Maybe? I think _understanding_ code is the ultimately goal, so that you can direct the effort of AI (and humans).

There's an assumption that writing code is the only way to truly understand it. That may be true... but maybe not. I think writing code is still part of the journey, but it might be a lot less writing and a lot more reading.

Either way, I think there's a bias of us gray hairs we need check at the door.

I feel like they need high school English teachers in the loop on the next ground of training to whip the language in shape.

It's not perfect, but I'm optimistic this will be a useful way to teach/learn in the future.

And, to be clear, I think this will be best utilized within a group/community setting. I don't think it will replace teachers or classrooms.

This assumes a) AI is a zero-sum game, and b) we're actually talking about on-prem AI will replace cloud-based AI. I think neither statements are true.

AI is like compute: we'll need all sorts of it, in various sizes, everywhere. I'm sure Nvidia whats to own all the workloads.

On the other hand, I do think open weight, like open source, will win in general.

Not saying every problem can or should be solved but AI, but mastery of the tools is kind of important when evaluating the tools. It's like complaining that vi or emacs is slow to use because of the bindings are complicated.

The second issue is: what was tooling and the prompt approach?

(To be clear, I have no problem with the premise of the write up. But without some details like this, it's sort of like saying "I had a bad board on my deck, and my tape measure wasn't able to help me remove the nails. What a bad tape measure."

"art is in the eye of the beholder."

I listen to a lot of EDM, which can be very mechanical, but I personally have strong emotional connection to. I personally would welcome AI-generated music as an alternative to human-made.

To be clear: I do agree a "human-verified" system would be great, but I don't think it would be black and white. And I would guess that eventually AI music will be better than a lot of human made music.

But, it produces solid results for a fraction of the price. Worth checking out if you have the time.

One of my goto "tests" of a new frontier models is having it rebuild a programming language from scratch. For GLM 5.2 I had it rebuild the old Rebol language in Rust:

https://github.com/mhs/rebol-clone-glm-5.2

It did a fairly good job roughing in the language for a low token cost.

However, I'm confused on the open source vs. commercial offerings. How do they differ? How do they work together?

But visiting local destinations is also such a joy. I'm a mile from one of the best BBQ joints in Michigan, in a "blink and you miss it" village. I try and make sure I don't take it for granted.

Lol, nope, I just sound that way. :-)

Great point, and I think that's my argument: above-average engineers can now produce more above average code. We don't need as many (any?) below-average developers moving forward.

I don't disagree, but I've been thinking about this a bit: a lot of _human_ written code was/is less-than-fine. And a lot of human devs didn't understand the context when they wrote it.

I'm not advocating that we fire devs, or evangelizing that LLms are awesome. But I do wish there was a slightly more honest take on the pre-LLM world: it's not just about cost reduction, it's about solving some long-term structural deficiencies of industry.

As a cheese lover, I don't care too much. :-)