I think I’ll trust owasp and my own over 20 years of experience building commercial software but you do you

This generally goes against security best practices as it can be used for attempted user enumeration. A better version would be “we’ll send you an email with your account status if this user exists” but obviously that results in a fair amount more complexity (and cost) to implement

Obviously you need lots of GPUs to run large deep learning models. I don’t see how that’s a fault of the developers and researchers, it’s just a fact of this technology.

In deep learning generally open source doesn’t include actual training or inference code. Rather it means they publish the model weights and parameters (necessary to run it locally/on your own hardware) and publish academic papers explaining how the model was trained. I’m sure Stallman disagrees but from the standpoint of deep learning research DeepSeek definitely qualifies as an “open source model”

Ughhh that was my fear. Haven’t built a desktop in probably 20 years. I definitely worry about the time sink mostly in deciding every component, researching if it’ll work with linux, sourcing it, hoping it’s authentic, etc. Any recent guides you could recommend if I have to go down that route?

https://rtyley.github.io/bfg-repo-cleaner/

Also revoke the key of course

Not OP but I’ve noticed a lot of racists, extreme right nationalists, religious chauvinists, etc. Which sucks because the concept is quite cool and I’d love to be able to jump from one question to the next, but pretty quickly I get irritated and have to close it.

Can you explain more? Don’t leave me hanging…

(((Truth)))

Thanks for all your hard work!

https://arewereorganizedyet.com/ lol already updated

Ahhh is that it? For whatever reason firefox mobile seems to not respect my general android setting and it won’t seem to turn off. But in any case, my apologies to the webmaster.

What in the everloving fuck kind of color scheme is this unreadable nonsense?

It’s the part of ruby that replaced perl. For whatever eldritch horror perl was it was very, very good at doing text manipulation, and IME the only language to really match that experience was ruby.

I don’t get the downvotes. I’ve hired probably 30+ engineers over the last 5 or so years, and have been writing code professionally for over 20, and I fully agree with your sentiment.

Did you just post your open ai api key on the internet?

In addition to iroh there is https://github.com/earthstar-project/meadowcap-js and other projects under https://github.com/earthstar-project. See https://willowprotocol.org/more/projects-and-communities/index.html#projects_and_communities

How does lemmy federation work in this case? Conceivably after being restored from backup the lemmy.ml instance could see those few hours of lost history as federated to other lemmy instances and resync it back as the host instance. Obv I’m vastly oversimplifiying things but what happens today?

Re thumb-key do you have recommended tutorials for getting comfortable with it? I found trying to do touch typing tutorials didn’t really help, both because they are generally made for desktop environments and they are geared towards qwerty layout (e.g., get comfortable with home row first etc). I tried forcing myself to use it for a full 24 hours as the concept makes a ton of sense to me, but got very frustrated with myself and then dug into the world of which layout to choose, got overwhelmed, and switched back to whatever this qwerty layout that samsung one ui provides on galaxys.

I think that is overly simplistic. Embeddings used for LLMs do definitely include a concept of what things mean and the relationship of things to other things.

E.g., compare the embeddings of Paris, Athens, and London to other cities and they will have small cosine distance between them. Compare France, Greece, and England and same. Then very interestingly, look at Paris - France, Athens - Greece, London - England and you’ll find the resulting vectors all align (fundamentally the vector operation seems to account for the relationship “is the capital of”). Then go a step further, compare those vector to Paris - US, Athens - US, London - Canada. You’ll see the previous set are not aligned with these nearly as much but these are aligned with each other (relationship being something like “is a smaller city in this countrry, named after a famous city in some other country”)

The way attention works there is a whole bunch of semantic meaning baked into embeddings, and by comparing embeddings you can get to pragmatic meaning as well.