I'm not very happy with the code quality and I think agents bloat abstractions, have poor code aesthetics, are very prone to copy pasting code blocks and it's a mess, but at this point I stopped fighting it too hard and just moved on. The agents do not listen to my instructions in the AGENTS.md files. E.g. just as one example, no matter how many times I say something like:
"Every line of code should do exactly one thing and use intermediate variables as a form of documentation"
They will still "multitask" and create complex constructs where one line of code calls 2 functions and then indexes an array with the result. I think in principle I could use hooks or slash commands to clean this up but at some point just a shrug is easier.
Yes I think LLM as a judge for soft rewards is in principle and long term slightly problematic (due to goodharting concerns), but in practice and for now I don't think we've picked the low hanging fruit yet here.
@rationalexpec Got it. What helps for data is installing the right skills. Maybe if it's a data issue then connecting to FRED as @ArthurMotaSP said might help. If you have a Bloomberg terminal you can use blpapi. I'd look into econometrics skills in the web, you might find something useful.
@rationalexpec How have you been using it? Just give it a repo with your research pdfs, or ask it to web search the papers you want it to know about. It should handle pretty well. I'm happy to chat about it too
@JFPuget@theodormarcu Yeah sure. But after that simple vector search is still the main way of integrating into an AI app? agentic search seems more like a solution to coding base questions than to PDFs chunks retrieval