control is good, and determinism is good - while the primary goal is to convince people "don't give up too much control" - there is a secondary which is: THESE are the places where it makes sense to give up some control

interesting - I think I have to side with the Boundary (YC W23) folks on this one - if you want bleeding edge performance, you need to be able to open the box and hack on the insides.

I don't agree fully with this article https://www.chrismdp.com/beyond-prompting/ but the comparison of punchards -> assembly -> c -> higher langs is quite useful here

I just don't know when we'll get the right abstraction - i don't think langchain or dspy are the "C programming language" of AI yet (they could get there!).

For now I'll stick to my "close to the metal" workbench where I can inspect tokens, reorder special tokens like system/user/JSON, and dynamically keep up with the idiosyncrasies of new models without being locked up waiting for library support.

yeah. the issue is when you're baked into a tool stack/framework where you cant go customize in that 1% of cases. A lot of tools try to get the right abstractions where you can "customize everything you would want to" but they miss the mark in some cases

i'd be interested to check it out

here's a take, I adapted this from someone on the notebookLM team on swyx's podcast

> the only way to build really impressive experiences in AI, is to find something right at the edge of the model's capability, and to get it right consistently.

So in order to build something very good / better than the rest, you will always benefit from being able to bring in every optimization you can.

*probably :)