It’s a bit of a stretch calling it a plastic, as it’s not petroleum based from what I’ve read.

It might, possibly, be a viable use case if the LLM produced the summary for an editor, who then confirmed it’s veracity and appropriateness to the article and posted it themselves.

Could you let me know what sort of models you’re using? Everything I’ve tried has basically been so bad it was quicker and more reliable to to the job myself. Most of the models can barely write boilerplate code accurately and securely, let alone anything even moderately complex.

I’ve tried to get them to analyse code too, and that’s hit and miss at best, even with small programs. I’d have no faith at all that they could handle anything larger; the answers they give would be confident and wrong, which is easy to spot with something small, but much harder to catch with a large, multi process system spread over a network. It’s hard enough for humans, who have actual context, understanding and domain knowledge, to do it well, and I’ve, personally, not seen any evidence that an LLM (which is what I’m assuming you’re referring to) could do anywhere near as well. I don’t doubt that they flag some issues, but without a comprehensive, human, review of the system architecture, implementation and code, you can’t be sure what they’ve missed, and if you’re going to do that anyway, you’ve done the job yourself!

Having said that, I’ve no doubt that things will improve, programming languages have well defined syntaxes and so they should be some of the easiest types of text for an LLM to parse and build a context from. If that can be combined with enough domain knowledge, a description of the deployment environment and a model that’s actually trained for and tuned for code analysis and security auditing, it might be possible to get similar results to humans.

I’m unlikely to do a full code audit, unless something about it doesn’t pass the ‘sniff test’. I will often go over the main code flows, the issue tracker, mailing lists and comments, positive or negative, from users on other forums.

I mean, if you’re not doing that, what are you doing, just installing it and using it??!? Where’s the fun in that? (I mean this at least semi seriously, you learn a lot about the software you’re running if you put in some effort to learn about it)

‘AI’ as we currently know it, is terrible at this sort of task. It’s not capable of understanding the flow of the code in any meaningful way, and tends to raise entirely spurious issues (see the problems the curl author has with being overwhealmed for example). It also wont spot actually malicious code that’s been included with any sort of care, nor would it find intentional behaviour that would be harmful or counterproductive in the particular scenario you want to use the program.

A valid point, trackers often give you a certain amount of upload credit for free, and there are often other ways to earn those credits too, so all users’ ratios would be above 1.0, but that should have read “A closed group of users can all have a seed ratio of 1.0” if we’re looking at just the data transfer itself.

A closed group of users can all have a seed ratio above 1.0, but it’s a bit of a contrived set up. For simplicity, in the following examples we assume that each file is the same size, but this also works for other combinations.

Consider the smallest group, two users. If user A seeds a file and user B downloads it, whilst B seeds a different file, which A downloads, both users will have a ratio of 1.0 as they’ve up and down loaded the same amount.

For three users, A seeds a file, B and C then download a different half each, which they then share with each other. A has a total (upload, download) of (1,0), whilst B and C have (0.5,1). If you repeat this with B seeding and A and C downloading, then C seeding to A and B, you get each peer uploading 2 files worth of data, and downloading 2 files worth, for a ratio of 1.0 each.

You can keep adding peers and keep the ratios balanced, so it is possible for all the users on a private tracker to have a 1.0 ratio, but it’s very unlikely to work out like that in real life, which is why you have other ways to boost your ratio.

The longer I stare at this, the wronger it gets. It’s like a magic eye picture made of nonsense.

From the rest of the article I very much got the ‘crazy people send crazy message’ message, but I can see how that might depend on the reader.

You say the letter is meaningless, but I think we have to be careful. Sesame Street teaches kindness, inclusivity and caring, and I think the republicans would be only too happy for an excuse to tear it down.

I’ve read the NYT article, and I can’t see anywhere where the author ‘sincerely considers the idea that Rachel Griffin-Accurso, the popular children’s entertainer known as Ms. Rachel, might be financially compensated by Hamas.’ Instead they report that ‘the advocacy group StopAntisemitism’ ‘sent a letter urging Attorney General Pam Bondi to investigate whether Accurso is receiving funding to further Hamas’s agenda.’

The article as a whole seems pretty positive towards Miss Rachel, and uses her comments to point out how bad things are in Gaza, and insinuates that StopAntisemitism are the problematic ones.

Clearly fake, it says there’s a tty number in the top left corner and there isn’t.

Perfect, he gets to make a big fuss for the racists, but doesn’t actually have to change anything. That way there’s less risk of breaking anything, and he’s probably looking for a win right now.

It’s a bit like decreeing that all English week day names must henceforth end in the letter ‘y’. It’s low risk, makes certain people think you’re busy taking action, and leaves everyone else wondering what you’re up to with this, taking some of the heat off of other issues.

I’m sure you’ve already considered it, but from that description it sounds very much like make. That compares the input files’ timestamps to the output files’ timestamps, so it might be different to your plan though.

You… you don’t? Surely there’s some mistake, have you checked down the back of your cupboard? Sometimes they fall down there. Where else do you keep your internet?

Appologies, I’m tired and that made more sense in my head.

On the other hand, the enemy of my enemy is my friend

Maxim 29: The enemy of my enemy is my enemy’s enemy. No more. No less.

That doesn’t mean you can’t work with them on a common cause, and maybe even show them the benefits of your way if thinking, but don’t get caught out when they turn on you.

Before you can decide on how to do this, you’re going to have to make a few choices:

Authentication and Access

Theres two main ways to expose a git repo, HTTPS or SSH, and they both have pros and cons here:

• HTTPS A standard sort of protocol to proxy, but you’ll need to make sure you set up authentication on the proxy properly so that only only thise who should have access can get it. The git client will need to store a username and password to talk to the server or you’ll have to enter them on every request. gitweb is a CGI that provides a basic, but useful, web interface.

• SSH Simpler to set up, and authentication is a solved problem. Proxying it isn’t hard, just forward the port to any of the backend servers, which avoids decrypting on the proxy. You will want to use the same hostkey on all the servers though, or SSH will refuse to connect. Doesn’t require any special setup.

Replication

Git is a distributed version control system, so you could replicate it at that level, alternatively you could use a replicated file system, or a simple file based replication. Each has it’s own trade-offs.

• Git replication Using git pull to replicate between repositories is probably going to be your most reliable option, as it’s the job git was built for, and doesn’t rely on messing with it’s underlying files directly. The one caveat is that, if you push to different servers in quick suscession you may cause a merge confict, which would break your replication. The cleanest way to deal with that is to have the load balancer send all requests to server1 if it’s up, and only switch to the next server if all the prior ones are down. That way writes will alk be going to the same place. Then set up replication in loop, with server2 pulling from server1, server3 pulling from server2, and so on up to server1 pulling from server5. With frequent pulls changes that are commited to server1 will quickly replicate to all the other servers. This would effectively be a shared nothing solution as none of the servers are sharing resources, which would make it easier to geigraphically separate them. The load balancer could be replaced by a CNAME record in DNS, with a daemon that updates it to point to the correct server.

• Replicated filesystem Git stores its data in a fairly simple file structure, so placing that on a replicated filesystem such as GlusterFS or Ceph would mean multiple servers could use the same data. From experience, this sort of thing is great when it’s working, but can be fragile and break in unexpected ways. You don’t want to be up at 2am trying to fix a file replication issue if you can avoid it.

• File replication. This is similar to the git replication option, in that you have to be very aware of the risk of conflicts. A similar strategy would probably work, but I’m not sure it brings you any advantages.

I think my prefered solution would be to have SSH access to the git servers and to set up pull based replication on a fairly fast schedule (where fast is relative to how frequently you push changes). You mention having a VPS as obe of the servers, so you might want to push changes to that rather than have be able to connect to your internal network.

A useful property of git is that, if the server is missing changesets you can just push them again. So if a server goes down before your last push gets replicated, you can just push again once the system has switched to the new server. Once the first server comes back online it’ll naturally get any changesets it’s missing and effectively ‘heal’.

I manage all my homelab infra stuff via ansible and run services via kubenetes. All the ansible playbooks are in git, so I can roll back if I screw something up, and I test it on a sacrificial VM first when I can. Running services in kubenetes means I can spin up new instances and test them before putting them live.

Working like that makes it all a lot more relaxing as I can be confident in my changes, and back them out if I still get it wrong.

That’s just crazy talk. If we don’t listen to the billionaires the line might not keep going up quite so fast. For the purposes of this argument, please ignore TSLA, the climatologists obviously got to that one.

Assembly language is not something you would ever really program a game in.

Back then you wrote whatever you needed to be performant and/or that involved close access to the hardware in assembler. A game would definitely count. It’s kind of nice to do, in many ways it’s simpler than high level programming, you’ve just got a lot more to keep track of.

Faggots can also be meatballs, you can walk in to a supermarket and buy some faggots, or make them yourself.

The use to mean “a bundle of sticks” is definitely more rare now-a-days though, you’re correct.