@movq I'm very curious...

What I like about this whole computer stuff is that you can explore how things work. You can dig through problems and solve them. Nothing is more satisfying than finally understanding something after you scratched your head for some hours.

Surely you could do the same with AI? Tinker with how it works, study it, understand it, build your own and realize what it really is (without all the big tech hype)?

@movq I think your points are pretty clear to me, that's fine. I'm just seeing if you can perhaps see things a different way maybe?🤔 I would challenge the assertion that you cannot understand how Claude Code generated an output; which I can demonstrate easily with a fairly trivial example by the input:

Write a program in Go that sums a list of numbers from stdin and prints the result.

Which it does so in seconds, faster than I can type. The code is correct, it compiles and does exactly what I wanted. And the code looks pretty reasonable. It handles flotas, has error handling and handles space or line separated numbers on stdin.

So going back to the understanding of how it generated this, is quite simply the most statistically relevant search space of it's weights it has been trianed on and it has basically just produced a series of tokens, one after another that are relevant to the input, the next token and so on. It's a trivial example I know, but it basically pattern matches it's way through it's vast search space just producing outputs based on context.

And every time I ask it to do the same thing, it produces basically the same result. It will sometimes not produce a go.mod, but that's probably because doing so isn't as statically high as writing the code to sum numbers from stdin.

@movq I'm kind of flag you bring thi sup, because you simply can't. You wouldn't even be able to in an atypical neural network either (which is what ehse things are anyway). The problem here really isn't the so-called "AI" (I wish we'd stop calling it AI), but the flawed usage(s) thereof. I believe I even stated earlier in this thread that sometimes it may not do what you expect, it's "probabilistic" not "deterministic" -- those pushing for greater use need to understand this, those not happy with the "push", should educate the ignorant here (especailly managers pushing for weak, insecure and bad uses).

It's one of the reasons in fact I've been working on bob so I have a very concrete and strong foundation for how these things work, how they behave and how bad or good they can be. I am on-purpose building bob to be not only a decent coding tool and general task completion tool, but with serious security boundaries, sanitation, auditing and compliance. If I'm going to succeed at building autoonmous agents that can cope with a wider array of varying inputs (mostly natural language, some structural language) then it needs to be both a) Safe and b) Robust

LIke with almost everything "big-tech" has done, it's not the tech you should not trust, but the companies themselves. For example, accessing and using the models (because let's face it, they have clusters of much larger and more powerful GPU clusters than we could ever afford to build and own ourselves, at least for now) is fine, but trusting their end-user products/services, not so much.

@movq All good, I'm tired too. Work has been burning me out lately 🥵

@movq Amen 🙏

Bought 2 tickets 🎫 Wish me luck! 🍀

@movq Hah 😅

@movq I guess I'm not so lucky haha 🤣 Only won $32 AUD 😅

@movq $95 🤣 So I'm down a fair bit 😳

@bender LOL 😂