clouds, which was way harder than I thought it would be:

• I honestly forgot about this one:

• This was an attempt at precisely replicating a physical object. Its a drone motor, but I didn’t end up finishing all the parts:

• Another replica, this time a ham radio dummy load (that’s a resistor with fairly precise impedance, used for testing). I don’t have the font anymore (and can’t be bothered to fix it), so the text is gone:

• Blender nearly froze trying to render this, don’t know why:

• Apparently, thin film shaders don’t end up working like thin film filters would in real life, and Blender gets annoyed at light sources with 1 GW power (I’m not quite sure what I was trying to do here, so I can’t really say if this experiment was a success or not):

ammo is much harder to manufacture

I’m not sure if that’s true. I don’t have any experience with it myself, but lead is notoriously easy to cast at home, because of its low melting point.

So it seems to me, all it takes to make some (shitty) ammo is some lead, a mould (which you can make from clay) and an explosive.

For a proper modern gun, that last piece is probably pretty difficult, and you would need to make casings somehow too (I’m not sure how difficult that would be). But if you were using some form of (possibly homemade) flintlock or similar, the gunpowder from fireworks should be enough.

I think regulating both guns and ammo is the way to go.

This is actually something that a turing machine is suitable for.

Get youself something to remember what state you’re in, write the rules on a sheet of paper and use a stack of paper as the tape, with each piece of paper being one cell.

Turing’s idea was to build a mathematical model that mirrored how humans go about large, complex tasks.

Honestly, all of these super basic mathematical models for calculation are fairly well suited to being “run” by hand.

Random-access-machines can be done similarly to turing machines. Write the program down, keep the program counter on one piece of paper, and have a (numbered) stack of paper as memory.

And lambda-calculus is probably the easiest. Just write out the expression, and then write out each reduction step. This is what functional programming languages build on, and with the additional effort of checking types, you could probabl evaluate something like Haskell by hand pretty well.

All of those are turing complete (if you ignore infinite memory).

EDIT: spelling

I think there are three main issues (though I might be overlooking something):

1. AI taking over peoples work is as much an issue for mathematicians here, as it is for programers, artists, writers, etc. elsewhere.
2. We still need to check if these 700 papers are correct (AI makes mistakes at least as often as humans, usually more often). This involves a huge amount of effort. And if it turns out that they contain a bunch of mistakes, OpenAI is unlikely to care. To them, these papers have already served their purpose (generating headlines).
3. Solving a problem in mathematics means being able to (formally) convince other people that your opinion is correct (massively simplified, but imo that is what mathematics boils down to), by getting them to understand why your opinion is correct. Having a pile of linear algebra spit out 700 texts about certain math problems removes the whole “human understanding” part of that.

However, it’s not entirely unprecedented for a computer to provide a proof that we don’t completely understand. I forgot the details (and I’ll edit them in if I can find it), but there was at least one problem that was solved using a computer and brute force trying millions of cases. That produces a proof much longer than any human could read in a lifetime, and iirc there where quite a few mathematicians unhappy about it at the time too.

After a closer look at it, I think it might be a different version/some kind of derivative of sitelen mun. Some parts don’t make sense, in particular the tiny circle crossing the small circle, for example here

However, it does contain the (more or less) correct syntax for names (multiple different ones). For example here

which I think reads “jan Loulou” or “jan Luoluo”.

So I think its close enough to sitelen mun to be based on it, but its definetly something different.

Is this using sitelen mun? I’ve always wondered what that would look like for more complex sentences.

Except… sitelen mun doesn’t merge circles like that, I think. So is this another way of writing Gallifreyan, or is this from somewhere in Doctor Who?

Debian is the OS.

Proxmox builds on Debian to turn it into a hypervisor. That’s an OS for managing VMs and containers. Proxmox (like most hypervisors) gives you a handy webinterface.

There are easier hypervisors to use, but Proxmox is great for learning, and allows you to expand well later.

tbh, except for gaming and AI, you don’t need nearly as much processing power as you might think. I run most of my stuff on a (fairly new, can’t remember which one exactly) i5, and it sits at an average 2-3% CPU usage, with the maximum its recorded being 10.9% (there’s two VMs and 8 containers running on it). 11th gen i5 will probably be more than enough to start with.

In my non-professional opinion, what you really want to look at is how much power your system is going to use when idle, since that’s what (small) servers are doing most of the time. This is not something specified by manufacturers, and it depends on all the parts going into the computer (CPU, mainboard, PSU, fans, how the BIOS is set up, …).

I would also recommend getting at least 16 GB of RAM (honestly I would say 32, if not for the current prices). Containers don’t use too much, but if you want to set up VMs for services, each VM will want at least 2-4 GB for itself.

LXC-Containers (https://en.wikipedia.org/wiki/LXC) are like extremely lightweight and slightly less secure Linux VMs. They are similar to Docker, but while Docker is containerizing applications, LXC containerizes Linux distros.

Running Docker inside a VM is the recommended way to run Docker on Proxmox.

Personally, I just run Docker inside LXC containers. Aparently, this is supposed to be not entirely stable, but its never given me any problems. (I commit an entirely different sin against efficiency, by keeping each service in its own LXC container, each with its own Docker host.)

I also run Immich (in Docker inside a container), and haven’t noticed any performance issues. But I’m the only user, and I don’t use it heavily, so ymmv.