> It hasn’t thought about the problem at all. It’s pattern-matching against its training data and producing the most plausible-sounding response. The article k

> It hasn’t thought about the problem at all. It’s pattern-matching against its training data and producing the most plausible-sounding response. The article kind of lost me here. Agents are way more than that, today. And the author knows it, as later it says stuff like > Claude will never do this. It’s trained to be helpful. But the first phrase just tell me author just have a deep dislike for agents and it's looking for rationalizations for that feeling. Part of the criticism is on point, sure. But if it "being trained to be helpful" is a problem, it's fixable. It can "be trained to be more critical". Later: > But it wasn’t designed for your team. (..) It was designed for the median of everything Claude has seen. A generic best practice for a generic problem at a generic company. Which is to say, it was designed for nobody. That's non-sense. Anybody who understand algorithms know that, sure, on a first instance you have a "good algorithm" that has a good performance on average, or in worst-case. But then, you can design algorithms that are adaptive to the input. Same applies here.