Dran

That’s reasonable; I just wouldn’t have called my wife’s laptop my laptop I guess. It was either that or there was probably an interesting story behind it.

How many laptops do you own lol?

I had an opposing shower thought the other day so I’m going to play devil’s advocate on this one.

I think in a world of rational, good-faith actors (which I’m not arguing we live in), this is both by-design, and optimal at society scale.

Think about those things you’re good at, and the things you’re not so good at. I’m really good with computers, my time is most efficiently spent troubleshooting and building technology stacks. This skillset is in demand enough that I make a comfortable living doing it.

I’m comfortable enough that I have time to learn other skills when needed, but not comfortable enough to hire out all the otherwise commodity tasks I need done. A leak in the roof, a sink that needs replacing, some cat6 through the walls, leveling a floor before replacing broken tile from the 80’s… You get the idea. I can do drywall and other general contractor work but I’m not great at it. It takes me longer to end up with a worse end product than a professional, and I don’t enjoy doing it.

Every Saturday I spend doing drywall could, at society-scale, be much more efficiently spent building a k8s cluster or helping a scientist build software for research. Just like the guy doing my drywall should have a me on the other end of a phone when he needs a new laptop, or his mother gets malware.

When people hit “rich” the unspoken meaning is supposed to be that their time is valuable enough that society deems it more useful to spend it outside of commodity tasks. That seems like a good fundamental design… say what you will about its current real-world implementation.

I’m with you that he doesn’t strictly need a gpu, but if the price is right (free from old gaming PC, cheap from a friend’s old gaming PC, cheap old workstation card, etc) I stand by that he probably wants one. A lot less fussy, a lot more capable, nad nvenc does better quality encoding at lower bitrates (and probably less power too if you take into account time spent encoding at full tilt.)

Generally power supplies are the most electrically efficient at 20-60% utilization, so there’s no issue with over-provisioning power, other than the (generally minor) upfront extra cost, which might very well pay for itself in the first months/years of usage. I’ll take a look and see what I can find on those sites.

Edit: okay, trying to shop through google translate / currency calculator is actually aids so I’m gonna teach a man to fish instead. This is what I should have done from the start anyway.

Power supply: Anything from a decent brand, at basically anything >450W. a 650W or 850W is totally fine if it’s at a decent price. They only draw the power they need, they don’t just constantly pull 850W if the downstream components aren’t calling for it.

CPU: 12400 is a fine cpu for what you’re doing. You’ll transcode at 720p no problem, 1080p maybe a single stream in real-time. I wouldn’t bank on more than that. Only downsides here are the relatively shallow core counts if you ever expanded into other workloads. Without access to used xeon boards/cpus, it might be a reasonable choice though. What I would say is look for something older but with more cores/threads if you can. For example, a 10900 or even 10700k would probably be a better server cpu than a 12400.

Memory: DDR4 platforms are a great way to save money, as long as you aren’t planning on expanding to inferencing on cpu. Get as much as you can. 32-64gb of ddr4 should be dirt cheap, especially if you find a cheap motherboard with 4 memory sockets.

Motherboard: If you want this thing to be versatile, you want 2x pci-e slots. Old gaming full-sized ATX boards are the way to go here. 1 slot for an HBA, 1 slot for a GPU, and that should be all you need. Bonus for as many open sata sockets as possible. 6-8 is pretty typical on 10th-12th gen gaming ATX boards.

GPU: gpus will be much more efficient at transcoding than an igpu, especially from older intel CPUs. A 1050, 2060, 3050, basically anything from the 10-series onward has a decent nvenc encoder that would work well with plex/jellyfin. My goto is generally old workstation cards, I use a p620 myself and it handles a single 4k encode job no problem. I’m not sure if they’re viably purchasable anywhere in your area, but I’d definitely look out for a P620, P1000, or T400. Great value in those cards.

Drives/HBA: there are inexpensive LSI HBA cards to expand how many drives you can attach to a system if you need them, all you need is a spare pci-e slot and a place to physically mount the drives. The cheapest way to start here is to look for a motherboard with 4-6 sata slots and use those. Hardware raid is functionally dead these days in the real world, just use zfs or mdadm under linux to create an array with your desired level of resiliency/capacity.

Once you’ve priced out what it would cost to buy all of this new, look for prebuilt gaming PCs and office PCs that might be able to be expanded to fit these requirements. Prices look kind of steep on those markets you listed, but I’m sure something exists if you look hard enough.

agree in principal, but in practice:

1. parents who live across the state

2. plexamp for music

They are indeed just that keen on our data.

They know they can’t get rid of it for all of their customers, but they do want to make it as hard as possible for random users to do so.

The problem with this is it doesn’t work for home users that want to pay for their software. Crazy… I know… but those people do exist.

It’s a little deeper than that, a lot of advertising works on engagement -based heuristics. Today, most people would call it “AI” but it’s fundamentally just a reinforcement learning network that trains itself constantly on user interactions. It’s difficult-to-impossible to determine why input X is associated with output Y, but we can measure in aggregate how subtle changes propagate across engagement metrics.

It is absolutely truthful to say we don’t know how a modern reinforcement learning network got to the state it’s in today, because transactions on the network usually aren’t journaled, just periodically snapshot for A/B testing.

To be clear, that’s not an excuse for undesirable heuristic behavior. Somebody somewhere made the choice to do this, and they should be liable for the output of their code.

Fail2ban and containers can be tricky, because under the hood, you’ll often have container policies automatically inserting themselves above host policies in iptables. The docker documentation has a good write-up on how to solve it for their implementation

https://docs.docker.com/engine/network/packet-filtering-firewalls/

For your usecase specifically: If you’re using VMs only, you could run it within any VM that is exposing traffic, but for containers you’ll have to run fail2ban on the host itself. I’m not sure how LXC handles this, but I assume it’s probably similar to docker.

The simplest solution would be to just put something between your hypervisor and the Internet physically (a raspberry-pi-based firewall, etc)