The article starts with the comparison of DSPy and LangChain monthly downloads and then wastes time comparing DSPy to hand-rolling basic infra, which is quite trivial in every barely mature setup.

I conjecture that the core value proposition of DSPy is its optimizer? Yet the article doesn't really touch it in any important way. How does it work? How would I integrate it into my production? Is it even worth it for usual use-cases? Adding a retry is not a problem, creating and maintaining an AI control plane is. LangChain provides services for observability, online and offline evaluation, prompt engineering, deployment, you name it.

You can see many people saying this in the comments :). I personally think this misses the core of what Dspy "is".

Dspy encourages you to write your code in a way that better enables optimization, yes (and provides direct abstractions for that). But this isn't in a sense unique to Dspy: you can get these same benefits by applying the right patterns.

And they are the patterns I just find people constantly implementing these without realizing it, and think they could benefit from understanding Dspy a bit better to make better implementations :)