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Comment Re:Summary (Score 1) 169

He chose a really cumbersome way to go about it. If you want to make your own cloud, it makes sense to use cloud software like NextCloud or OwnCloud. Also, for like $10/mo. he could get a VPS and slap NextCloud on there. Another easy solution would be TrueNAS.

Solid recs.

Comment Re:AI coding (Score 1) 48

With modern ones it's taking into account all the previous text in the session and comparing that to the training data to generate an output, similar to how you might say a response to someone in Chinese

That's not at all similar to how people talk in Chinese. When humans speak Chinese, they understand what they are saying. If they say apple, they know what an apple tastes like.

This is about qualia, but qualia are just the beginning of the problems. LLMs are not like the human brain, and the researchers building them do not claim them to be.

Comment Re:Just like humans: How we train them. (Score 1) 48

It'll only do that when making predictions that lean on training samples that were secure.. In general it just makes code that look like what it was trained on, warts and all.

It's not clear that LLMs could write secure code, even if the training samples were all 100% secure.

Comment Summary (Score 5, Insightful) 169

His reasoning is "My self-built server was difficult to build and it didn't have all the features I wanted."

While that is true, it is a solvable problem, by making better software. He also added needless complexity to his project, for example, putting everything in Docker containers. There is also some question as to whether he needed 8 TB of hard drive space, but also I feel jealous of that, so if he wants it, go for it.

His solution is to make a public cloud, available from the library, with all the features and data available by API. This is not something the US government will be able to do, for various reasons. One party will try to censor it, and the other will try to destroy it (or vice versa, depending who implements it first).

In short, he came up with a solution that was not great, and proposed a solution that is not feasible.

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