It's a neat idea. But if, as we're told, LLMs will get better and better, something like this, in theory, will be increasingly unnecessary.

I feel like most of the problems with AI using a library is how we mix code and implementation. C, C++ got it right (even if by accident) with separating specification from implementation.

Instead of lamenting the design trend of not maintaining this split, for my own code I wrote a utility to extract specifications from my existing code.

> It's a neat idea. But if, as we're told, LLMs will get better and better, something like this, in theory, will be increasingly unnecessary.

I don't think so. I think understanding the context of a project will always produce superior results. I think instead we'll just make it a lot easier to add to the training corpus the LLM pulls from.

Respectfully, I disagree. It is much faster and cheaper to direct an LLM to add a call to a battle-tested library that encapsulates complex logic than it is to design and implement that logic from scratch, even if it’s capable of that.

We’re betting on almost the exact opposite idea: we can make agentic software engineering cheaper and more reliable by making it easy for LLMs to write, find, and integrate libraries and other third party software.