chiasmus

Project source is here: https://github.com/yogthos/chiasmus

I find a lot of the inefficiency also comes from the model just randomly poking around and grepping all the time which is the fault of the harness. I ended up building a Prolog based MCP where I use tree-sitter to parse the code into a graph, and then the model can just ask questions like 'what are all the functions connected to this function'. So, in case you're trying to focus on what a particular endpoint is doing, you can trivially and predictably trace the whole subgraphs of calls. https://github.com/yogthos/chiasmus

Can't agree more. Lisp was discovered/invented for the purpose of AI research. Of course, modern neural nets and transformers is a big departure from McCarthy's vision of AI - logical, interpretable, symbolic. However, if the current wave of AI hits a wall - and many serious researchers think it will, or already has at the margins - there's growing interest in neurosymbolic approaches that combine neural nets with symbolic reasoning. That's closer to McCarthy's original vision, and Lisps are genuinely well-suited for it. Let's be honest: Lisp probably won't ever get bigger than Python, unless Python for whatever reason starts dying on its own. But if AI ever gets serious about interpretability, formal reasoning, program synthesis - all the stuff Lisp was built for - it just might quietly become relevant again in research contexts, without

repo: https://github.com/yogthos/chiasmus