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Out of curiosity what model did you use?

Do you have examples? It should only happen in case of overfitting, i.e. too many identical image for the same subject

I read it, and I read the messages from the devs. The communication issue I am trying to point is also highlighted in the comments: if the decision on merging a PR is uniquely dictated by financial benefits of IBM, ignoring the broader benefits of the community, the message is that red hat is looking for free labor and it is not really interested in anything else. Which is absolutely the case, as we all know, but writing it down after the recent events is another PR issue, as red hat justified controversial decisions on the lack of contributions from downstream.

The Italian dev tried to put it down as “we have to follow our service management processes that are messy, tedious and expensive” but he didn’t address the problems in the original message. The contributor himself felt like they asked his contribution just to reject it because of purely financial reasons without any additional details. It is a new PR incident

The Apparently is already patch on fedora… Just reporting other comments in this thread. But why do they accept contribution to centos of they don’t want patches that are not economically beneficial to the company? It is a pretty bad message written as this

I stopped recommending it. It is a pity, but there are alternatives

Why would they accept PR at all if they don’t have a robust testing process and approvals are dictated by customers needs?

The message as it is now to potential contributors is that their contribution in not welcome, unless it’s free labor that financially benefits only ibm.

Which is fair, but the message itself is a new PR issue for red hat

This is exactly the issue, shorter wave lengths can carry more data, but they are blocked by literally everything between the source and the antenna… Longer wave lengths carry less information, but at least they are more reliable and can pass through many obstacles. It’s a compromised at the end

The problem of current LLM implementations is that they learn from scratch, like taking a baby to a library and telling him “learn, I’ll wait out in the cafeteria”.

You need a lot of data to do so, just to learn how to write, gramma, styles, concepts, relationships without any guidance.

This strategy might change in the future, but the only solution we have now is to refine the model afterward, let’s say.

Tbf biases are integral part of literature and human artistic production. Eliminating biases means having “boring” texts. Which is fine for me, but a lot of people will complain that AI is dumb and boring

Yeah, first thing I noticed as well. Hilarious how the guy has no idea what he is talking about

But it is for wifi communication apparently. Unfortunately short wave lengths are absorbed more easily than longer wave lengths as the current radio/microwave solutions. That is the main physical limitations to overcome