Nope. Just need to know an official language. English only is fine.

Most Canadians know fuck all French, just for the record. It’s only really spoken in two provinces.

Completely correct.

Been using Seafile for years, it’s fantastic.

Oh hell yes! Awesome update.

I’m curious about what the client would be in this context. Does it require a desktop environment, or just an X server? Are you using an existing web browser, or are you rendering web content directly in the app?

I’ve been using it for years and this has never been an issue for me.

Yeah, hi, um… Fuckface McParasite… You have literally zero fucking say over what people do in their home you fucking moron. If that means your precious fucking lightswitches wear out faster, too bad, so sad.

Absolutely there’s a difference. LLMs, when it comes to this specific task, are better. That’s why they’re being used here. It’s a job they are uniquely well suited to. They do indeed come with high hardware requirements, which is why you’re not forced to use them, and why they provide the option to offload the work to a cloud service.

Personally I would absolutely not want to ever feed my documents into an off-device model, but the point of self-hosted software is that it does what you tell it to and they absolutely should include letting you make bad decisions.

I think you’re under the impression that the difference between those things is far greater than it actually is. Large Language Models work by developing statistical maps of associations. That’s discrimination. They’re a direct evolution of categorization models. The ability to associate a hash of a JPEG with “cat” is the same as the ability to associate “How are you?” with “Great, how about you?” It’s all associative mapping. LLMs are just the current leading edge of that technology. If you want to, for example, generate a list of tags that describe a document, an LLM is the best tool we currently have for doing that.

To put it another way, what you term “discriminative AI” is “generative AI.” It’s generating a category or list of categories in response to an input. That’s not functionally different than generating a sentence in response to a sentence, it’s just an order of magnitude less complex. You can argue terminology but the technology exists on an evolutionary curve, with no real hard boundaries.

Paperless and Papermerge have always done a lot more than just OCR. If that’s all they were, most people can do that already on the software that comes with their scanner. The core selling point of these applications is automatic categorization, sorting and tagging, and those have always relied on machine learning tools. Literally the first thing you do after setting Paperless up is start training the AI.

what’s the best alternative?

You’re asking for suggestions for an automatic document categorization tool that doesn’t use any automatic document categorization?

It reads as especially hysterical in this context, because Paperless is an automatic document categorization system, and I’m really sure what they think the automatic part of that is if it’s not “AI” of some broad description. Paperless, Papermerge et al are basically wrappers for machine learning tools and have been for as long as they’ve existed. LLMs are a natural and obvious fit for the kind of work these applications exist to do.

This just feels like someone reading “Improved AI pathfinding” in the patch notes for a video game and screaming “OH MY GOD IS NOWHERE SAFE?!”

Modern routers have a feature that’s typically known as “hairpin NAT” or “NAT loopback”. This means that if the DNS lookup resolves to the router’s public IP, it routes the packet internally instead of passing it out to the internet. So for your subdomains all of the traffic should stay local. Typically this feature is enabled by default, but you may want to check just to be sure.

“Imagine a terrible burger…”

Interesting. If I’m following this correctly, the architecture is very similar to how services like Tailscale and Zerotier work; every client contacts a central server to say where it can be reached, and then the central server shares that information with authorized parties, but the actual communication is all peer to peer.

Assuming I’ve grasped this correctly, this sounds like a very smart use of a proven architecture. I guess my main question would be, how does the central server determine when to share contact info between two clients? Is everyone essentially aware of everyone else (on the backend at least) and then some client side component restricts who can actually communicate, or is there some pre-shared secret that would authorize the server to connect your client to someone else’s?

Edit to add: The other thing I was going to note is that obviously removing the central server means there’s no way to retrieve your messages if you lose or wipe your phone, but I see you’ve already included robust backup options. Thumbs up for that.

Edit 2: Just to clarify my question, I see that theres a system for scanning or importing QR codes in order to add someone as a contact (I do want to suggest that you should also be able to use a code; not everyone always wants to be sending an image in place of, effectively, a phone number), but my question is more about how this is architectured on the backend. Does the key encoded in the QR code authorize the TURN server to connect you with the other user?

It’s cool, I bought one for you.

French press usually, sometimes a pour over or a moka pot.

But no routine. I switched to decaf a few years back after I got tired of needing coffee instead of enjoying it. Now I make a brew whenever I feel like it. With no caffeine, that can be first thing in the morning or last thing at night, doesn’t matter. Some days I’ll have four cups, some whole weeks I’ll have none.

But can that which was lost ever be saved?

I love the hell out of Cena’s Peacemaker performance, but if you forced me to pick between those two it’s gonna be Big Dave.