chacham15

the machines arent optimized for it is why. the main driving factor is large unified memory which makes large(r) models possible, but there isnt the gpu horsepower to back it up. essentially, it fits the corner of the market that wants large models and is ok with running them slowly which doesnt sound like it would be a large market.

I really miss the prompting that A1111 had in concert with the grid. It was so easy to do a grids changing variables per axis. e.g. prompt on vertical and model on horizontal axis. is there a comfy node that does this? i've looked and havent found one.

> How do you decide what shared features a peacock and a palm tree have? At what scale should they be represented? How do you deal with invariance under affine transforms? Do you want to open the box of invariance under non-affine transforms?

The whole point is that the representation is learned. When you talk about various levels of abstraction, you're missing that all of these levels are representable with words and the relationships between them. That is verbatim what LLMs are optimized for. Interestingly, when you take an embedding, you do see that some transformations in embedding space actually hold which is quite interesting (e.g. tree + many ~ forest)

This article reads like more of an ad than anything else.

> Environment variables only deliver values

Yes, the problem .env files try to solve is having "environment variables" be injectable from a file so that different applications can have different environment variables by default.

> A string is not a schema

Yes, input validation is an application concern. The application should know what these values represent / how to parse them and error if they're invalid.

I could go on, but the article is all about trying to use a hammer as a screwdriver and complaining that the hammer doesnt work.

"AI is just a tool" is meant as a counterpoint to AI producing slop [1]. It isnt that there aren't ethical/societal implications with it as with everything we do in society.

[1] If you take the slop and manually fix/improve/verify it to production value it can be immensely valuable.

This is a shallow analysis. Banter doesnt need to be about a transgression or offensive at all. E.g. if you were talking to Armand Duplantis, you could tease him about how easy he makes breaking the world record look: "Hey Armand, you havent broken the world record in this past year...arent you due?"

There is a difference between a service and a good.

It doesnt make sense to "own" a massage just the same way it doesnt make sense to "own" spectating a game in person. The video recording of people playing a sport is a good that you can own however. This is why an online/multiplayer game is harder to separate because it straddles the line of both a service and a good, but other cases are much more clear cut. (also, a quick google does reveal multipke open source diablo projects fyi)

I think you're confusing two different axes. There is a difference between the cache state and the context state.

Imagine a conversation with turns X, Y, and Z. When the LLM "reasons" about the next token A it does: P(A | X,Y,Z) and then P(B | X,Y,Z,A), etc. It will eventually produce a result P(D | X,Y,Z,A,B,C). Instead of continuing the context from X,Y,Z,A,B,C it continues it from X,Y,Z so you have P(N | X,Y,Z,D). This is what is meant by dropping the reasoning. This is done to save cache context for the session.

This is a different thing than preserving the K/V state of P(N | X,Y,Z,D).

I hate these overly grand clickbaity statements. AI is a tool. You can use it well. You can use it poorly. "AI Slop" is the category of lazy AI tool usage. It is the same with AI code. Do you ask Claude to implement a feature and then not do a manual code review? If so, you're likely to get slop.

> The research findings “could present a challenge to those who argue that the AI model does not store or reproduce any copyright works,” said Cerys Wyn Davies, an intellectual property partner at law firm Pinsent Masons.

The defense to training with copyright is that it is the same as how humans learn from copyrighted material. The storage or reproduction is a red herring. Humans can also reproduce copyrighted works from memory as well. Showing that machines can reproduce copyrighted material is no different than saying that a human can reproduce copyright material that the human learned from.

The defense to actually reproducing a work is that in order to do so, the user has to "break" the system. It is the same as how you can make legal software do illegal things (e.g. screen recorder to "steal" a movie)

None of this is to say that these defenses are correct/moral; but rather that this article doesnt add any additional input into whether it is or isnt.

I am so confused at how this is supposed to work. If the code, running in whatever language, does any sort of transform with the key that it thinks it has, doesnt this break? E.g. OAuth 1 signatures, JWTs, HMACs...

Now that I think further, doesnt this also potentially break HTTP semantics? E.g. if the key is part of the payload, then a data.replace(fake_key, real_key) can change the Content Length without actually updating the Content-Length header, right?

Lastly, this still doesnt protect you from other sorts of malicious attacks (e.g. 'DROP TABLE Users;')...Right? This seems like a mitigation, but hardly enough to feel comfortable giving an LLM direct access to prod, no?

- Facebook was not first. Before it was friendster and myspace.

- Tiktok was not first. Before it was vine and youtube.

- Google was not first. Before it was yahoo and altavista.

Plenty of todays big companies were not the first in their area.

This only works when strangers = target customer because there is no way a stranger would have the understanding of the pain you are relieving for someone when they dont feel that pain. Therefore, it can be better read as "validate your ideas on your target customer" which is kind of obvious.