[gchamonlive]

> There are two obvious approaches: start with lots of guardrails, or start with very few and learn what the models actually do.

> We chose the second because we didn’t want to overfit our assumptions.

> Some of it went better than expected.

> But they also broke in very unexpected ways, sometimes spectacularly.

You clearly missed the whole point of the article, which is to experiment with agents and explore the limits of having them run wild.

Efficient use of tokens and which tasks to delegate is secondary to the experiment. Optimizing these is in any case premature if you don't understand the limits of the models.

[neya]

> which is to experiment with agents

I think you completely missed the point - they built a product purely using agents and deployed it to production for others to use. Read what the product actually does first.

[gchamonlive]

Why shouldn't they ship it to production if the experiment was a success? You say the only way to code is to "learn to appropriate the correct usage of algorithms and AI" which for you is to code a generator and only use "dumb" generators to produce code, which is fine, but they just showed that for 20 bucks and a few minutes you can get very far, so their evidence is just stronger than yours.

[neya]

> their evidence is just stronger than yours.

What evidence? There is 0 evidence. It's deployed to production, but that doesn't mean it works fine or is free of bugs - which is exactly my point and why you use algorithms for these types of things. They're testable, repeatable and scalable.

With LLM slop it's just that - slop.

[gchamonlive]

Have you seen the code to write it off as slop?