There’s even a framework already: https://start.solidjs.com/

Feels a bit like the AI browser that in the end imported Servo for all difficult things.

The way I see it, this is only possible because you can trust the lower layers work reliably and predictably enough that you can move up.

If your operating system was regenerated every day slightly differently and with certain things working and others not, you’d quickly revert to the lower predictable abstraction.

> it spends the 6 hours waiting for the tickets to go on sale and buys them for you

Or not, because other 7 billion agents were also waiting for it.

I guess the thing here (which they admit in the post) is that they’re just porting it to Vite, which is the real champ of the story. The LLM basically worked as a translator instead of rebuilding the whole thing from scratch.

So maybe the project is sort of maintainable, as long as people maintain Vite.

Actually, most VPN providers explicitly label the virtual locations as such, I think the famous ones at least do it (ex: Proton and NordVPN even explain them in their respective docs).

It depends on whether the VPN is lying to you. Proton, for example, makes them quite explicit in the software and even lists them for you here: https://protonvpn.com/support/how-smart-routing-works and seems like NordVPN also has a page explaining that.

Yeah, Proton is quite explicit about that: https://protonvpn.com/support/how-smart-routing-works

I have this impression that LLMs are so complicated and entangled (in comparison to previous machine learning models) that they’re just too difficult to tune all around.

What I mean is, it seems they try to tune them to a few certain things, that will make them worse on a thousand other things they’re not paying attention to.

Anything that is very specific has the same problem, because LLMs can’t have the same representation of all topics in the training. It doesn’t have to be too niche, just specific enough for it to start to fabricate it.

One of these days I had a doubt about something related to how pointers work in Swift and I tried discussing with ChatGPT (don’t remember exactly what, but it was purely intellectual curiosity). It gave me a lot of explanations that seemed correct, but being skeptical and started pushing it for ways to confirm what it was saying and eventually realized it was all bullshit.

This kind of thing makes me basically wary of using LLMs for anything that isn’t brainstorming, because anything that requires knowing information that isn’t easily/plentifully found online will likely be incorrect or have sprinkles of incorrect all over the explanations.