Slightly off-topic but related: currently I'm in a research environment (biomedicine) where a lot of AI is used. Sometimes well, often poorly. So as an exercise I drafted some rules and commitments about AI and research ("Research After AI: Principles for Accelerated Exploration" [1]), I took the Agile manifesto as a starting point. Anyways, this might be interesting as a perspective on the problem space as I see it.
RDF and semantic web used to be my goto's for this, as I believe many of the core ideas are still valid and often overlooked and sometimes even poorly re-implemented. Which says something.
However, lately I've come to like llama.cpp and friends, yes it's not ChatGTP miracle whatever but how often do you /actually/ need that? Despite its tremendous popularity, it still seems like something more people should know about. For me, I've had great fun with running LLMs locally and experiencing their different "flavors" from a more "phenomenological" (what is it like to use them) perspective rather than a technological one.
[1] https://gist.github.com/joelkuiper/d52cc0e5ff06d12c85e492e42...